Jacob Gipson, V. Tang, J. Seah, H. Kavnoudias, Adil Zia, Robin Lee, B. Mitra, W. Clements
OBJECTIVES Trauma chest radiographs may contain subtle and time-critical pathology. Artificial intelligence (AI) may aid in accurate reporting, timely identification and worklist prioritisation. However, few AI programs have been externally validated. This study aimed to evaluate the performance of a commercially available deep convolutional neural network - Annalise CXR V1.2 (Annalise.ai)- for detection of traumatic injuries on supine chest radiographs. METHODS Chest radiographs with a CT performed within 24 h in the setting of trauma were retrospectively identified at a level one adult trauma centre between January 2009 and June 2019. Annalise.ai assessment of the chest radiograph was compared to the radiologist report of the chest radiograph. Contemporaneous CT report was taken as the ground truth. Agreement with CT was measured using Cohen's κ and sensitivity/specificity for both AI and radiologists were calculated. RESULTS There were 1404 cases identified with a median age of 52 (IQR 33-69) years, 949 male. AI demonstrated superior performance compared to radiologists in identifying pneumothorax (p = 0.007) and segmental collapse (p = 0.012) on chest radiograph. Radiologists performed better than AI for clavicle fracture (p = 0.002), humerus fracture (p < 0.0015) and scapula fracture (p = 0.014). No statistical difference was found for identification of rib fractures and pneumomediastinum. CONCLUSION The evaluated AI performed comparably to radiologists in interpreting chest radiographs. Further evaluation of this AI program has the potential to enable it to be safely incorporated in clinical processes. ADVANCES IN KNOWLEDGE Clinically useful AI programs represent promising decision support tools.
{"title":"Diagnostic accuracy of a commercially available deep learning algorithm in supine chest radiographs following trauma.","authors":"Jacob Gipson, V. Tang, J. Seah, H. Kavnoudias, Adil Zia, Robin Lee, B. Mitra, W. Clements","doi":"10.1259/bjr.20210979","DOIUrl":"https://doi.org/10.1259/bjr.20210979","url":null,"abstract":"OBJECTIVES\u0000Trauma chest radiographs may contain subtle and time-critical pathology. Artificial intelligence (AI) may aid in accurate reporting, timely identification and worklist prioritisation. However, few AI programs have been externally validated. This study aimed to evaluate the performance of a commercially available deep convolutional neural network - Annalise CXR V1.2 (Annalise.ai)- for detection of traumatic injuries on supine chest radiographs.\u0000\u0000\u0000METHODS\u0000Chest radiographs with a CT performed within 24 h in the setting of trauma were retrospectively identified at a level one adult trauma centre between January 2009 and June 2019. Annalise.ai assessment of the chest radiograph was compared to the radiologist report of the chest radiograph. Contemporaneous CT report was taken as the ground truth. Agreement with CT was measured using Cohen's κ and sensitivity/specificity for both AI and radiologists were calculated.\u0000\u0000\u0000RESULTS\u0000There were 1404 cases identified with a median age of 52 (IQR 33-69) years, 949 male. AI demonstrated superior performance compared to radiologists in identifying pneumothorax (p = 0.007) and segmental collapse (p = 0.012) on chest radiograph. Radiologists performed better than AI for clavicle fracture (p = 0.002), humerus fracture (p < 0.0015) and scapula fracture (p = 0.014). No statistical difference was found for identification of rib fractures and pneumomediastinum.\u0000\u0000\u0000CONCLUSION\u0000The evaluated AI performed comparably to radiologists in interpreting chest radiographs. Further evaluation of this AI program has the potential to enable it to be safely incorporated in clinical processes.\u0000\u0000\u0000ADVANCES IN KNOWLEDGE\u0000Clinically useful AI programs represent promising decision support tools.","PeriodicalId":226783,"journal":{"name":"The British journal of radiology","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131898577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yan Zhu, Chaohui Liu, Y. Mo, Hao Dong, Chencui Huang, Ya-Ni Duan, Lei-lei Tang, Yuan-Yuan Chu, J. Qin
OBJECTIVES To explore the correlation between radiomic features and the pathology of pure ground-glass opacities (pGGOs), we established a radiomics model for predicting the pathological subtypes of minimally invasive adenocarcinoma (MIA) and precursor lesions. METHODS CT images of 1521 patients with lung adenocarcinoma or precursor lesions appearing as pGGOs on CT in our hospital from January 2015 to March 2021 were analysed retrospectively and selected based on inclusion and exclusion criteria. pGGOs were divided into an atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS) group and an MIA group. Radiomic features were extracted from the original and preprocessed images of the region of interest (ROI). ANOVA and least absolute shrinkage and selection operator (LASSO) feature selection algorithm were used for feature selection. Logistic regression algorithm was used to construct radiomics prediction model. Receiver operating characteristic (ROC) curves were used to evaluate the classification efficiency. RESULTS 129 pGGOs were included. 2107 radiomic features were extracted from each ROI. 18 radiomic features were eventually selected for model construction. The area under the curve (AUC) of the radiomics model was 0.884 (95% confidence interval (CI), 0.818-0.949) in the training set and 0.872 (95% CI, 0.756-0.988) in the test set, with a sensitivity of 72.73%, specificity of 88.24% and accuracy of 79.47%. The decision curve indicated that the model had a high net benefit rate. CONCLUSIONS The prediction model for pathological subtypes of MIA and precursor lesions in pGGOs demonstrated a high diagnostic accuracy. ADVANCES IN KNOWLEDGE We focused on lesions appearing as pGGOs on CT and revealed the differences in radiomic features between MIA and precursor lesions. We constructed a radiomics prediction model and improved the diagnostic accuracy for the pathology of MIA and precursor lesions.
