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Background Tumor fraction (TF) at liquid biopsy is a potential noninvasive marker for tumor burden, but validation is needed. Purpose To evaluate TF as a potential surrogate for tumor burden, assessed at contrast-enhanced CT across diverse metastatic cancers. Methods This retrospective monocentric study included patients with cancer and metastatic disease, with TF results and contemporaneous contrast-enhanced CT performed between January 2021 and January 2023. The total tumor volume (TTV), representing CT tumor burden, was calculated by adding all lesion volumes and was computed by using manually outlined annotations of each lesion on the largest surface of the axial slice. TF greater than 10% was considered high. A training-validation split was applied. Correlations between TF and TTV were assessed using regression models and Spearman correlation coefficients. Receiver operating characteristic curve analysis established the TTV cutoff. The metastatic site, histology type, and TTV were used to predict liquid biopsy contributory status. Results Among 1065 patients (median age, 62 years [IQR: 53, 70]; 537 female), 56 288 lesions were annotated, mostly in the lung ( n = 20 334), lymph nodes ( n = 11 651), and liver ( n = 10 277). A total of 763 liquid biopsies were contributive, 254 were noncontributive, and 48 failed. The training and validation sets included 745 and 320 patients, respectively. TF helped predict TTV with the linear model ( R 2 = 0.17; ρ = 0.41; P < .001). The TTV and TF categories achieved an area under the receiver operating characteristic curve (AUC) of 0.74 (95% CI: 0.71, 0.78), with an optimal cutoff of 151 cm 3 for TTV and a TF cutoff of 10%. The sensitivity was 57% (204 of 359) and the specificity was 80% (525 of 658). TTV helped predict contributory status, with an AUC of 0.71 (95% CI: 0.67, 0.76) and an optimal cutoff greater than 37 cm 3 . Liver lesion volumes were significantly associated with a contributory liquid biopsy in the validation cohort. Conclusion While correlated, TF at liquid biopsy did not accurately represent the TTV at CT. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Koh in this issue.

Background: This retrospective study determined survival responses to immune checkpoint inhibitors (ICIs), comparing mono- (mono) and combo-immunotherapy (combo) in patients with microsatellite instability-high (MSI-H) metastatic colorectal cancer (mCRC) by analyzing quantitative imaging data and clinical factors., Methods: One hundred fifty patients were included from two centers and divided into training (n = 105) and validation (n = 45) cohorts. Radiologists manually annotated chest-abdomen-pelvis computed tomography and calculated tumor burden. Progression-free survival (PFS) was assessed, and variables were selected through Recursive Feature Elimination. Cutoff values were determined using maximally selected rank statistics to binarize features, forming a risk score with hazard ratio-derived weights., Results: In total, 2258 lesions were annotated with excellent reproducibility. Key variables in the training cohort included: total tumor volume (cutoff: 73 cm 3 ), lesion count (cutoff: 20), age (cutoff: 60) and the presence of peritoneal carcinomatosis. Their respective weights were 1.13, 0.96, 0.91, and 0.38, resulting in a risk score cutoff of 1.36. Low-score patients showed similar overall survival and PFS regardless of treatment, while those with a high-score had significantly worse survivals with mono vs combo (P = 0.004 and P = 0.0001). In the validation set, low-score patients exhibited no significant difference in overall survival and PFS with mono or combo. However, patients with a high-score had worse PFS with mono (P = 0.046)., Conclusions: A score based on total tumor volume, lesion count, the presence of peritoneal carcinomatosis, and age can guide MSI-H mCRC treatment decisions, allowing oncologists to identify suitable candidates for mono and combo ICI therapies., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/ personal relationships which may be considered as potential competing interests: Thierry ANDRE reports consulting/advisory role and/or honoraria from Amgen, Aptitude Health, Astellas Pharma, Astrazeneca, Bristol-Myers Squibb, Gritstone Oncology, GlaxoSmithKline, Merck & Co., Inc., Merck Serono, Roche, Sanofi, Seagen, Servier, Takeda, compensation for travel, accommodation