Pub Date : 2024-09-14DOI: 10.1016/j.acra.2024.08.058
Yidan Lou, Xiaoling Zhang, Pengfei Sun, Xu Chang
Rationale and objectives: Hepatocellular carcinoma (HCC) with invasion into the inferior vena cava (IVC) or the right atrium (RA) presents significant therapeutic challenges due to its rapid progression and limited available treatments.
Materials and methods: This retrospective study evaluated the effectiveness of hepatic arterial infusion chemotherapy alongside lenvatinib and PD-1 inhibitors (HAIC-Len-PD1) compared to treatment with only lenvatinib and PD-1 inhibitors (Len-PD1). A total of 115 patients with HCC and IVC or RA invasion were included. We analyzed groups for median overall survival (OS) and progression-free survival (PFS) through the Kaplan-Meier method, along with tumor response rates, disease control rates, and adverse event frequencies.
Results: The HAIC-Len-PD1 treatment showed a marked improvement in median OS (22.2 vs. 14.4 months; P = 0.007) and median PFS (13.8 vs. 5.1 months; P = 0.001) over the Len-PD1 regimen. There was also a higher overall response rate (68.7% vs. 37.5%; P < 0.05) and disease control rate (92.5% vs. 75%; P < 0.05) observed in the HAIC-Len-PD1 group. A subgroup analysis demonstrated consistent survival benefits across diverse patient demographics. Although the incidence of adverse events was higher in the HAIC-Len-PD1 group, these were generally manageable and well-tolerated.
Conclusion: The combined regimen of HAIC, lenvatinib, and PD-1 inhibitors may improve survival and tumor management in HCC patients with IVC or RA invasion, suggesting a potential therapeutic option for this critically at-risk group. Further research in the form of randomized controlled trials are needed to verify these findings for advanced-stage HCC with vascular compromise.
理由和目标：侵犯下腔静脉（IVC）或右心房（RA）的肝细胞癌（HCC）因其进展迅速和可用治疗方法有限而给治疗带来了巨大挑战：这项回顾性研究评估了肝动脉灌注化疗联合来伐替尼和PD-1抑制剂（HAIC-Len-PD1）与仅使用来伐替尼和PD-1抑制剂（Len-PD1）治疗的有效性。共纳入了115例HCC合并IVC或RA侵犯的患者。我们采用Kaplan-Meier法分析了各组的中位总生存期（OS）和无进展生存期（PFS），以及肿瘤反应率、疾病控制率和不良事件发生率：与Len-PD1治疗方案相比，HAIC-Len-PD1治疗方案的中位OS（22.2个月 vs. 14.4个月；P = 0.007）和中位PFS（13.8个月 vs. 5.1个月；P = 0.001）明显改善。此外，HAIC-Len-PD1治疗组的总体反应率（68.7%对37.5%；P＜0.05）和疾病控制率（92.5%对75%；P＜0.05）也更高。一项亚组分析显示，不同患者人口统计学特征的生存获益一致。虽然HAIC-Len-PD1组的不良反应发生率较高，但这些不良反应总体上可控且耐受性良好：结论：HAIC、lenvatinib和PD-1抑制剂的联合治疗方案可改善有IVC或RA侵犯的HCC患者的生存和肿瘤管理，为这一高风险人群提供了潜在的治疗选择。对于有血管损伤的晚期HCC患者，还需要通过随机对照试验的形式进行进一步研究，以验证这些发现。
{"title":"Hepatic arterial infusion chemotherapy enhances the efficacy of lenvatinib plus PD-1 inhibitors in hepatocellular carcinoma patients with tumor thrombosis in the inferior vena cava and/or right atrium.","authors":"Yidan Lou, Xiaoling Zhang, Pengfei Sun, Xu Chang","doi":"10.1016/j.acra.2024.08.058","DOIUrl":"https://doi.org/10.1016/j.acra.2024.08.058","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Hepatocellular carcinoma (HCC) with invasion into the inferior vena cava (IVC) or the right atrium (RA) presents significant therapeutic challenges due to its rapid progression and limited available treatments.</p><p><strong>Materials and methods: </strong>This retrospective study evaluated the effectiveness of hepatic arterial infusion chemotherapy alongside lenvatinib and PD-1 inhibitors (HAIC-Len-PD1) compared to treatment with only lenvatinib and PD-1 inhibitors (Len-PD1). A total of 115 patients with HCC and IVC or RA invasion were included. We analyzed groups for median overall survival (OS) and progression-free survival (PFS) through the Kaplan-Meier method, along with tumor response rates, disease control rates, and adverse event frequencies.