{"title":"Radiomics for differentiating minimally invasive adenocarcinoma from precursor lesions in pure ground-glass opacities on chest computed tomography.","authors":"Yan Zhu, Chaohui Liu, Y. Mo, Hao Dong, Chencui Huang, Ya-Ni Duan, Lei-lei Tang, Yuan-Yuan Chu, J. Qin","doi":"10.1259/bjr.20210768","DOIUrl":"https://doi.org/10.1259/bjr.20210768","url":null,"abstract":"OBJECTIVES\u0000To explore the correlation between radiomic features and the pathology of pure ground-glass opacities (pGGOs), we established a radiomics model for predicting the pathological subtypes of minimally invasive adenocarcinoma (MIA) and precursor lesions.\u0000\u0000\u0000METHODS\u0000CT images of 1521 patients with lung adenocarcinoma or precursor lesions appearing as pGGOs on CT in our hospital from January 2015 to March 2021 were analysed retrospectively and selected based on inclusion and exclusion criteria. pGGOs were divided into an atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS) group and an MIA group. Radiomic features were extracted from the original and preprocessed images of the region of interest (ROI). ANOVA and least absolute shrinkage and selection operator (LASSO) feature selection algorithm were used for feature selection. Logistic regression algorithm was used to construct radiomics prediction model. Receiver operating characteristic (ROC) curves were used to evaluate the classification efficiency.\u0000\u0000\u0000RESULTS\u0000129 pGGOs were included. 2107 radiomic features were extracted from each ROI. 18 radiomic features were eventually selected for model construction. The area under the curve (AUC) of the radiomics model was 0.884 (95% confidence interval (CI), 0.818-0.949) in the training set and 0.872 (95% CI, 0.756-0.988) in the test set, with a sensitivity of 72.73%, specificity of 88.24% and accuracy of 79.47%. The decision curve indicated that the model had a high net benefit rate.\u0000\u0000\u0000CONCLUSIONS\u0000The prediction model for pathological subtypes of MIA and precursor lesions in pGGOs demonstrated a high diagnostic accuracy.\u0000\u0000\u0000ADVANCES IN KNOWLEDGE\u0000We focused on lesions appearing as pGGOs on CT and revealed the differences in radiomic features between MIA and precursor lesions. We constructed a radiomics prediction model and improved the diagnostic accuracy for the pathology of MIA and precursor lesions.","PeriodicalId":226783,"journal":{"name":"The British journal of radiology","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117025317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ann Christy Saju, A. Chatterjee, A. Sahu, T. Gupta, R. Krishnatry, S. Mokal, A. Sahay, S. Epari, M. Prasad, G. Chinnaswamy, J. Agarwal, J. Goda
OBJECTIVE Image based prediction of molecular subgroups of Medulloblastoma (MB) has the potential to optimize and personalize therapy. The objective of the study is to distinguish between broad molecular subgroups of MB using MR-Texture analysis. METHODS Thirty-eight MB patients treated between 2007-2020 were retrospectively analyzed. Texture analysis was performed on contrast enhanced T1(T1C) and T2 weighted(T2W) MR images. Manual segmentation was performed on all slices and radiomic features were extracted which included first order, second order (GLCM - Grey level co-occurrence matrix) and shape features. Feature enrichment was done using LASSO (Least Absolute Shrinkage and Selection Operator) regression and thereafter Support Vector Machine (SVM) and a 10-fold cross-validation strategy was used for model development. The area under Receiver Operator Characteristic (ROC) curve was used to evaluate the model. RESULTS A total of 174 and 170 images were obtained for analysis from the Axial T1C and T2W image datasets. One hundred and sixty-four MR based texture features were extracted. The best model was arrived at by using a combination of 30 GLCM and six shape features on T1C MR sequence. A 10-fold cross-validation demonstrated an AUC of 0.93, 0.9, 0.93, and 0.93 in predicting WNT, SHH, Group 3, and Group 4 MB subgroups, respectively. CONCLUSION Radiomic analysis of MR images in MB can predict molecular subgroups with acceptable degree of accuracy. The strategy needs further validation in an external dataset for its potential use in ab initio management paradigms of MBs. ADVANCES IN KNOWLEDGE Medulloblastoma can be classified into four distinct molecular subgroups using radiomic feature classifier from non-invasive Multiparametric Magnetic resonance imaging. This can have future ramifications in the extent of surgical resection of Medulloblastoma which can ultimately result in reduction of morbidity.
{"title":"Machine learning approach to predict molecular subgroups of medulloblastoma using multiparametric MRI based tumor radiomics.","authors":"Ann Christy Saju, A. Chatterjee, A. Sahu, T. Gupta, R. Krishnatry, S. Mokal, A. Sahay, S. Epari, M. Prasad, G. Chinnaswamy, J. Agarwal, J. Goda","doi":"10.1259/bjr.20211359","DOIUrl":"https://doi.org/10.1259/bjr.20211359","url":null,"abstract":"OBJECTIVE\u0000Image based prediction of molecular subgroups of Medulloblastoma (MB) has the potential to optimize and personalize therapy. The objective of the study is to distinguish between broad molecular subgroups of MB using MR-Texture analysis.\u0000\u0000\u0000METHODS\u0000Thirty-eight MB patients treated between 2007-2020 were retrospectively analyzed. Texture analysis was performed on contrast enhanced T1(T1C) and T2 weighted(T2W) MR images. Manual segmentation was performed on all slices and radiomic features were extracted which included first order, second order (GLCM - Grey level co-occurrence matrix) and shape features. Feature enrichment was done using LASSO (Least Absolute Shrinkage and Selection Operator) regression and thereafter Support Vector Machine (SVM) and a 10-fold cross-validation strategy was used for model development. The area under Receiver Operator Characteristic (ROC) curve was used to evaluate the model.\u0000\u0000\u0000RESULTS\u0000A total of 174 and 170 images were obtained for analysis from the Axial T1C and T2W image datasets. One hundred and sixty-four MR based texture features were extracted. The best model was arrived at by using a combination of 30 GLCM and six shape features on T1C MR sequence. A 10-fold cross-validation demonstrated an AUC of 0.93, 0.9, 0.93, and 0.93 in predicting WNT, SHH, Group 3, and Group 4 MB subgroups, respectively.\u0000\u0000\u0000CONCLUSION\u0000Radiomic analysis of MR images in MB can predict molecular subgroups with acceptable degree of accuracy. The strategy needs further validation in an external dataset for its potential use in ab initio management paradigms of MBs.\u0000\u0000\u0000ADVANCES IN KNOWLEDGE\u0000Medulloblastoma can be classified into four distinct molecular subgroups using radiomic feature classifier from non-invasive Multiparametric Magnetic resonance imaging. This can have future ramifications in the extent of surgical resection of Medulloblastoma which can ultimately result in reduction of morbidity.","PeriodicalId":226783,"journal":{"name":"The British journal of radiology","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122624313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zheng Li, M. Xian, Jian Guo, Chengshuo Wang, Luo Zhang, J. Xian