expenses from Bristol-Myers Squibb, MSD & Co., Inc, and DMC member role for Inspirna. RC has received personal fees from Bristol-Myers Squibb, Exeliom Biosciences, Enterome Bioscience, MSD Oncology, Mylan Medical, Pierre Fabre, Servier and non-financial support from Amgen, Bristol-Myers Squibb, Mylan Medical and Servier. AH reports consulting/advisory role and/or honoraria from Basilea, Debiopharm, EISAI, Incyte, QED Therapeutics, Relay Therapeutics, Servier, Tahio, AstraZeneca and Reseach Grant from Incite. Nathalie LASSAU (professor of radiology) reports grand for institute Gustave Roussy from Guerbet and fees (Speaker) from Jazz Pharmaceuticals. Other authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Purpose: The purpose of this study was to propose a deep learning-based approach to detect pulmonary embolism and quantify its severity using the Qanadli score and the right-to-left ventricle diameter (RV/LV) ratio on three-dimensional (3D) computed tomography pulmonary angiography (CTPA) examinations with limited annotations., Materials and Methods: Using a database of 3D CTPA examinations of 1268 patients with image-level annotations, and two other public datasets of CTPA examinations from 91 (CAD-PE) and 35 (FUME-PE) patients with pixel-level annotations, a pipeline consisting of: (i), detecting blood clots; (ii), performing PE-positive versus negative classification; (iii), estimating the Qanadli score; and (iv), predicting RV/LV diameter ratio was followed. The method was evaluated on a test set including 378 patients. The performance of PE classification and severity quantification was quantitatively assessed using an area under the curve (AUC) analysis for PE classification and a coefficient of determination (R²) for the Qanadli score and the RV/LV diameter ratio., Results: Quantitative evaluation led to an overall AUC of 0.870 (95% confidence interval [CI]: 0.850-0.900) for PE classification task on the training set and an AUC of 0.852 (95% CI: 0.810-0.890) on the test set. Regression analysis yielded R² value of 0.717 (95% CI: 0.668-0.760) and of 0.723 (95% CI: 0.668-0.766) for the Qanadli score and the RV/LV diameter ratio estimation, respectively on the test set., Conclusion: This study shows the feasibility of utilizing AI-based assistance tools in detecting blood clots and estimating PE severity scores with 3D CTPA examinations. This is achieved by leveraging blood clots and cardiac segmentations. Further studies are needed to assess the effectiveness of these tools in clinical practice., Competing Interests: Disclosure of Interests The authors declare that they have no competing interest., (Copyright © 2023 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

Purpose: In 2022, the French Society of Radiology together with the French Society of Thoracic Imaging and CentraleSupelec organized their 13th data challenge. The aim was to aid in the diagnosis of pulmonary embolism, by identifying the presence of pulmonary embolism and by estimating the ratio between right and left ventricular (RV/LV) diameters, and an arterial obstruction index (Qanadli's score) using artificial intelligence., Materials and Methods: The data challenge was composed of three tasks: the detection of pulmonary embolism, the RV/LV diameter ratio, and Qanadli's score. Sixteen centers all over France participated in the inclusion of the cases. A health data hosting certified web platform was established to facilitate the inclusion process of the anonymized CT examinations in compliance with general data protection regulation. CT pulmonary angiography images were collected. Each center provided the CT examinations with their annotations. A randomization process was established to pool the scans from different centers. Each team was required to have at least a radiologist, a data scientist, and an engineer. Data were provided in three batches to the teams, two for training and one for evaluation. The evaluation of the results was determined to rank the participants on the three tasks., Results: A total of 1268 CT examinations were collected from the 16 centers following the inclusion criteria. The dataset was split into three batches of 310, 580 and 378 C T examinations provided to the participants respectively on September 5, 2022, October 7, 2022 and October 9, 2022. Seventy percent of the data from each center were used for training, and 30% for the evaluation. Seven teams with a total of 