</p><p><strong>Results: </strong>The HAIC-Len-PD1 treatment showed a marked improvement in median OS (22.2 vs. 14.4 months; P = 0.007) and median PFS (13.8 vs. 5.1 months; P = 0.001) over the Len-PD1 regimen. There was also a higher overall response rate (68.7% vs. 37.5%; P < 0.05) and disease control rate (92.5% vs. 75%; P < 0.05) observed in the HAIC-Len-PD1 group. A subgroup analysis demonstrated consistent survival benefits across diverse patient demographics. Although the incidence of adverse events was higher in the HAIC-Len-PD1 group, these were generally manageable and well-tolerated.</p><p><strong>Conclusion: </strong>The combined regimen of HAIC, lenvatinib, and PD-1 inhibitors may improve survival and tumor management in HCC patients with IVC or RA invasion, suggesting a potential therapeutic option for this critically at-risk group. Further research in the form of randomized controlled trials are needed to verify these findings for advanced-stage HCC with vascular compromise.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13DOI: 10.1016/j.acra.2024.08.048
Ahmad A Toubasi, Gary Cutter, Caroline Gheen, Taegan Vinarsky, Keejin Yoon, Salma AshShareef, Pragnya Adapa, Olivia Gruder, Stephanie Taylor, James E Eaton, Junzhong Xu, Francesca Bagnato
Rationale and objectives: Several quantitative magnetic resonance imaging (MRI) methods are available to measure tissue injury in multiple sclerosis (MS), but their pathological specificity remains limited. The multi-compartment diffusion imaging using the spherical mean technique (SMT) overcomes several technical limitations of the diffusion-weighted image signal, thus delivering metrics with increased pathological specificity. Given these premises, here we assess whether the SMT-derived apparent axonal volume (Vax) provides a better tissue classifier than the diffusion tensor imaging (DTI)-derived axial diffusivity (AD) in the white matter (WM) of MS brains.
Methods: Forty-three treatment-naïve people with newly diagnosed MS, clinically isolated syndrome, or radiologically isolated syndrome and 18 healthy controls (HCs) underwent a 3.0 Tesla MRI inclusive of T1-weighted (T1-w) and T2-w fluid-attenuated inversion recovery (FLAIR) sequences, and multi-b shell diffusion-weighted imaging. In patients only, pre- and post-gadolinium diethylenetriamine penta-acetic acid T1-w sequences were obtained for the evaluation of contrast-active lesions (CELs). Vax and AD were calculated in T2-lesions, chronic black holes (cBHs), and normal appearing (NAWM) in patients and normal WM (NWM) in HCs. Vax and AD values were compared across all the possible combinations of these regions. CELs were excluded from the analyses.
Results: Vax differed in all comparisons (p ≤ 0.047 by paired t-test); AD differed in most comparisons (p < 0.001) except between NAWM and NWM, and between cBHs and T2-lesions. Vax had higher accuracy (p ≤ 0.029 by DeLong test) and larger effect size (p ≤ 0.038 by paired t-test) than AD in differentiating areas with even minimal tissue injury.
Conclusions: Vax provides a better radiological quantitative discriminator of different degrees of axonal-mediated tissue injury even between areas with expected minimal pathology. Our data support further studies to assess the readiness of Vax as a measure of outcome for clinical trials on neuroprotection in MS.