OBJECTIVES To investigate the diagnostic performance of quantitative and semi-quantitative parameters derived from DCE-MRI in differentiating sinonasal inverted papilloma (SIP) from SIP with coexisting malignant transformation into squamous cell carcinoma (MT-SIP). METHODS This retrospective study included 122 patients with 88 SIP and 34 MT-SIP. Quantitative and semi-quantitative parameters derived from DCE-MRI were compared between SIP and MT-SIP. The multivariate logistic regression analysis was performed to identify independent indicators and construct regression model for distinguishing MT-SIP and SIP. Diagnostic performance of independent indicators and regression model were evaluated using receiver operating coefficient (ROC) analysis and compared using DeLong test. RESULTS There were significant differences in maximum slope of increase, contrast-enhancement ratio, bolus arrival time, volume of extravascular extracellular space (Ve), and rate constant (Kep) between SIP and MT-SIP (p < 0.05). There were no significant differences in initial area under the gadolinium curve (p = 0.174) and volume transfer constant (p = 0.105) between two groups. Multivariate analysis results showed that Ve and Kep were identified as the independent indicators for differentiating MT-SIP from SIP (p < 0.001). Areas under the ROC curves (AUCs) for predicting MT-SIP were 0.779 for Ve and 0.766 for Kep. The AUC of the combination of Ve and Kep was 0.831, yielding 83% specificity and 76.5% sensitivity. CONCLUSIONS DCE-MRI can quantitatively differentiate between MT-SIP and SIP. The combination of Ve and Kep yielded an optimal performance for discriminating SIP from its malignant mimics. ADVANCES IN KNOWLEDGE DCE-MRI with quantitative and semi-quantitative parameters can provide valuable evidences for quantitatively identifying MT-SIP.
{"title":"Dynamic contrast-enhanced MRI can quantitatively identify malignant transformation of sinonasal inverted papilloma.","authors":"Zheng Li, M. Xian, Jian Guo, Chengshuo Wang, Luo Zhang, J. Xian","doi":"10.1259/bjr.20211374","DOIUrl":"https://doi.org/10.1259/bjr.20211374","url":null,"abstract":"OBJECTIVES\u0000To investigate the diagnostic performance of quantitative and semi-quantitative parameters derived from DCE-MRI in differentiating sinonasal inverted papilloma (SIP) from SIP with coexisting malignant transformation into squamous cell carcinoma (MT-SIP).\u0000\u0000\u0000METHODS\u0000This retrospective study included 122 patients with 88 SIP and 34 MT-SIP. Quantitative and semi-quantitative parameters derived from DCE-MRI were compared between SIP and MT-SIP. The multivariate logistic regression analysis was performed to identify independent indicators and construct regression model for distinguishing MT-SIP and SIP. Diagnostic performance of independent indicators and regression model were evaluated using receiver operating coefficient (ROC) analysis and compared using DeLong test.\u0000\u0000\u0000RESULTS\u0000There were significant differences in maximum slope of increase, contrast-enhancement ratio, bolus arrival time, volume of extravascular extracellular space (Ve), and rate constant (Kep) between SIP and MT-SIP (p < 0.05). There were no significant differences in initial area under the gadolinium curve (p = 0.174) and volume transfer constant (p = 0.105) between two groups. Multivariate analysis results showed that Ve and Kep were identified as the independent indicators for differentiating MT-SIP from SIP (p < 0.001). Areas under the ROC curves (AUCs) for predicting MT-SIP were 0.779 for Ve and 0.766 for Kep. The AUC of the combination of Ve and Kep was 0.831, yielding 83% specificity and 76.5% sensitivity.\u0000\u0000\u0000CONCLUSIONS\u0000DCE-MRI can quantitatively differentiate between MT-SIP and SIP. The combination of Ve and Kep yielded an optimal performance for discriminating SIP from its malignant mimics.\u0000\u0000\u0000ADVANCES IN KNOWLEDGE\u0000DCE-MRI with quantitative and semi-quantitative parameters can provide valuable evidences for quantitatively identifying MT-SIP.","PeriodicalId":226783,"journal":{"name":"The British journal of radiology","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131622556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rafat Chowdhury, Marianthi-Vasiliki Papoutsaki, Christoph Muller, Lorna Smith, Fiona Gong, Max Bullock, Harriet J. Rogers, Manju Mathew, Tom Syer, Saurabh Singh, A. Retter, L. Caselton, Jung Ryu, A. Oliver-Taylor, X. Golay, A. Bainbridge, D. Gadian, S. Punwani
OBJECTIVES To develop a phantom system which can be integrated with an automated injection system, eliminating the experimental variability that arises with manual injection; for the purposes of pulse sequence testing and metric derivation in hyperpolarised 13C-MR. METHODS The custom dynamic phantom was machined from Ultem and filled with an NADH and LDH mixture dissolved in phosphate buffered saline. Hyperpolarised [1-13C]-pyruvate was then injected into the phantom (n = 8) via an automated syringe pump and the conversion of pyruvate to lactate monitored through a 13C imaging sequence. RESULTS The phantom showed low coefficient of variation for the lactate to pyruvate peak signal heights (11.6%) and dynamic area-under curve ratios (11.0%). The variance for the LDH enzyme rate constant (kP) was also seen to be low at 15.6%. CONCLUSION The dynamic phantom demonstrates high reproducibility for quantification of 13C-hyperpolarised MR derived metrics. Establishing such a phantom is needed to facilitate development of hyperpolarsed 13C-MR pulse sequenced; and moreover, to enable multi site hyperpolarised 13C-MR clinical trials where assessment of metric variability across sites is critical. ADVANCES IN KNOWLEDGE The dynamic phantom developed during the course of this study will be a useful tool in testing new pulse sequences and standardisation in future hyperpolarised work.