48 participants including data scientists, researchers, radiologists and engineering students were registered for participation. The metrics chosen for evaluation included areas under receiver operating characteristic curves, specificity and sensitivity for the classification task, and the coefficient of determination r 2 for the regression tasks. The winning team achieved an overall score of 0.784., Conclusion: This multicenter study suggests that the use of artificial intelligence for the diagnosis of pulmonary embolism is possible on real data. Moreover, providing quantitative measures is mandatory for the interpretability of the results, and is of great aid to the radiologists especially in emergency settings., Competing Interests: Disclosure of Interests The authors declare that they have no known competing financial or personal relationships that could be viewed as influencing the work reported in this paper., (Copyright © 2023 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

Background: Our aim was to explore the prognostic value of anthropometric parameters in a large population of patients treated with immunotherapy., Methods: We retrospectively included 623 patients with advanced non-small cell lung cancer (NSCLC) (n=318) or melanoma (n=305) treated by an immune-checkpoint-inhibitor having a pretreatment (thorax-)abdomen-pelvis CT scan. An external validation cohort of 55 patients with NSCLC was used. Anthropometric parameters were measured three-dimensionally (3D) by a deep learning software (Anthropometer3DNet) allowing an automatic multislice measurement of lean body mass, fat body mass (FBM), muscle body mass (MBM), visceral fat mass (VFM) and sub-cutaneous fat mass (SFM). Body mass index (BMI) and weight loss (WL) were also retrieved. Receiver operator characteristic (ROC) curve analysis was performed and overall survival was calculated using Kaplan-Meier (KM) curve and Cox regression analysis., Results: In the overall cohort, 1-year mortality rate was 0.496 (95% CI: 0.457 to 0.537) for 309 events and 5-year mortality rate was 0.196 (95% CI: 0.165 to 0.233) for 477 events. In the univariate Kaplan-Meier analysis, prognosis was worse (p<0.001) for patients with low SFM (<3.95 kg/m 2 ), low FBM (<3.26 kg/m 2 ), low VFM (<0.91 kg/m 2 ), low MBM (<5.85 kg/m 2 ) and low BMI (<24.97 kg/m 2 ). The same parameters were significant in the Cox univariate analysis (p<0.001) and, in the multivariate stepwise Cox analysis, the significant parameters were MBM (p<0.0001), SFM (0.013) and WL (0.0003). In subanalyses according to the type of cancer, all body composition parameters were statistically significant for NSCLC in ROC, KM and Cox univariate analysis while, for melanoma, none of them, except MBM, was statistically significant. In multivariate Cox analysis, the significant parameters for NSCLC were MBM (HR=0.81, p=0.0002), SFM (HR=0.94, p=0.02) and WL (HR=1.06, p=0.004). For NSCLC, a KM analysis combining SFM and MBM was able to separate the population in three categories with the worse prognostic for the patients with both low SFM (<5.22 kg/m 2 ) and MBM (<6.86 kg/m 2 ) (p<0001). On the external validation cohort, combination of low SFM and low MBM was pejorative with 63% of mortality at 1 year versus 25% (p=0.0029)., Conclusions: 3D measured low SFM and MBM are significant prognosis factors of NSCLC treated by immune checkpoint inhibitors and can be combined to improve the prognostic value., Competing Interests: Competing interests: No, there are no competing interests., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose: The purpose of this study was to develop a method for generating synthetic MR images of macrotrabecular-massive hepatocellular carcinoma (MTM-HCC)., Materials and Methods: A set of abdominal MR images including fat-saturated T1-weighted images obtained during the arterial and portal venous phases of enhancement and T2-weighted images of 91 patients with MTM-HCC, and another set of MR abdominal images from 67 other patients were used. Synthetic images were obtained using a 3-step pipeline that consisted in: (i), generating a synthetic MTM-HCC tumor on a neutral background; (ii), randomly selecting a background among the 67 patients and a position inside the liver; and (iii), merging the generated tumor in the background at the specified location. Synthetic images were qualitatively evaluated by three radiologists and quantitatively assessed using a