理由和目标：目前有几种定量磁共振成像（MRI）方法可用于测量多发性硬化症（MS）的组织损伤，但其病理特异性仍然有限。使用球面均值技术（SMT）进行的多室弥散成像克服了弥散加权图像信号的一些技术局限性，从而提供了病理特异性更强的指标。鉴于这些前提，我们在此评估在多发性硬化症大脑白质（WM）中，SMT得出的表观轴突体积（Vax）是否比扩散张量成像（DTI）得出的轴向扩散率（AD）提供了更好的组织分类器：43名未经治疗的新诊断多发性硬化症、临床孤立综合征或放射学孤立综合征患者和18名健康对照组（HCs）接受了3.0特斯拉核磁共振成像（MRI）检查，包括T1加权（T1-w）和T2-w流体衰减反转恢复（FLAIR）序列以及多B壳扩散加权成像。仅对患者进行了二乙烯三胺五乙酸钆前和后 T1-w 序列检查，以评估造影剂活性病变（CEL）。计算了患者 T2- 病变、慢性黑洞（cBHs）和正常显示（NAWM）以及 HCs 正常 WM（NWM）的 Vax 和 AD。在这些区域的所有可能组合中比较了 Vax 和 AD 值。分析中不包括CEL：在所有比较中，Vax 均有差异（通过配对 t 检验，p ≤ 0.047）；在大多数比较中，AD 均有差异（p 2-裂隙）。在区分组织损伤最小的区域方面，Vax 比 AD 具有更高的准确性（通过 DeLong 检验，p ≤ 0.029）和更大的效应大小（通过配对 t 检验，p ≤ 0.038）：结论：Vax 是轴突介导的不同组织损伤程度的更好的放射学定量判别指标，即使在预期病理程度极轻的区域之间也是如此。我们的数据支持进一步的研究，以评估 Vax 是否可作为多发性硬化症神经保护临床试验的结果测量指标。
{"title":"Improving the assessment of axonal injury in early multiple sclerosis.","authors":"Ahmad A Toubasi, Gary Cutter, Caroline Gheen, Taegan Vinarsky, Keejin Yoon, Salma AshShareef, Pragnya Adapa, Olivia Gruder, Stephanie Taylor, James E Eaton, Junzhong Xu, Francesca Bagnato","doi":"10.1016/j.acra.2024.08.048","DOIUrl":"https://doi.org/10.1016/j.acra.2024.08.048","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Several quantitative magnetic resonance imaging (MRI) methods are available to measure tissue injury in multiple sclerosis (MS), but their pathological specificity remains limited. The multi-compartment diffusion imaging using the spherical mean technique (SMT) overcomes several technical limitations of the diffusion-weighted image signal, thus delivering metrics with increased pathological specificity. Given these premises, here we assess whether the SMT-derived apparent axonal volume (V<sub>ax</sub>) provides a better tissue classifier than the diffusion tensor imaging (DTI)-derived axial diffusivity (AD) in the white matter (WM) of MS brains.</p><p><strong>Methods: </strong>Forty-three treatment-naïve people with newly diagnosed MS, clinically isolated syndrome, or radiologically isolated syndrome and 18 healthy controls (HCs) underwent a 3.0 Tesla MRI inclusive of T<sub>1</sub>-weighted (T<sub>1</sub>-w) and T<sub>2</sub>-w fluid-attenuated inversion recovery (FLAIR) sequences, and multi-b shell diffusion-weighted imaging. In patients only, pre- and post-gadolinium diethylenetriamine penta-acetic acid T<sub>1</sub>-w sequences were obtained for the evaluation of contrast-active lesions (CELs). V<sub>ax</sub> and AD were calculated in T<sub>2</sub>-lesions, chronic black holes (cBHs), and normal appearing (NAWM) in patients and normal WM (NWM) in HCs. V<sub>ax</sub> and AD values were compared across all the possible combinations of these regions. CELs were excluded from the analyses.</p><p><strong>Results: </strong>V<sub>ax</sub> differed in all comparisons (p ≤ 0.047 by paired t-test); AD differed in most comparisons (p < 0.001) except between NAWM and NWM, and between cBHs and T<sub>2</sub>-lesions. V<sub>ax</sub> had higher accuracy (p ≤ 0.029 by DeLong test) and larger effect size (p ≤ 0.038 by paired t-test) than AD in differentiating areas with even minimal tissue injury.</p><p><strong>Conclusions: </strong>V<sub>ax</sub> provides a better radiological quantitative discriminator of different degrees of axonal-mediated tissue injury even between areas with expected minimal pathology. Our data support further studies to assess the readiness of V<sub>ax</sub> as a measure of outcome for clinical trials on neuroprotection in MS.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1016/j.acra.2024.08.057
Yongping Hong,Xingxing Chen,Wei Sun,Guofeng Li
PURPOSEOur aim is to develop and validate an MRI-based diagnostic model for predicting pathological deterioration upgrading in rectal tumor.METHODSThis retrospective study included 158 eligible patients from January 2017 to November 2023. The patients were divided into a training group (n = 110) and a validation group (n = 48). Radiomics features were extracted from T2-weighted images to create a radiomics score model. Significant factors identified through multifactor analysis were used to develop the final clinical feature model. By combining these two models, an combined radiomics-clinical model was established. The model's performance was evaluated using Receiver Operating Characteristic (ROC) analysis and the Area Under the ROC Curve (AUC).RESULTSA total of 1197 features were extracted, with 11 features selected for calculating the radiomics score to establish the radiomics model. This model demonstrated good predictive performance for pathological upgrading in both the training and validation groups (AUC of 0.863 and 0.861, respectively). Clinical factors such as chief complaint and differential carcinoembryonic antigen levels showed statistical significance (P < 0.05). The clinical model, incorporating these factors, yielded AUC values of 0.669 and 0.651 for the training and validation groups, respectively. Furthermore, the radiomics-clinical combined model outperformed the individual models in predicting preoperative pathological upgrading in both the training and validation groups (AUC of 0.932 and 0.907, respectively).CONCLUSIONSA radiomics-clinical model, which combines clinical features with radiomics features based on MRI, can predict pathological deterioration upgrading in patients with rectal tumor and provide valuable insights for personalized treatment strategies.
