Cancer Imaging最新文献

Background: In 2020, we introduced the Greater Omentum Imaging-Reporting and Data System (GOI-RADS), a novel classification system related to peritoneal lesions. However, its clinical application remained unvalidated.

Objective: This study aimed to validate GOI-RADS, optimize its parameters for a new grading system, and explore its clinical usefulness.

Methods: A retrospective-prospective study was conducted to validate and refine the GOI-RADS system. The study consisted of two phases: a retrospective validation phase and a prospective application phase. The first phase included patients with peritoneal lesions from 2019 to 2021, classified by GOI-RADS and verified against pathology. Contrast-enhanced ultrasound (CEUS) and real-time elastography (RTE) data were collected for developing a new grading system. Odds ratios optimized parameters. The second phase (2021-2024) assessed diagnostic consistency among sonographers and performance of grading systems.

Results: Among 215 patients with peritoneal lesions, the actual malignancy rates for GOI-RADS 2 (40.00%) and GOI-RADS 3 (61.22%) were much higher than predicted (5.56% and 37.25%). Combining CEUS and RTE parameters showed varying sensitivity and specificity: RTE + GOI-RADS (95.35%, 55.56%) and CEUS + GOI-RADS (96.51%, 44.44%). However, the grading system based on multiple ultrasound parameters, specifically when incorporating RTE, CEUS parameters, and GOI-RADS (Multi-GOIRADS), exhibited the highest diagnostic sensitivity and specificity of 88.37% and 83.33%, respectively. Its simplified version, sMulti-GOIRADS, had sensitivity of 73.26% and specificity of 94.44%. In the prospective study involving three sonographers of different qualifications, the use of sMulti-GOIRADS was found to be the most time-efficient and showed excellent diagnostic consistency among them. In contrast, Multi-GOIRADS required more time for scoring but offered superior diagnostic performance, particularly among senior sonographers (88.35% and 91.43%).

Conclusions: This study proposes a multiparametric ultrasound-based imaging-reporting and data system for risk stratification of omental malignancy, Multi-GOIRADS, and presents an optimized and simplified version, sMulti-GOIRADS, which demonstrates excellent diagnostic consistency and performance in clinical applications.

Background: To construct and assess a deep learning (DL) signature that employs computed tomography imaging to predict the expression status of programmed cell death ligand 1 in patients with bladder cancer (BCa).

Methods: This retrospective study included 190 patients from two hospitals who underwent surgical removal of BCa (training set/external validation set, 127/63). We used convolutional neural network and radiomics machine learning technology to generate prediction models. We then compared the performance of the DL signature with the radiomics machine learning signature and selected the optimal signature to build a nomogram with the clinical model. Finally, the internal forecasting process of the DL signature was explained using Shapley additive explanation technology.

Results: On the external validation set, the DL signature had an area under the curve of 0.857 (95% confidence interval: 0.745-0.932), and demonstrated superior prediction performance in comparison with the other models. SHAP expression images revealed that the prediction of PD-L1 expression status is mainly influenced by the tumor edge region, particularly the area close to the bladder wall.

Conclusions: The DL signature performed well in comparison with other models and proved to be a valuable, dependable, and interpretable tool for predicting programmed cell death ligand 1 expression status in patients with BCa.

Objectives: To evaluate the feasibility and value of dynamic contrast-enhanced MRI (DCE-MRI) quantitative analysis and MRI-based radiomics in predicting the efficacy of microwave ablation (MWA) in lung cancers (LCs).

Methods: Forty-three patients with LCs who underwent DCE-MRI within 24 h of receiving MWA were enrolled in the study and divided into two groups according to the modified response evaluation criteria in solid tumors (m-RECIST) criteria: the effective treatment (complete response + partial response + stable disease, n = 28) and the ineffective treatment (progressive disease, n = 15). DCE-MRI datasets were processed by Omni. Kinetics software, using the extended tofts model (ETM) and exchange model (ECM) to yield pharmacokinetic parameters and their histogram features. Changes in quantitative perfusion parameters were compared between the two groups. Scientific research platform ( https://medresearch.shukun.net/ ) was used for radiomics analysis. A total of 1874 radiomic features were extracted for each tumor by manually segmentation of T1WI and Contrast-enhanced of T1WI (Ce-T1WI) fat inhibition sequence. The performances of radiomics models were evaluated by the receiver operating characteristic curve. Based on radiomics features, survival curves were generated by Kaplan-Meier survival analysis to evaluate patient outcomes. P < 0.05 was set for the significance threshold.