{"title":"A reproducible dynamic phantom for sequence testing in hyperpolarised 13C-magnetic resonance.","authors":"Rafat Chowdhury, Marianthi-Vasiliki Papoutsaki, Christoph Muller, Lorna Smith, Fiona Gong, Max Bullock, Harriet J. Rogers, Manju Mathew, Tom Syer, Saurabh Singh, A. Retter, L. Caselton, Jung Ryu, A. Oliver-Taylor, X. Golay, A. Bainbridge, D. Gadian, S. Punwani","doi":"10.1259/bjr.20210770","DOIUrl":"https://doi.org/10.1259/bjr.20210770","url":null,"abstract":"OBJECTIVES\u0000To develop a phantom system which can be integrated with an automated injection system, eliminating the experimental variability that arises with manual injection; for the purposes of pulse sequence testing and metric derivation in hyperpolarised 13C-MR.\u0000\u0000\u0000METHODS\u0000The custom dynamic phantom was machined from Ultem and filled with an NADH and LDH mixture dissolved in phosphate buffered saline. Hyperpolarised [1-13C]-pyruvate was then injected into the phantom (n = 8) via an automated syringe pump and the conversion of pyruvate to lactate monitored through a 13C imaging sequence.\u0000\u0000\u0000RESULTS\u0000The phantom showed low coefficient of variation for the lactate to pyruvate peak signal heights (11.6%) and dynamic area-under curve ratios (11.0%). The variance for the LDH enzyme rate constant (kP) was also seen to be low at 15.6%.\u0000\u0000\u0000CONCLUSION\u0000The dynamic phantom demonstrates high reproducibility for quantification of 13C-hyperpolarised MR derived metrics. Establishing such a phantom is needed to facilitate development of hyperpolarsed 13C-MR pulse sequenced; and moreover, to enable multi site hyperpolarised 13C-MR clinical trials where assessment of metric variability across sites is critical.\u0000\u0000\u0000ADVANCES IN KNOWLEDGE\u0000The dynamic phantom developed during the course of this study will be a useful tool in testing new pulse sequences and standardisation in future hyperpolarised work.","PeriodicalId":226783,"journal":{"name":"The British journal of radiology","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127778255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. Ojha, K. Ganga, Avinash Mani, Priya Jagia, S. Gurpreet, S. Seth, T. Nakra, S. Arava, Sanjeev Kumar, Ruma Ray, Sanjiv Sharma
OBJECTIVES We aimed to evaluate the diagnostic accuracy (DA) of dual-source CT coronary angiography (DSCTCA) against Invasive coronary angiography (ICA) in assessing stenotic cardiac allograft vasculopathy (CAV) in heart transplant (HTX) recipients. METHODS Consecutive HTX recipients(n = 38) on annual surveillance, underwent DSCTCA prior to ICA on a 192-detector 384-slice DSCT scanner. Cases were classified as no CAV(no stenosis), any CAV(any degree of stenosis) or significant CAV(>50% stenosis). RESULTS Mean age was 33.66 ± 11.45 years (M:F = 27:11, median time from HTX-23.5 months). Prevalence of any CAV on DSCTCA and ICA was 44.7%(n = 17) and 39.5%(n = 15), respectively and that of significant CAV was 21.1%(n = 8) and 15.8%(n = 6), respectively. 557 (96.7%) segments were interpretable on DSCTCA. Mean radiation dose was 4.24 ± 2.15 mSv. At patient-level, the sensitivity, specificity, positive predictive value, negative predictive value(NPV), and DA of DSCTCA for detection of any CAV and significant CAV were 100%, 91.3%, 88.2%, 100%, 94.73 and 100%, 94%, 75%, 100%, 95% respectively. The same on segment-based analysis were 96%, 97.6%, 80%, 99.6%, 97.5 and 100%, 99.6%,86.7%,100%, 99.6%, respectively. There was excellent agreement between the two modalities for detection of any CAV and significant CAV [κ = 0.892 and 0.826(patient-level), 0.859 and 0.927(segment-level)]. CAC score correlated significantly with the presence of any CAV on both modalities. A diagnosis of rejection on biopsy did not correlate with any/significant CAV on DSCTCA or ICA. CONCLUSION High sensitivity and NPV of DSCTCA in the evaluation of stenotic CAV suggests that it can be an accurate and noninvasive alternative to ICA for routine surveillance of HTX recipients. ADVANCES IN KNOWLEDGE Dual source coronary CT angiography detects the stenotic cardiac allograft vasculopathy (CAV) non-invasively in transplant recipients with high sensitivity, specificity and negative predictive value when compared with catheter coronary angiography, at lower radiation doses. There is excellent agreement between CT angiography and catheter coronary angiography for detection of any CAV and significant CAV. A diagnosis of rejection on biopsy does not correlate with any/significant CAV on CT angiography or catheter angiography.