{"title":"MRI-based radiomics features for prediction of pathological deterioration upgrading in rectal tumor.","authors":"Yongping Hong,Xingxing Chen,Wei Sun,Guofeng Li","doi":"10.1016/j.acra.2024.08.057","DOIUrl":"https://doi.org/10.1016/j.acra.2024.08.057","url":null,"abstract":"PURPOSEOur aim is to develop and validate an MRI-based diagnostic model for predicting pathological deterioration upgrading in rectal tumor.METHODSThis retrospective study included 158 eligible patients from January 2017 to November 2023. The patients were divided into a training group (n = 110) and a validation group (n = 48). Radiomics features were extracted from T2-weighted images to create a radiomics score model. Significant factors identified through multifactor analysis were used to develop the final clinical feature model. By combining these two models, an combined radiomics-clinical model was established. The model's performance was evaluated using Receiver Operating Characteristic (ROC) analysis and the Area Under the ROC Curve (AUC).RESULTSA total of 1197 features were extracted, with 11 features selected for calculating the radiomics score to establish the radiomics model. This model demonstrated good predictive performance for pathological upgrading in both the training and validation groups (AUC of 0.863 and 0.861, respectively). Clinical factors such as chief complaint and differential carcinoembryonic antigen levels showed statistical significance (P < 0.05). The clinical model, incorporating these factors, yielded AUC values of 0.669 and 0.651 for the training and validation groups, respectively. Furthermore, the radiomics-clinical combined model outperformed the individual models in predicting preoperative pathological upgrading in both the training and validation groups (AUC of 0.932 and 0.907, respectively).CONCLUSIONSA radiomics-clinical model, which combines clinical features with radiomics features based on MRI, can predict pathological deterioration upgrading in patients with rectal tumor and provide valuable insights for personalized treatment strategies.","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1016/j.acra.2024.07.052
Ning Mao,Yuhan Bao,Chuntong Dong,Heng Zhou,Haicheng Zhang,Heng Ma,Qi Wang,Haizhu Xie,Nina Qu,Peiyuan Wang,Fan Lin,Jie Lu
PURPOSETo develop and test a radiomics nomogram based on magnetic resonance imaging (MRI) and clinicopathological factors for predicting the axillary pathologic complete response (apCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients with axillary lymph node (ALN) metastases.MATERIALS AND METHODSA total of 319 patients who underwent MRI examination and received NAC treatment were enrolled from two centers, and the presence of ALN metastasis was confirmed by biopsy pathology before NAC. The radiomics features were extracted from regions of interest of ALNs before (pre-radiomics) and after (post-radiomics) NAC. The difference of features before and after NAC, named delta radiomics, was calculated. The variance threshold, selectKbest and least absolute shrinkage and selection operator algorithm were used to select radiomics features. Radscore was calculated by a linear combination of selected features, weighted by their respective coefficients. The univariate and multivariate logistic regression was used to select the clinicopathological factors and radscores, and a radiomics nomogram was built by multivariable logistic regression analysis. The performance of the nomogram was evaluated by the area under the receiver operator characteristic curve (AUC), decision curve analysis (DCA) and calibration curves. Furthermore, to explore the biological basis of radiomics nomogram, 16 patients with RNA-sequence data were included for genetic analysis.RESULTSThe radiomics nomogram was constructed by two radscores (post- and delta- radscores) and one clinicopathological factor (progesterone hormone, PR), and showed powerful predictive performance in both internal and external test sets, with AUCs of 0.894 (95% confidence interval [CI], 0.877-0.959) and 0.903 (95% CI, 0.801-0.986), respectively. The calibration curves and DCA showed favorable consistency and clinical utility. With the assistance of nomogram, the rate of unnecessary ALND would be reduced from 60.42% to 21.88%, and the rate of final benefit rate would be increased from 39.58% to 70.83%. Moreover, genetic analysis revealed that high apCR prediction scores were associated with the upregulation of immune-mediated genes and pathways.CONCLUSIONThe radiomics nomogram showed great performance in predicting apCR after NAC for breast cancer patients, which could help clinicians to identify patients with apCR and avoid unnecessary axillary lymph node dissection.