Results: The V_p value of ECM was significantly higher in the ineffective group compared to the effective group (p = 0.027). Additionally, the skewness, and kurtosis of V_p (p = 0.020 and 0.013, respectively) derived from ETM and F_p (p = 0.027 and 0.030, respectively) from ECM as well as the quantiles were higher in the ineffective group than in the effective group. Significant statistical differences were observed in the energy and entropy of V_e (p = 0.044 and 0.025, respectively) and V_p (p = 0.025 and 0.026, respectively) between the two groups. In the process of model construction, seven features from T1WI, five features from Ce-T1WI, and ten features from combined sequences were ultimately selected. The area under the curve (AUC) values for the T1WI model, Ce-T1WI model, and combined model were 0.910, 0.890, 0.965 in the training group, and 0.850, 0.700, 0.850 in the test group, respectively.

Conclusions: DCE-MRI quantitative analysis and MRI-based radiomics may be helpful in assessing the early response to MWA in patients with LCs.

Background: Apart from rare cases such as lymphomas, germ cell tumors, neuroendocrine neoplasms, and thymic hyperplasia, thymic mass lesions (TMLs) are typically categorized into cysts, and thymomas. However, the classification results cannot be determined in advance and can only be confirmed through postoperative pathology. Therefore, the objective of this study is to rely on clinical parameters and radiomic features extracted from chest computed tomography (CT) scans to facilitate the preoperative classification of TMLs. The model development specifically focused on thymic cysts and thymomas, as these are the most commonly encountered anterior mediastinal tumors in clinical practice.

Materials and methods: This retrospective study included 400 participants from 3 hospitals between September 2017 and September 2024 due to TMLs. The participants were classified into 7 groups based on the ultimately confirmed etiology: thymic cysts and thymomas, including types A, AB, B1, B2, B3, and C. All participants underwent contrast-enhanced chest CT scans, with senior radiologists delineating regions of interest to extract radiomic features. Additionally, the participants' ages were also collected as clinical parameters for analysis. The participants were randomly allocated into a training set and a validation set at a 7:3 ratio. A classifier models were developed using the data from the training set, and their performances were evaluated on the validation set.

Results: The model exhibited good classification performance with accuracies of 0.8547.

Conclusion: The model can assist in early diagnosis and the development of personalized treatment strategies for patients with TMLs.

Background: Imaging genomics is a burgeoning field that seeks to connections between medical imaging and genomic features. It has been widely applied to explore heterogeneity and predict responsiveness and disease progression in cancer. This review aims to assess current applications and advancements of imaging genomics in cancer.

Methods: Literature on imaging genomics in cancer was retrieved and selected from PubMed, Web of Science, and Embase before July 2024. Detail information of articles, such as systems and imaging features, were extracted and analyzed. Citation information was extracted from Web of Science and Scopus. Additionally, a bibliometric analysis of the included studies was conducted using the Bibliometrix R package and VOSviewer.

Results: A total of 370 articles were included in the study. The annual growth rate of articles on imaging genomics in cancer is 24.88%. China (133) and the USA (107) were the most productive countries. The top 2 keywords plus were "survival" and "classification". The current research mainly focuses on the central nervous system (121) and the genitourinary system (110, including 44 breast cancer articles). Despite different systems utilizing different imaging modalities, more than half of the studies in each system employed radiomics features.

Conclusions: Publication databases provide data support for imaging genomics research. The development of artificial intelligence algorithms, especially in feature extraction and model construction, has significantly advanced this field. It is conducive to enhancing the related-models' interpretability. Nonetheless, challenges such as the sample size and the standardization of feature extraction and model construction must overcome. And the research trends revealed in this study will guide the development of imaging genomics in the future and contribute to more accurate cancer diagnosis and treatment in the clinic.

Background: ⁶⁸Ga-FAPI (fibroblast activation protein inhibitor) is a novel and highly promising radiotracer for PET/CT imaging. It has shown significant tumor uptake and high sensitivity in lesion detection across a range of cancer types. We aimed to compare the diagnostic value of ⁶⁸Ga-FAPI and ¹⁸F-FDG PET/CT in common gynecological malignancies.

Methods: This retrospective study included 35 patients diagnosed with common gynecological tumors, including breast cancer, ovarian cancer, and cervical cancer. Among the 35 patients, 27 underwent PET/CT for the initial assessment of tumors, while 8 were assessed for recurrence detection. The median and range of tumor size and maximum standardized uptake values (SUV_max) were calculated.