mix of 1-nearest neighbor classifier metric and Fréchet inception distance., Results: A set of 1000 triplets of synthetic MTM-HCC images with consistent contrasts were successfully generated. Evaluation of selected synthetic images by three radiologists showed that the method gave realistic, consistent and diversified images. Qualitative and quantitative evaluation led to an overall score of 0.64., Conclusion: This study shows the feasibility of generating realistic synthetic MR images with very few training data, by leveraging the wide availability of liver backgrounds. Further studies are needed to assess the added value of those synthetic images for automatic diagnosis of MTM-HCC., Competing Interests: Declaration of Competing Interest Vincent Couteaux, Cheng Zhang, Caroline Raynaud, Anna-Sesilia Vlachomitrou, Cybele Ciofolo-Veit and Carole Lazarus are employees of Philips Research France. The other authors declare that they have no competing interest., (Copyright © 2023 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

Purpose: The objective of the study is to propose the immunotherapy progression decision (iPD) score, a practical tool based on patient features that are available at the first evaluation of immunotherapy treatment, to help oncologists decide whether to continue the treatment or switch rapidly to another therapeutic line when facing a progressive disease patient at the first evaluation., Experimental Design: This retrospective study included 107 patients with progressive disease at first evaluation according to RECIST 1.1. Clinical, radiological, and biological data at baseline and first evaluation were analyzed. An external validation set consisting of 31 patients with similar baseline characteristics was used for the validation of the score., Results: Variables were analyzed in a univariate study. The iPD score was constructed using only independent variables, each considered as a worsening factor for the survival of patients. The patients were stratified in three groups: good prognosis (GP), poor prognosis (PP), and critical prognosis (CP). Each group showed significantly different survivals (GP: 11.4, PP: 4.4, CP: 2.3 months median overall survival, P < 0.001, log-rank test). Moreover, the iPD score was able to detect the pseudoprogressors better than other scores. On the validation set, CP patients had significantly worse survival than PP and GP patients (P < 0.05, log-rank test)., Conclusions: The iPD score provides oncologists with a new evaluation, computable at first progression, to decide whether treatment should be continued (for the GP group), or immediately changed for the PP and CP groups. Further validation on larger cohorts is needed to prove its efficacy in clinical practice., (©2023 American Association for Cancer Research.)

Background: Body composition could help to better define the prognosis of cancers treated with anti-angiogenics. The aim of this study is to evaluate the prognostic value of 3D and 2D anthropometric parameters in patients given anti-angiogenic treatments. Methods: 526 patients with different types of cancers were retrospectively included. The software Anthropometer3DNet was used to measure automatically fat body mass (FBM3D), muscle body mass (MBM3D), visceral fat mass (VFM3D) and subcutaneous fat mass (SFM3D) in 3D computed tomography. For comparison, equivalent two-dimensional measurements at the L3 level were also measured. The area under the curve (AUC) of the receiver operator characteristics (ROC) was used to determine the parameters’ predictive power and optimal cut-offs. A univariate analysis was performed using Kaplan−Meier on the overall survival (OS). Results: In ROC analysis, all 3D parameters appeared statistically significant: VFM3D (AUC = 0.554, p = 0.02, cutoff = 0.72 kg/m2), SFM3D (AUC = 0.544, p = 0.047, cutoff = 3.05 kg/m2), FBM3D (AUC = 0.550, p = 0.03, cutoff = 4.32 kg/m2) and MBM3D (AUC = 0.565, p = 0.007, cutoff = 5.47 kg/m2), but only one 2D parameter (visceral fat area VFA2D AUC = 0.548, p = 0.034). In log-rank tests, low VFM3D (p = 0.014), low SFM3D (p < 0.0001), low FBM3D (p = 0.00019) and low VFA2D (p = 0.0063) were found as a significant risk factor. Conclusion: automatic and 3D body composition on pre-therapeutic CT is feasible and can improve prognostication in patients treated with anti-angiogenic drugs. Moreover, the 3D measurements appear to be more effective than their 2D counterparts.