{"title":"Delta Radiomics Based on MRI for Predicting Axillary Lymph Node Pathologic Complete Response After Neoadjuvant Chemotherapy in Breast Cancer Patients.","authors":"Ning Mao,Yuhan Bao,Chuntong Dong,Heng Zhou,Haicheng Zhang,Heng Ma,Qi Wang,Haizhu Xie,Nina Qu,Peiyuan Wang,Fan Lin,Jie Lu","doi":"10.1016/j.acra.2024.07.052","DOIUrl":"https://doi.org/10.1016/j.acra.2024.07.052","url":null,"abstract":"PURPOSETo develop and test a radiomics nomogram based on magnetic resonance imaging (MRI) and clinicopathological factors for predicting the axillary pathologic complete response (apCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients with axillary lymph node (ALN) metastases.MATERIALS AND METHODSA total of 319 patients who underwent MRI examination and received NAC treatment were enrolled from two centers, and the presence of ALN metastasis was confirmed by biopsy pathology before NAC. The radiomics features were extracted from regions of interest of ALNs before (pre-radiomics) and after (post-radiomics) NAC. The difference of features before and after NAC, named delta radiomics, was calculated. The variance threshold, selectKbest and least absolute shrinkage and selection operator algorithm were used to select radiomics features. Radscore was calculated by a linear combination of selected features, weighted by their respective coefficients. The univariate and multivariate logistic regression was used to select the clinicopathological factors and radscores, and a radiomics nomogram was built by multivariable logistic regression analysis. The performance of the nomogram was evaluated by the area under the receiver operator characteristic curve (AUC), decision curve analysis (DCA) and calibration curves. Furthermore, to explore the biological basis of radiomics nomogram, 16 patients with RNA-sequence data were included for genetic analysis.RESULTSThe radiomics nomogram was constructed by two radscores (post- and delta- radscores) and one clinicopathological factor (progesterone hormone, PR), and showed powerful predictive performance in both internal and external test sets, with AUCs of 0.894 (95% confidence interval [CI], 0.877-0.959) and 0.903 (95% CI, 0.801-0.986), respectively. The calibration curves and DCA showed favorable consistency and clinical utility. With the assistance of nomogram, the rate of unnecessary ALND would be reduced from 60.42% to 21.88%, and the rate of final benefit rate would be increased from 39.58% to 70.83%. Moreover, genetic analysis revealed that high apCR prediction scores were associated with the upregulation of immune-mediated genes and pathways.CONCLUSIONThe radiomics nomogram showed great performance in predicting apCR after NAC for breast cancer patients, which could help clinicians to identify patients with apCR and avoid unnecessary axillary lymph node dissection.","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Implementing Patient-Centered Care in Breast Imaging: What is the evidence?","authors":"Soraia Quaranta Damião,Almir Galvão Vieira Bitencourt","doi":"10.1016/j.acra.2024.08.019","DOIUrl":"https://doi.org/10.1016/j.acra.2024.08.019","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142262197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.1016/j.acra.2024.08.055
Xinyu Li,Fang Yuan,Li Ni,Xiaopan Li
RATIONALE AND OBJECTIVESAt present, the application of magnetic resonance imaging (MRI) in the prediction of response to neoadjuvant therapy and concurrent chemoradiotherapy for the treatment of esophageal cancer still needs to be further explored, and its early differential value remains controversial, thus we carried out this systematic review with a meta-analysis. In the application, different MRI sequences and corresponding parameters are used for the differential diagnosis of the response to neoadjuvant therapy and concurrent chemoradiotherapy.METHODSAll relevant studies evaluated the efficacy and response to MRI in neoadjuvant therapy or concurrent chemoradiotherapy for esophageal cancer on Pubmed, Embase, Cohrane Library, and Web of Science databases published before October 10, 2023 (inclusive) were systematically searched. A revised tool was used to assess the quality of diagnostic accuracy studies (QUADAS-2) to assess the risk of bias in the included original studies. A