Results: Thirty-five patients (median age, 57 years [interquartile range], 51-65 years) were evaluated. In treatment-naive patients (n = 27), ⁶⁸Ga-FAPI PET/CT led to upstaging of the clinical TNM stage in five (19%) patients compared with ¹⁸F-FDG PET/CT. No significant difference in tracer uptake was observed between ¹⁸F-FDG and ⁶⁸Ga-FAPI for primary lesions: breast cancer (7.2 vs. 4.9, P = 0.086), ovarian cancer (16.3 vs. 15.7, P = 0.345), and cervical cancer (18.3 vs. 17.1, P = 0.703). For involved lymph nodes, ⁶⁸Ga-FAPI PET/CT demonstrated a higher SUV_max for breast cancer (9.9 vs. 6.1, P = 0.007) and cervical cancer (6.3 vs. 4.8, P = 0.048), while no significant difference was noted for ovarian cancer (7.0 vs. 5.9, P = 0.179). Furthermore, ⁶⁸Ga-FAPI PET/CT demonstrated higher specificity and accuracy compared to ¹⁸F-FDG PET/CT for detecting metastatic lymph nodes (100% vs. 66%, P < 0.001; 94% vs. 80%, P < 0.001). In contrast, sensitivity did not differ significantly (97% vs. 86%, P = 0.125). For most distant metastases, ⁶⁸Ga-FAPI exhibited a higher SUV_max than ¹⁸F-FDG in bone metastases (12.9 vs. 4.9, P = 0.036).

Conclusions: ⁶⁸Ga-FAPI PET/CT demonstrated higher tracer uptake and was superior to ¹⁸F-FDG PET/CT in detecting primary and metastatic lesions in patients with common gynecological malignancies.

Trial registration: ChiCTR, ChiCTR2100044131. Registered 10 October 2022, https://www.chictr.org.cn , ChiCTR2100044131.

Objective: Exploring the construction of a fusion model that combines radiomics and deep learning (DL) features is of great significance for the precise preoperative diagnosis of meningioma sinus invasion.

Materials and methods: This study retrospectively collected data from 601 patients with meningioma confirmed by surgical pathology. For each patient, 3948 radiomics features, 12,288 VGG features, 6144 ResNet features, and 3072 DenseNet features were extracted from MRI images. Thus, univariate logistic regression, correlation analysis, and the Boruta algorithm were applied for further feature dimension reduction, selecting radiomics and DL features highly associated with meningioma sinus invasion. Finally, diagnosis models were constructed using the random forest (RF) algorithm. Additionally, the diagnostic performance of different models was evaluated using receiver operating characteristic (ROC) curves, and AUC values of different models were compared using the DeLong test.

Results: Ultimately, 21 features highly associated with meningioma sinus invasion were selected, including 6 radiomics features, 2 VGG features, 7 ResNet features, and 6 DenseNet features. Based on these features, five models were constructed: the radiomics model, VGG model, ResNet model, DenseNet model, and DL-radiomics (DLR) fusion model. This fusion model demonstrated superior diagnostic performance, with AUC values of 0.818, 0.814, and 0.769 in the training set, internal validation set, and independent external validation set, respectively. Furthermore, the results of the DeLong test indicated that there were significant differences between the fusion model and both the radiomics model and the VGG model (p < 0.05).

Conclusions: The fusion model combining radiomics and DL features exhibits superior diagnostic performance in preoperative diagnosis of meningioma sinus invasion. It is expected to become a powerful tool for clinical surgical plan selection and patient prognosis assessment.