Purpose: The 2021 edition of the Artificial Intelligence Data Challenge was organized by the French Society of Radiology together with the Centre National d'Études Spatiales and CentraleSupélec with the aim to implement generative adversarial networks (GANs) techniques to provide 1000 magnetic resonance imaging (MRI) cases of macrotrabecular-massive (MTM) hepatocellular carcinoma (HCC), a rare and aggressive subtype of HCC, generated from a limited number of real cases from multiple French centers., Materials and Methods: A dedicated platform was used by the seven inclusion centers to securely upload their anonymized MRI examinations including all three cross-sectional images (one late arterial and one portal-venous phase T1-weighted images and one fat-saturated T2-weighted image) in compliance with general data protection regulation. The quality of the database was checked by experts and manual delineation of the lesions was performed by the expert radiologists involved in each center. Multidisciplinary teams competed between October 11 th , 2021 and February 13 th , 2022., Results: A total of 91 MTM-HCC datasets of three images each were collected from seven French academic centers. Six teams with a total of 28 individuals participated in this challenge. Each participating team was asked to generate one thousand 3-image cases. The qualitative evaluation was performed by three radiologists using the Likert scale on ten randomly selected cases generated by each participant. A quantitative evaluation was also performed using two metrics, the Frechet inception distance and a leave-one-out accuracy of a 1-Nearest Neighbor algorithm., Conclusion: This data challenge demonstrates the ability of GANs techniques to generate a large number of images from a small sample of imaging examinations of a rare malignant tumor., Competing Interests: Declaration of Competing Interest The authors declare that they have no competing interest., (Copyright © 2022 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

Background: Anti-PD-(L)1 treatment is indicated for patients with mismatch repair-deficient (MMRD) tumors, regardless of tumor origin. However, the response rate is highly heterogeneous across MMRD tumors. The objective of the study is to find a score that predicts anti-PD-(L)1 response in patients with MMRD tumors., Methods: Sixty-one patients with various origin of MMRD tumors and treated with anti-PD-(L)1 were retrospectively included in this study. An expert radiologist annotated all tumors present at the baseline and first evaluation CT-scans for all the patients by circumscribing them on their largest axial axis (single slice), allowing us to compute an approximation of their tumor volume. In total, 2120 lesions were annotated, which led to the computation of the total tumor volume for each patient. The RECIST sum of target lesions' diameters and neutrophile-to-lymphocyte (NLR) were also reported at both examinations. These parameters were determined at baseline and first evaluation and the variation between the first evaluation and baseline was calculated, to determine a comprehensive score for overall survival (OS) and progression-free survival (PFS)., Results: Total tumor volume at baseline was found to be significantly correlated to the OS (p-value: 0.005) and to the PFS (p-value:<0.001). The variation of the RECIST sum of target lesions' diameters, total tumor volume and NLR were found to be significantly associated to the OS (p-values:<0.001, 0.006,<0.001 respectively) and to the PFS (<0.001,<0.001, 0.007 respectively). The concordance score combining total tumor volume and NLR variation was better at stratifying patients compared to the tumor volume or NLR taken individually according to the OS (pairwise log-rank test p-values: 0.033,<0.001, 0.002) and PFS (pairwise log-rank test p-values: 0.041,<0.001, 0.003)., Conclusion: Total tumor volume appears to be a prognostic biomarker of anti-PD-(L)1 response to immunotherapy in metastatic patients with MMRD tumors. Combining tumor volume and NLR with a simple concordance score stratifies patients well according to their survival and offers a good predictive measure of response to immunotherapy., Competing Interests: NC reports grants from Cytune Pharma, grants from BMS, grants from SANOFI, personal fees from AstraZeneca France, outside the submitted work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Belkouchi, Nebot-Bral, Lawrance, Kind, David, Ammari, Cournède, Talbot, Vuagnat, Smolenschi, Kannouche, Chaput, Lassau and Hollebecque.)

Purpose: The objective of our study is to propose fast, cost-effective, convenient, and effective biomarkers using the perfusion parameters from dynamic contrast-enhanced ultrasound (DCE-US) for the evaluation of immune checkpoint inhibitors (ICI) early response., Methods: The retrospective cohort used in this study included 63 patients with metastatic cancer eligible for immunotherapy. DCE-US was performed at baseline, day 8 (D8), and day 21 (D21) after treatment onset. A tumor perfusion curve was modeled on these three dates, and change in the seven perfusion parameters was measured between baseline, D8, and D21. These perfusion parameters were studied to show the impact of their variation on the overall survival (OS)., Results: After the removal of missing or suboptimal DCE-US, the Baseline-D8, the Baseline-D21, and the D8-D21 groups included 37, 53, and 33 patients, respectively. A decrease of more than 45% in the area under the perfusion curve (AUC) between baseline and D21 was significantly associated with better OS ( p = 0.0114). A decrease of any amount in the AUC between D8 and D21 was also significantly associated with better OS ( p = 0.0370)., Conclusion: AUC from DCE-US looks to be a promising new biomarker for fast, effective, and convenient immunotherapy response evaluation.