subgroup analysis of MRI sequences diffusion weighted imaging (DWI), dynamic contrast enhanced (DCE) and their corresponding different parameters, as well as the acquisition timepoints (before and after treatment) for different parameters, was performed during the meta-analysis. The bivariate mixed-effects model was used for meta-analysis.RESULTS21 studies were finally included, involving 1128 patients with esophageal cancer. The sensitivity, specificity, and area under receiver operating characteristic curve (ROC curve) of DWI sequence for identifying response to concurrent chemoradiotherapy were 0.82 (95% CI: 0.74-0.87), 0.81 (95% CI: 0.72-0.87) and 0.88 (95% CI: 0.56-0.98), respectively. The sensitivity, specificity, and area under ROC curve of DCE sequence for identifying response to concurrent chemoradiotherapy were 0.78 (95% CI: 0.70-0.84), 0.65 (95% CI: 0.59-0.70) and 0.73 (95% CI: 0.50-0.88), respectively. In patients with esophageal cancer, the sensitivity, specificity, and area under the ROC curve of DWI sequences for identifying response to neoadjuvant therapy were 0.80 (95% CI: 0.69 - 0.88), 0.81 (95% CI: 0.69 - 0.89), and 0.88 (95% CI: 0.34 - 0.99), respectively; the sensitivity, specificity, and area under the ROC curve of DCE sequences for identifying response to neoadjuvant therapy were 0.84 (95% CI: 0.76 - 0.90), 0.61 (95% CI: 0.53 - 0.68), and 0.70 (95% CI: 0.27 - 0.94), respectively.CONCLUSIONSBased on the available evidence, MRI had a very good value in the early identification of response to neoadjuvant therapy and concurrent chemoradiotherapy for esophageal cancer, especially DWI. Apparent diffusion coefficient (ADC) value changes before and after treatment could be used as predictors of pathological response. Also, ADC value changes before and after treatment could be used as a tool to guide clinical decision-making.
{"title":"Meta-Analysis of MRI in Predicting Early Response to Radiotherapy and Chemotherapy in Esophageal Cancer.","authors":"Xinyu Li,Fang Yuan,Li Ni,Xiaopan Li","doi":"10.1016/j.acra.2024.08.055","DOIUrl":"https://doi.org/10.1016/j.acra.2024.08.055","url":null,"abstract":"RATIONALE AND OBJECTIVESAt present, the application of magnetic resonance imaging (MRI) in the prediction of response to neoadjuvant therapy and concurrent chemoradiotherapy for the treatment of esophageal cancer still needs to be further explored, and its early differential value remains controversial, thus we carried out this systematic review with a meta-analysis. In the application, different MRI sequences and corresponding parameters are used for the differential diagnosis of the response to neoadjuvant therapy and concurrent chemoradiotherapy.METHODSAll relevant studies evaluated the efficacy and response to MRI in neoadjuvant therapy or concurrent chemoradiotherapy for esophageal cancer on Pubmed, Embase, Cohrane Library, and Web of Science databases published before October 10, 2023 (inclusive) were systematically searched. A revised tool was used to assess the quality of diagnostic accuracy studies (QUADAS-2) to assess the risk of bias in the included original studies. A subgroup analysis of MRI sequences diffusion weighted imaging (DWI), dynamic contrast enhanced (DCE) and their corresponding different parameters, as well as the acquisition timepoints (before and after treatment) for different parameters, was performed during the meta-analysis. The bivariate mixed-effects model was used for meta-analysis.RESULTS21 studies were finally included, involving 1128 patients with esophageal cancer. The sensitivity, specificity, and area under receiver operating characteristic curve (ROC curve) of DWI sequence for identifying response to concurrent chemoradiotherapy were 0.82 (95% CI: 0.74-0.87), 0.81 (95% CI: 0.72-0.87) and 0.88 (95% CI: 0.56-0.98), respectively. The sensitivity, specificity, and area under ROC curve of DCE sequence for identifying response to concurrent chemoradiotherapy were 0.78 (95% CI: 0.70-0.84), 0.65 (95% CI: 0.59-0.70) and 0.73 (95% CI: 0.50-0.88), respectively. In patients with esophageal cancer, the sensitivity, specificity, and area under the ROC curve of DWI sequences for identifying response to neoadjuvant therapy were 0.80 (95% CI: 0.69 - 0.88), 0.81 (95% CI: 0.69 - 0.89), and 0.88 (95% CI: 0.34 - 