{"title":"Detection of cardiac allograft vasculopathy on dual source computed tomography in heart transplant recipients: comparison with invasive coronary angiography.","authors":"V. Ojha, K. Ganga, Avinash Mani, Priya Jagia, S. Gurpreet, S. Seth, T. Nakra, S. Arava, Sanjeev Kumar, Ruma Ray, Sanjiv Sharma","doi":"10.1259/bjr.20211237","DOIUrl":"https://doi.org/10.1259/bjr.20211237","url":null,"abstract":"OBJECTIVES\u0000We aimed to evaluate the diagnostic accuracy (DA) of dual-source CT coronary angiography (DSCTCA) against Invasive coronary angiography (ICA) in assessing stenotic cardiac allograft vasculopathy (CAV) in heart transplant (HTX) recipients.\u0000\u0000\u0000METHODS\u0000Consecutive HTX recipients(n = 38) on annual surveillance, underwent DSCTCA prior to ICA on a 192-detector 384-slice DSCT scanner. Cases were classified as no CAV(no stenosis), any CAV(any degree of stenosis) or significant CAV(>50% stenosis).\u0000\u0000\u0000RESULTS\u0000Mean age was 33.66 ± 11.45 years (M:F = 27:11, median time from HTX-23.5 months). Prevalence of any CAV on DSCTCA and ICA was 44.7%(n = 17) and 39.5%(n = 15), respectively and that of significant CAV was 21.1%(n = 8) and 15.8%(n = 6), respectively. 557 (96.7%) segments were interpretable on DSCTCA. Mean radiation dose was 4.24 ± 2.15 mSv. At patient-level, the sensitivity, specificity, positive predictive value, negative predictive value(NPV), and DA of DSCTCA for detection of any CAV and significant CAV were 100%, 91.3%, 88.2%, 100%, 94.73 and 100%, 94%, 75%, 100%, 95% respectively. The same on segment-based analysis were 96%, 97.6%, 80%, 99.6%, 97.5 and 100%, 99.6%,86.7%,100%, 99.6%, respectively. There was excellent agreement between the two modalities for detection of any CAV and significant CAV [κ = 0.892 and 0.826(patient-level), 0.859 and 0.927(segment-level)]. CAC score correlated significantly with the presence of any CAV on both modalities. A diagnosis of rejection on biopsy did not correlate with any/significant CAV on DSCTCA or ICA.\u0000\u0000\u0000CONCLUSION\u0000High sensitivity and NPV of DSCTCA in the evaluation of stenotic CAV suggests that it can be an accurate and noninvasive alternative to ICA for routine surveillance of HTX recipients.\u0000\u0000\u0000ADVANCES IN KNOWLEDGE\u0000Dual source coronary CT angiography detects the stenotic cardiac allograft vasculopathy (CAV) non-invasively in transplant recipients with high sensitivity, specificity and negative predictive value when compared with catheter coronary angiography, at lower radiation doses. There is excellent agreement between CT angiography and catheter coronary angiography for detection of any CAV and significant CAV. A diagnosis of rejection on biopsy does not correlate with any/significant CAV on CT angiography or catheter angiography.","PeriodicalId":226783,"journal":{"name":"The British journal of radiology","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120944220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Zanoni, D. Calabrò, E. Fortunati, G. Argalia, C. Malizia, V. Allegri, S. Civollani, S. Fanti, V. Ambrosini
OBJECTIVES To assess how patients' dependent parameters may affect [68Ga]Ga-DOTANOC image quality and to propose a theoretical body mass index (BMI)-adjusted injected activity (IA) scheme, to improve imaging of high weight patients. METHODS Among patients prospectively enrolled (June-2019 and May-2020) in an Institutional Ethical Committee-approved electronic archive, we included those affected by primary gastro-entero-pancreatic (GEP) or lung neuroendocrine tumour and referred by our Institutional clinicians (excluding even minimal radiopharmaceutical extravasation, movement artifacts, renal insufficiency). All PET/CT images were acquired following EANM guidelines and rated for visual quality (1 = non-diagnostic, 2 = poor, 3 = moderate, 4 = good). Collected data included patient's body mass, height, BMI, age, IA (injected activity), IA per Kg (IAkg), IA per BMI (IABMI), liver SUVmean, liver SUVmax standard deviation, liver-signal-to-noise (LSNR), normalized_LSNR (LSNR_norm) and contrast-to-noise ratio (CNR) for positive scans and were compared to image rating (poor vs moderate/good). RESULTS Overall, 77 patients were included. Rating concordance was high (agreement = 81.8%, Fleiss k score = 0.806). All patients' dependent parameters resulted significantly different between poor-rated and moderate/good rated scans (IA: p = 0.006, IAkg: p =< 0.001, body weight: p =< 0.001, BMI: p =< 0.001, IABMI: p =< 0.001). Factors significantly associated with moderate/good rating were BMI (p =< 0.001), body weight (p =< 0.001), IABMI (p =< 0.001), IAkg (p = 0.001), IA (p = 0.003), LSNR_norm (p = 0.01). The BMI-based model presented the best predictive efficiency (81.82%). IABMI performance to differentiate moderate/good from poor rating resulted statistically significant (IA-AUC = 0.78; 95% CI: 0.68-0.89; cut-off value of 4.17MBq*m2/kg, sensitivity = 81.1%, specificity = 66.7%). If BMI-adjusted IA (=4.17*BMI) would have been applied in this population, the median IA would have slightly inferior (-4.8%), despite a different IA in each patient. ADVANCES IN KNOWLEDGE BMI resulted the best predictor of image quality. The proposed theoretical BMI-adjusted IA scheme (4.17*BMI) should yeld images of better quality (especially in high-BMI patients) mantaining practical scanning times (3 min/bed).