Pelvic MRI is essential for evaluating local and regional tumor extent in uterine cervical cancer (CC). Tumor microstructure captured by diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) markers may be closely linked to prognosis in CC.Purpose To explore whether primary tumor ADC markers predict survival in CC.Material and methods CC patients (n = 179) diagnosed during 2009-2020 with MRI-assessed primary maximum tumor_size ≥ 2 cm were included in this retrospective single-center study. Two radiologists read all MRIs independently, measuring mean tumor ADC values in manually drawn regions of interest (ROIs) and mean tumor ADC (tumor_ADCmean) from five measurements for the two readers was used. ADC from ROIs in the myometrium (myometrium_ADC), cervical stroma (cervix_ADC), and bladder (bladder_ADC) were used to calculate ADC ratios. ADC markers were explored in relation to the International Federation of Gynecology and Obstetrics (FIGO) (2018) stage, disease-specific survival (DSS), and recurrence/progression-free survival (RPFS).Results Inter-reader agreement for all ADC measurements was high (ICC:0.59-0.79). Low tumor_ADCmean predicted advanced FIGO stage (P = 0.04) and reduced DSS (hazard ratio (HR): 0.96, P < 0.001; AIC: 441). Myometrium_ADC/tumor_ADCmean yielded the best Cox regression fit (AIC = 430) among all tumor ADC markers. Patients with high myometrium_ADC/tumor_ADCmean had significantly reduced 5-year DSS for FIGO stage I, II, and III (P = 0.01, 0.004, and 0.02, respectively) and tended to the same for FIGO IV (P = 0.22).Conclusion Low tumor_ADCmean predicted reduced DSS in CC. High myometrium_ADC/tumor_ADCmean was the strongest ADC predictor of poor DSS and a marker of high-risk phenotype independent of FIGO stage.

Objective: To refine the existing CT algorithm to enhance inter-reader agreement and improve the diagnostic performance for clear-cell renal cell carcinoma (ccRCC) in solid renal masses less than 4 cm.

Methods: A retrospective collection of 331 patients with pathologically confirmed renal masses were enrolled in this study. Two radiologists independently assessed the CT images: in addition to heterogeneity score (HS) and mass-to-cortex corticomedullary attenuation ratio (MCAR), measured parameters included ratio of major diameter to minor diameter at the maximum axial section (Major axis / Minor axis), tumor-renal interface, standardized heterogeneity ratio (SHR), and standardized nephrographic reduction rate (SNRR). Spearman's correlation analysis was performed to evaluate the relationship between SHR and HS. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors and then CT-score was adjusted by those indicators. The diagnostic efficacy of the modified CT-scores was evaluated using ROC curve analysis.

Results: The SHR and heterogeneity grade (HG) of mass were correlated positively with the HS (R = 0.749, 0.730, all P < 0.001). Logistic regression analysis determined that the Major axis / Minor axis (> 1.16), the tumor-renal interface (> 22.3 mm), and the SNRR (> 0.16) as additional independent risk factors to combine with HS and MCAR. Compared to the original CT-score, the two CT algorithms combined tumor-renal interface and SNRR showed significantly improved diagnostic efficacy for ccRCC (AUC: 0.770 vs. 0.861 and 0.862, all P < 0.001). The inter-observer agreement for HG was higher than that for HS (weighted Kappa coefficient: 0.797 vs. 0.722). The consistency of modified CT-score was also superior to original CT-score (weighted Kappa coefficient: 0.935 vs. 0.878).

Conclusion: The modified CT algorithms not only enhanced inter-reader consistency but also improved the diagnostic capability for ccRCC in small renal masses.

Background: This study aims to introduce the concept of habitat subregions and construct an accurate prediction model by analyzing refined medical images, to predict lymph node metastasis (LNM) in patients with intrahepatic cholangiocarcinoma (ICC) before surgery, and to provide personalized support for clinical decision-making.

Methods: Clinical, radiological, and pathological data from ICC patients were retrospectively collected. Using information from the arterial and venous phases of multisequence CT images, tumor habitat subregions were delineated through the K-means clustering algorithm. Radiomic features were extracted and screened, and prediction models based on different subregions were constructed and compared with traditional intratumoral models. Finally, a lymph node metastasis prediction model was established by integrating the features of several subregional models, and its performance was evaluated.

Results: A total of 164 ICC patients were included in this study, 103 of whom underwent lymph node dissection. The patients were divided into LNM- and LNM + groups on the basis of lymph node status, and significant differences in white blood cell indicators were found between the two groups. Survival analysis revealed that patients with positive lymph nodes had significantly worse prognoses. Through cluster analysis, the optimal number of habitat subregions was determined to be 5, and prediction models based on different subregions were constructed. A comparison of the performance of each model revealed that the Habitat1 and Habitat5 models had excellent performance. The optimal model obtained by fusing the features of the Habitat1 and Habitat5 models had AUC values of 0.923 and 0.913 in the training set and validation set, respectively, demonstrating good predictive ability. Calibration curves and decision curve analysis further validated the superiority and clinical application value of the model.

Conclusions: This study successfully constructed an accurate prediction model based on habitat subregions that can effectively predict the lymph node metastasis of ICC patients preoperatively. This model is expected to provide personalized decision support to clinicians and help to optimize treatment plans and improve patient outcomes.