0.99), respectively; the sensitivity, specificity, and area under the ROC curve of DCE sequences for identifying response to neoadjuvant therapy were 0.84 (95% CI: 0.76 - 0.90), 0.61 (95% CI: 0.53 - 0.68), and 0.70 (95% CI: 0.27 - 0.94), respectively.CONCLUSIONSBased on the available evidence, MRI had a very good value in the early identification of response to neoadjuvant therapy and concurrent chemoradiotherapy for esophageal cancer, especially DWI. Apparent diffusion coefficient (ADC) value changes before and after treatment could be used as predictors of pathological response. Also, ADC value changes before and after treatment could be used as a tool to guide clinical decision-making.","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.1016/j.acra.2024.08.050
Lily M Belfi,Reni Butler,Constantine M Burgan,Alison Chetlen,Terry Desser,Sosamma T Methratta,Lori A Deitte
{"title":"Strategies to Optimize Well-Being During Transitions in the Life Cycle of a Radiologist.","authors":"Lily M Belfi,Reni Butler,Constantine M Burgan,Alison Chetlen,Terry Desser,Sosamma T Methratta,Lori A Deitte","doi":"10.1016/j.acra.2024.08.050","DOIUrl":"https://doi.org/10.1016/j.acra.2024.08.050","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142262196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.1016/j.acra.2024.08.062
Chase S Hall
{"title":"Invisible Insights: Probing Lung Function with 129Xe MRI.","authors":"Chase S Hall","doi":"10.1016/j.acra.2024.08.062","DOIUrl":"https://doi.org/10.1016/j.acra.2024.08.062","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142262195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-10DOI: 10.1016/j.acra.2024.08.035
Fabian Schmitz,Sam Sedaghat
Soft tissue sarcomas (STS) are a heterogeneous group of rare malignant tumors. Tumor grade might be underestimated in biopsy due to intratumoral heterogeneity. This mini-review aims to present the current state of predicting malignancy grades of STS through radiomics, machine learning, and deep learning on magnetic resonance imaging (MRI). Several studies investigated various machine-learning and deep-learning approaches in T2-weighted (w) images, contrast-enhanced (CE) T1w images, and DWI/ADC maps with promising results. Combining semantic imaging features, radiomics features, and deep-learning signatures in machine-learning models has demonstrated superior predictive performances compared to individual feature sources. Furthermore, incorporating features from both tumor volume and peritumor region is beneficial. Especially random forest and support vector machine classifiers, often combined with the least absolute shrinkage and selection operator (LASSO) and/or synthetic minority oversampling technique (SMOTE), did show high area under the curve (AUC) values and accuracies in existing studies.
{"title":"Diagnostic Value of Magnetic Resonance Imaging Radiomics and Machine-learning in Grading Soft Tissue Sarcoma: A Mini-review on the Current State.","authors":"Fabian Schmitz,Sam Sedaghat","doi":"10.1016/j.acra.2024.08.035","DOIUrl":"https://doi.org/10.1016/j.acra.2024.08.035","url":null,"abstract":"Soft tissue sarcomas (STS) are a heterogeneous group of rare malignant tumors. Tumor grade might be underestimated in biopsy due to intratumoral heterogeneity. This mini-review aims to present the current state of predicting malignancy grades of STS through radiomics, machine learning, and deep learning on magnetic resonance imaging (MRI). Several studies investigated various machine-learning and deep-learning approaches in T2-weighted (w) images, contrast-enhanced (CE) T1w images, and DWI/ADC maps with promising results. Combining semantic imaging features, radiomics features, and deep-learning signatures in machine-learning models has demonstrated superior predictive performances compared to individual feature sources. Furthermore, incorporating features from both tumor volume and peritumor region is beneficial. Especially random forest and support vector machine classifiers, often combined with the least absolute shrinkage and selection operator (LASSO) and/or synthetic minority oversampling technique (SMOTE), did show high area under the curve (AUC) values and accuracies in existing studies.","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142262198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