{"title":"Two birds with one stone: can [68Ga]Ga-DOTANOC PET/CT image quality be improved through BMI-adjusted injected activity without increasing acquisition times?","authors":"L. Zanoni, D. Calabrò, E. Fortunati, G. Argalia, C. Malizia, V. Allegri, S. Civollani, S. Fanti, V. Ambrosini","doi":"10.1259/bjr.20211152","DOIUrl":"https://doi.org/10.1259/bjr.20211152","url":null,"abstract":"OBJECTIVES\u0000To assess how patients' dependent parameters may affect [68Ga]Ga-DOTANOC image quality and to propose a theoretical body mass index (BMI)-adjusted injected activity (IA) scheme, to improve imaging of high weight patients.\u0000\u0000\u0000METHODS\u0000Among patients prospectively enrolled (June-2019 and May-2020) in an Institutional Ethical Committee-approved electronic archive, we included those affected by primary gastro-entero-pancreatic (GEP) or lung neuroendocrine tumour and referred by our Institutional clinicians (excluding even minimal radiopharmaceutical extravasation, movement artifacts, renal insufficiency). All PET/CT images were acquired following EANM guidelines and rated for visual quality (1 = non-diagnostic, 2 = poor, 3 = moderate, 4 = good). Collected data included patient's body mass, height, BMI, age, IA (injected activity), IA per Kg (IAkg), IA per BMI (IABMI), liver SUVmean, liver SUVmax standard deviation, liver-signal-to-noise (LSNR), normalized_LSNR (LSNR_norm) and contrast-to-noise ratio (CNR) for positive scans and were compared to image rating (poor vs moderate/good).\u0000\u0000\u0000RESULTS\u0000Overall, 77 patients were included. Rating concordance was high (agreement = 81.8%, Fleiss k score = 0.806). All patients' dependent parameters resulted significantly different between poor-rated and moderate/good rated scans (IA: p = 0.006, IAkg: p =< 0.001, body weight: p =< 0.001, BMI: p =< 0.001, IABMI: p =< 0.001). Factors significantly associated with moderate/good rating were BMI (p =< 0.001), body weight (p =< 0.001), IABMI (p =< 0.001), IAkg (p = 0.001), IA (p = 0.003), LSNR_norm (p = 0.01). The BMI-based model presented the best predictive efficiency (81.82%). IABMI performance to differentiate moderate/good from poor rating resulted statistically significant (IA-AUC = 0.78; 95% CI: 0.68-0.89; cut-off value of 4.17MBq*m2/kg, sensitivity = 81.1%, specificity = 66.7%). If BMI-adjusted IA (=4.17*BMI) would have been applied in this population, the median IA would have slightly inferior (-4.8%), despite a different IA in each patient.\u0000\u0000\u0000ADVANCES IN KNOWLEDGE\u0000BMI resulted the best predictor of image quality. The proposed theoretical BMI-adjusted IA scheme (4.17*BMI) should yeld images of better quality (especially in high-BMI patients) mantaining practical scanning times (3 min/bed).","PeriodicalId":226783,"journal":{"name":"The British journal of radiology","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117323755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y. Noda, N. Kawai, Tomotaka Kawamura, Akikazu Kobori, Rena Miyase, Ken Iwashima, T. Kaga, T. Miyoshi, F. Hyodo, H. Kato, M. Matsuo
OBJECTIVES To evaluate the feasibility of a simultaneous reduction of radiation and iodine doses in dual-energy thoraco-abdomino-pelvic CT reconstructed with deep learning image reconstruction (DLIR). METHODS Thoraco-abdomino-pelvic CT was prospectively performed in 111 participants; 52 participants underwent a standard-dose single-energy CT with a standard iodine dose (600 mgI/kg; SD group), while 59 underwent a low-dose dual-energy CT with a reduced iodine dose (300 mgI/kg; double low-dose [DLD] group). CT data were reconstructed with a hybrid iterative reconstruction in the SD group and a high-strength level of DLIR at 40 keV in the DLD group. Two radiologists measured the CT numbers of the descending and abdominal aorta, portal vein, hepatic vein, inferior vena cava, liver, pancreas, spleen, and kidney, and background noise. Two other radiologists assessed diagnostic acceptability using a five-point scale. The CT dose-index volumes (CTDIvol), iodine weight, CT numbers of anatomical structures, background noise, and diagnostic acceptability were compared between the two groups using Mann-Whitney U test. RESULTS The median CTDIvol (10 mGy; interquartile range [IQR], 9-13 mGy vs 4 mGy; IQR, 4-5 mGy) and median iodine weight (35 g; IQR, 31-38 g vs 16 g; IQR, 14-18 g) were lower in the DLD group than in the SD group (p < 0.001 for each). The CT numbers of all anatomical structures and background noise were higher in the DLD group than in the SD group (p < 0.001 for all). The diagnostic image quality was obtained in 100% (52/52) of participants in the SD group and 95% (56/59) of participants in the DLD group. CONCLUSION Monoenergetic images at 40 keV with DLIR could achieve half doses of radiation and iodine while maintaining diagnostic image quality. ADVANCES IN KNOWLEDGE Virtual monochromatic images at 40 keV reconstructed with deep learning image reconstruction algorithm allowed to reduce the doses of radiation and iodine while maintaining diagnostic image quality.
目的探讨深度学习图像重建(DLIR)双能胸腹盆腔CT同时降低辐射和碘剂量的可行性。方法对111例受试者进行胸腹盆腔CT前瞻性检查;52名参与者接受了标准剂量的单能CT，碘的标准剂量为600毫克/公斤;SD组)，59例接受低剂量双能CT，碘剂量降低(300 mgI/kg;双低剂量[DLD]组)。SD组采用混合迭代重建，DLD组采用40kev高强度DLIR重建。两名放射科医师测量降腹主动脉、门静脉、肝静脉、下腔静脉、肝、胰、脾、肾的CT值及背景噪声。另外两名放射科医生使用五分制评估诊断的可接受性。采用Mann-Whitney U检验比较两组患者CT剂量指数体积(CTDIvol)、碘重、解剖结构CT数、背景噪声及诊断可接受性。结果中位CTDIvol (10 mGy);四分位间距[IQR]， 9-13 mGy vs 4 mGy;IQR, 4-5毫戈瑞)和碘的中位重量(35克;IQR, 31-38 g vs 16 g;DLD组IQR, 14-18 g)低于SD组(p < 0.001)。DLD组各解剖结构CT数及背景噪声均高于SD组(p < 0.001)。SD组和DLD组的诊断图像质量分别为100%(52/52)和95%(56/59)。结论40kev单能量DLIR成像在保持诊断图像质量的前提下，可达到一半剂量的辐射和碘。使用深度学习图像重建算法重建40 keV的虚拟单色图像，可以在保持诊断图像质量的同时减少辐射和碘的剂量。
{"title":"Radiation and iodine dose reduced thoraco-abdomino-pelvic dual-energy CT at 40 keV reconstructed with deep learning image reconstruction.","authors":"Y. Noda, N. Kawai, Tomotaka Kawamura, Akikazu Kobori, Rena Miyase, Ken Iwashima, T. Kaga, T. Miyoshi, F. Hyodo, H. Kato, M. Matsuo","doi":"10.1259/bjr.20211163","DOIUrl":"https://doi.org/10.1259/bjr.20211163","url":null,"abstract":"OBJECTIVES\u0000To evaluate the feasibility of a simultaneous reduction of radiation and iodine doses in dual-energy thoraco-abdomino-pelvic CT reconstructed with deep learning image reconstruction (DLIR).