RATIONALE AND OBJECTIVESEvaluating the capability of CT nomograms and CT-based radiomics nomograms to differentiate between Bronchiolar Adenoma (BA) and Early-stage Lung Adenocarcinoma (LUAD).MATERIALS AND METHODSIn this retrospective study; we analyzed data from 226 patients who were treated at our institution and pathologically confirmed to have either BA or Early-stage LUAD. Patients were randomly divided into a training cohort (n=158) and a testing cohort (n=68). All CT images were independently analyzed and measured by two radiologists using conventional computed tomography. Clinical predictive factors were identified using logistic regression. Multivariable logistic regression analysis was used to construct differential diagnostic models for BA and early-stage LUAD, including traditional CT and radiomics models. The performance of the models was determined based on the area under the receiver operating characteristic curve, discrimination ability, and decision curve analysis (DCA).RESULTSLesion shape, tumor-lung interface, and pleural retraction signs were identified as independent clinical predictors. The areas under the curve for the CT nomogram, radiomic features, and radiomics nomogram were 0.854, 0.769, and 0.901, respectively. Both the CT nomogram and the radiomics nomogram demonstrated good generalizability in distinguishing between the two entities. DCA indicated that the nomograms achieved a higher net benefit compared to the use of radiomic features alone.CONCLUSIONThe two preoperative nomograms hold significant value in differentiating between patients with BA and those with Early-stage LUAD, and they contribute to informed clinical treatment decision-making.
材料与方法在这项回顾性研究中，我们分析了在本院接受治疗并经病理证实患有支气管腺瘤（BA）或早期肺腺癌（LUAD）的 226 名患者的数据。患者被随机分为训练组（158 人）和测试组（68 人）。所有 CT 图像均由两名放射科医生使用传统计算机断层扫描进行独立分析和测量。采用逻辑回归法确定临床预测因素。多变量逻辑回归分析用于构建 BA 和早期 LUAD 的差异诊断模型，包括传统 CT 模型和放射组学模型。结果肿块形状、肿瘤-肺界面和胸膜回缩征被确定为独立的临床预测因素。CT提名图、放射学特征和放射学提名图的曲线下面积分别为0.854、0.769和0.901。CT 提名图和放射组学提名图在区分两种实体方面都表现出良好的普适性。结论 这两个术前提名图在区分 BA 患者和早期 LUAD 患者方面具有重要价值，有助于做出明智的临床治疗决策。
{"title":"Preoperative CT and Radiomics Nomograms for Distinguishing Bronchiolar Adenoma and Early-Stage Lung Adenocarcinoma.","authors":"Xiulan Liu,Yanqiong Xu,Jiajia Shu,Yan Zuo,Zhi Li,Meng Lin,Chenrong Li,Yuqi Liu,Xianhong Wang,Ying Zhao,Zihong Du,Gang Wang,Wenjia Li","doi":"10.1016/j.acra.2024.08.047","DOIUrl":"https://doi.org/10.1016/j.acra.2024.08.047","url":null,"abstract":"RATIONALE AND OBJECTIVESEvaluating the capability of CT nomograms and CT-based radiomics nomograms to differentiate between Bronchiolar Adenoma (BA) and Early-stage Lung Adenocarcinoma (LUAD).MATERIALS AND METHODSIn this retrospective study; we analyzed data from 226 patients who were treated at our institution and pathologically confirmed to have either BA or Early-stage LUAD. Patients were randomly divided into a training cohort (n=158) and a testing cohort (n=68). All CT images were independently analyzed and measured by two radiologists using conventional computed tomography. Clinical predictive factors were identified using logistic regression. Multivariable logistic regression analysis was used to construct differential diagnostic models for BA and early-stage LUAD, including traditional CT and radiomics models. The performance of the models was determined based on the area under the receiver operating characteristic curve, discrimination ability, and decision curve analysis (DCA).RESULTSLesion shape, tumor-lung interface, and pleural retraction signs were identified as independent clinical predictors. The areas under the curve for the CT nomogram, radiomic features, and radiomics nomogram were 0.854, 0.769, and 0.901, respectively. Both the CT nomogram and the radiomics nomogram demonstrated good generalizability in distinguishing between the two entities. DCA indicated that the nomograms achieved a higher net benefit compared to the use of radiomic features alone.CONCLUSIONThe two preoperative nomograms hold significant value in differentiating between patients with BA and those with Early-stage LUAD, and they contribute to informed clinical treatment decision-making.","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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