\u0000\u0000\u0000METHODS\u0000Thoraco-abdomino-pelvic CT was prospectively performed in 111 participants; 52 participants underwent a standard-dose single-energy CT with a standard iodine dose (600 mgI/kg; SD group), while 59 underwent a low-dose dual-energy CT with a reduced iodine dose (300 mgI/kg; double low-dose [DLD] group). CT data were reconstructed with a hybrid iterative reconstruction in the SD group and a high-strength level of DLIR at 40 keV in the DLD group. Two radiologists measured the CT numbers of the descending and abdominal aorta, portal vein, hepatic vein, inferior vena cava, liver, pancreas, spleen, and kidney, and background noise. Two other radiologists assessed diagnostic acceptability using a five-point scale. The CT dose-index volumes (CTDIvol), iodine weight, CT numbers of anatomical structures, background noise, and diagnostic acceptability were compared between the two groups using Mann-Whitney U test.\u0000\u0000\u0000RESULTS\u0000The median CTDIvol (10 mGy; interquartile range [IQR], 9-13 mGy vs 4 mGy; IQR, 4-5 mGy) and median iodine weight (35 g; IQR, 31-38 g vs 16 g; IQR, 14-18 g) were lower in the DLD group than in the SD group (p < 0.001 for each). The CT numbers of all anatomical structures and background noise were higher in the DLD group than in the SD group (p < 0.001 for all). The diagnostic image quality was obtained in 100% (52/52) of participants in the SD group and 95% (56/59) of participants in the DLD group.\u0000\u0000\u0000CONCLUSION\u0000Monoenergetic images at 40 keV with DLIR could achieve half doses of radiation and iodine while maintaining diagnostic image quality.\u0000\u0000\u0000ADVANCES IN KNOWLEDGE\u0000Virtual monochromatic images at 40 keV reconstructed with deep learning image reconstruction algorithm allowed to reduce the doses of radiation and iodine while maintaining diagnostic image quality.","PeriodicalId":226783,"journal":{"name":"The British journal of radiology","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121018788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Burnet, T. Mee, S. Gaito, N. Kirkby, Adam Henry Aitkenhead, C. Anandadas, M. Aznar, L. Barraclough, G. Borst, Francis C Charlwood, M. Clarke, R. Colaco, A. Crellin, N. Defourney, C. Hague, M. Harris, N. Henthorn, K. Hopkins, E. Hwang, S. Ingram, K. Kirkby, L. Lee, D. Lines, Z. Lingard, M. Lowe, R. Mackay, C. Mcbain, M. Merchant, D. Noble, S. Pan, J. Price, G. Radhakrishna, David Reboredo-Gil, A. Salem, Srijith Sashidharan, P. Sitch, E. Smith, E. Smith, M. Taylor, D. Thomson, N. Thorp, T. Underwood, J. Warmenhoven, J. Wylie, G. Whitfield
OBJECTIVES High energy Proton Beam Therapy (PBT) commenced in England in 2018 and NHS England commissions PBT for 1.5% of patients receiving radical radiotherapy. We sought expert opinion on the level of provision. METHODS Invitations were sent to 41 colleagues working in PBT, most at one UK centre, to contribute by completing a spreadsheet. 39 responded: 23 (59%) completed the spreadsheet; 16 (41%) declined, arguing that clinical outcome data are lacking, but joined six additional site-specialist oncologists for two consensus meetings. The spreadsheet was pre-populated with incidence data from Cancer Research UK and radiotherapy use data from the National Cancer Registration and Analysis Service. 'Mechanisms of Benefit' of reduced growth impairment, reduced toxicity, dose escalation and reduced second cancer risk were examined. RESULTS The most reliable figure for percentage of radical radiotherapy patients likely to benefit from PBT was that agreed by 95% of the 23 respondents at 4.3%, slightly larger than current provision. The median was 15% (range 4-92%); consensus median 13%. The biggest estimated potential benefit was from reducing toxicity, median benefit to 15% (range 4-92%), followed by dose escalation median 3% (range 0 to 47%); consensus values were 12 and 3%. Reduced growth impairment and reduced second cancer risk were calculated to benefit 0.5 and 0.1%. CONCLUSIONS The most secure estimate of percentage benefit was 4.3% but insufficient clinical outcome data exist for confident estimates. The study supports the NHS approach of using the evidence base, and developing it through randomised trials, non-randomised studies and outcomes tracking. ADVANCES IN KNOWLEDGE Less is known about the percentage of patients who may benefit from PBT than is generally acknowledged. Expert opinion varies widely. Insufficient clinical outcome data exists to provide robust estimates. Considerable further work is needed to address this, including international collaboration; much is already underway but will take time to provide mature data.
{"title":"Estimating the percentage of patients who might benefit from proton beam therapy instead of X-ray radiotherapy.","authors":"N. Burnet, T. Mee, S. Gaito, N. Kirkby, Adam Henry Aitkenhead, C. Anandadas, M. Aznar, L. Barraclough, G. Borst, Francis C Charlwood, M. Clarke, R. Colaco, A. Crellin, N. Defourney, C. Hague, M. Harris, N. Henthorn, K. Hopkins, E. Hwang, S. Ingram, K. Kirkby, L. Lee, D. Lines, Z. Lingard, M. Lowe, R. Mackay, C. Mcbain, M. Merchant, D. Noble, S. Pan, J. Price, G. Radhakrishna, David Reboredo-Gil, A. Salem, Srijith Sashidharan, P. Sitch, E. Smith, E. Smith, M. Taylor, D. Thomson, N. Thorp, T. Underwood, J. Warmenhoven, J. Wylie, G. Whitfield","doi":"10.1259/bjr.20211175","DOIUrl":"https://doi.org/10.1259/bjr.20211175","url":null,"abstract":"OBJECTIVES\u0000High energy Proton Beam Therapy (PBT) commenced in England in 2018 and NHS England commissions PBT for 1.5% of patients receiving radical radiotherapy. We sought expert opinion on the level of provision.\u0000\u0000\u0000METHODS\u0000Invitations were sent to 41 colleagues working in PBT, most at one UK centre, to contribute by completing a spreadsheet. 39 responded: 23 (59%) completed the spreadsheet; 16 (41%) declined, arguing that clinical outcome data are lacking, but joined six additional site-specialist oncologists for two consensus meetings. The spreadsheet was pre-populated with incidence data from Cancer Research UK and radiotherapy use data from the National Cancer Registration and Analysis Service. 'Mechanisms of Benefit' of reduced growth impairment, reduced toxicity, dose escalation and reduced second cancer risk were examined.\u0000\u0000\u0000RESULTS\u0000The most reliable figure for percentage of radical radiotherapy patients likely to benefit from PBT was that agreed by 95% of the 23 respondents at 4.3%, slightly larger than current provision. The median was 15% (range 4-92%); consensus median 13%. The biggest estimated potential benefit was from reducing toxicity, median benefit to 15% (range 4-92%), followed by dose escalation median 3% (range 0 to 47%); consensus values were 12 and 3%. Reduced growth impairment and reduced second cancer risk were calculated to benefit 0.5 and 0.1%.\u0000\u0000\u0000CONCLUSIONS\u0000The most secure estimate of percentage benefit was 4.3% but insufficient clinical outcome data exist for confident estimates. The study supports the NHS approach of using the evidence base, and developing it through randomised trials, non-randomised studies and outcomes tracking.\u0000\u0000\u0000ADVANCES IN KNOWLEDGE\u0000Less is known about the percentage of patients who may benefit from PBT than is generally acknowledged. Expert opinion varies widely. Insufficient clinical outcome data exists to provide robust estimates. Considerable further work is needed to address this, including international collaboration; much is already underway but will take time to provide mature data.","PeriodicalId":226783,"journal":{"name":"The British journal of radiology","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132274879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qingqiang Zhu, Wen-rong Zhu, Jingtao Wu, Wen-xin Chen, Jing Ye, J. Ling
OBJECTIVE To quantitatively compare the diagnostic values of conventional diffusion-weighted imaging (DWI) and introvoxel incoherent motion (IVIM) analysis of microstructural differences for clear cell renal cell carcinoma (ccRCC). METHODS Multiple b value DWIs and IVIMs were performed in patients with 146 ccRCCs, 42 with Grade Ⅰ, 46 with Grade Ⅱ, 28 with Grade Ⅲ and 30 with Grade Ⅳ. These tumours were divided into low (Ⅰ+Ⅱ, n = 88) and high grades (Ⅲ+Ⅳ, n = 58). The diagnostic efficacy of various diffusion parameters for predicting ccRCC grades was compared. RESULTS The mean signal-to-noise ratios (SNRs) of IVIM images at b = 0, 800 and 1500 s/mm2 were 31.9, 12.3 and 8.4, respectively. The apparent diffusion coefficient (ADC), D and D* values correlated negatively with ccRCC grading (r = -0.786,-0.913, -0879, p < 0.05). f values correlated positively with ccRCC grading (r = 0.811, p < 0.05). The ADC, D and D* values were higher for Grade Ⅱ ccRCC than that of Grade Ⅲ ccRCC (p < 005), however, f values were higher for Grade Ⅲ ccRCC than that of Grade Ⅱ ccRCC (p < 005). Receiver operating characteristic curve analyses showed that D values had the highest diagnostic efficacy in differentiating low/high and Ⅱ/Ⅲ ccRCC grading. The area under the curve, sensitivity, specificity and accuracy of the D values were 0.963, 0.960; 90.9%, 89.1%; 81.0%,78.6 and 89.0%, 87.8%, respectively. For pairwise comparisons of receiver operating characteristic curves and diagnostic efficacy, ADC was worse than IVIM (all p < 0.05). CONCLUSION IVIM parameters have better performance than ADC in differentiating ccRCC grading, given an adequate SNR of IVIM images. ADVANCES IN KNOWLEDGE 1. D values had the highest diagnostic efficacy in differentiating low/high and Ⅱ/Ⅲ ccRCC grading. 2. IVIM parameters have better performance than ADC in differentiating ccRCC grading, given an adequate SNR of IVIM images. 3. The ADC, D and D* values correlated negatively with ccRCC grading, however, f values correlated positively with ccRCC grading.
{"title":"Comparative study of conventional diffusion-weighted imaging and introvoxel incoherent motion in assessment of pathological grade of clear cell renal cell carcinoma.","authors":"Qingqiang Zhu, Wen-rong Zhu, Jingtao Wu, Wen-xin Chen, Jing Ye, J. Ling","doi":"10.1259/bjr.20210485","DOIUrl":"https://doi.org/10.1259/bjr.20210485","url":null,"abstract":"OBJECTIVE\u0000To quantitatively compare the diagnostic values of conventional diffusion-weighted imaging (DWI) and introvoxel incoherent motion (IVIM) analysis of microstructural differences for clear cell renal cell carcinoma (ccRCC).\u0000\u0000\u0000METHODS\u0000Multiple b value DWIs and IVIMs were performed in patients with 146 ccRCCs, 42 with Grade Ⅰ, 46 with Grade Ⅱ, 28 with Grade Ⅲ and 30 with Grade Ⅳ. These tumours were divided into low (Ⅰ+Ⅱ, n = 88) and high grades (Ⅲ+Ⅳ, n = 58). The diagnostic efficacy of various diffusion parameters for predicting ccRCC grades was compared.\u0000\u0000\u0000RESULTS\u0000The mean signal-to-noise ratios (SNRs) of IVIM images at b = 0, 800 and 1500 s/mm2 were 31.9, 12.3 and 8.4, respectively. The apparent diffusion coefficient (ADC), D and D* values correlated negatively with ccRCC grading (r = -0.786,-0.913, -0879, p < 0.05). f values correlated positively with ccRCC grading (r = 0.811, p < 0.05). The ADC, D and D* values were higher for Grade Ⅱ ccRCC than that of Grade Ⅲ ccRCC (p < 005), however, f values were higher for Grade Ⅲ ccRCC than that of Grade Ⅱ ccRCC (p < 005). Receiver operating characteristic curve analyses showed that D values had the highest diagnostic efficacy in differentiating low/high and Ⅱ/Ⅲ ccRCC grading. The area under the curve, sensitivity, specificity and accuracy of the D values were 0.963, 0.960; 90.9%, 89.1%; 81.0%,78.6 and 89.0%, 87.8%, respectively. For pairwise comparisons of receiver operating characteristic curves and diagnostic efficacy, ADC was worse than IVIM (all p < 0.05).\u0000\u0000\u0000CONCLUSION\u0000IVIM parameters have better performance than ADC in differentiating ccRCC grading, given an adequate SNR of IVIM images.\u0000\u0000\u0000ADVANCES IN KNOWLEDGE\u00001. D values had the highest diagnostic efficacy in differentiating low/high and Ⅱ/Ⅲ ccRCC grading. 2. IVIM parameters have better performance than ADC in differentiating ccRCC grading, given an adequate SNR of IVIM images. 3. The ADC, D and D* values correlated negatively with ccRCC grading, however, f values correlated positively with ccRCC grading.","PeriodicalId":226783,"journal":{"name":"The British journal of radiology","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129200999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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