Differentiation between adrenocortical carcinoma and lipid-poor adrenal adenoma using a multiparametric MRI-based diagnostic algorithm.

PURPOSE: The purpose of this study was to evaluate the capabilities of multiparametric magnetic resonance imaging (MRI) in differentiating between lipid-poor adrenal adenoma (LPAA) and adrenocortical carcinoma (ACC). MATERIALS AND METHODS: Patients of two centers who underwent surgical resection of LPAA or ACC after multiparametric MRI were retrospectively included. A training cohort was used to build a diagnostic algorithm obtained through recursive partitioning based on multiparametric MRI variables, including apparent diffusion coefficient and chemical shift signal ratio (i.e., tumor signal intensity index). The diagnostic performances of the multiparametric MRI-based algorithm were evaluated using a validation cohort, alone first and then in association with adrenal tumor size using a cut-off of 4 cm. Performances of the diagnostic algorithm for the diagnosis of ACC vs. LPAA were calculated using pathology as the reference standard. RESULTS: Fifty-four patients (27 with LPAA and 27 with ACC; 37 women; mean age, 48.5 ± 13.3 [standard deviation (SD)] years) were used as the training cohort and 61 patients (24 with LPAA and 37 with ACC; 47 women; mean age, 49 ± 11.7 [SD] years) were used as the validation cohort. In the validation cohort, the diagnostic algorithm yielded best accuracy for the diagnosis of ACC vs. LPAA (75%; 46/61; 95% CI: 55-88) when used without lesion size. Best sensitivity was obtained with the association of the diagnostic algorithm with tumor size (96%; 23/24; 95% CI: 80-99). Best specificity was obtained with the diagnostic algorithm used alone (76%; 28/37; 95% CI: 60-87). CONCLUSION: A multiparametric MRI-based diagnostic algorithm that includes apparent diffusion coefficient and tumor signal intensity index helps discriminate between ACC and LPAA with high degrees of specificity and accuracy. The association of the multiparametric MRI-based diagnostic algorithm with adrenal lesion size helps maximize the sensitivity of multiparametric MRI for the diagnosis of ACC.

Stomach size in anorexia nervosa: A new challenge?

BACKGROUND & AIMS: Changes in stomach size may impact eating behaviour. A recent study showed gastric dilatation in restrictive eating disorders using computed tomography scans. This study aimed to describe stomach size in the standing position in women with anorexia nervosa (AN). METHODS: Women treated for AN at our institution were retrospectively included if they had undergone upper gastrointestinal radiography (UGR) after the diagnosis of AN. Two control groups (CG1 and CG2) were included, both comprising female patients: CG1 patients were not obese and underwent UGR for digestive symptoms of other aetiologies, and CG2 comprised obese individuals who had UGR before bariatric surgery. A UGR-based Stomach Size Index (SSI), calculated as the ratio of the length of the stomach to the distance between the upper end of the stomach and the top of the iliac crests, was measured in all three groups. Gastromegaly was defined as SSI >1.00. RESULTS: 45 patients suffering from AN (28 with restrictive and 17 with binge/purge subtype), 10 CG1 and 20 CG2 subjects were included in this study. Stomach Size Index was significantly higher in AN (1.27 ± 0.24) than in CG1 (0.80 ± 0.11) and CG2 (0.68 ± 0.09); p < 0.001, but was not significantly different between patients with the restrictive and binge/purge subtypes. Gastromegaly was present in 82.2% of patients with AN and not present in the control groups. In patients with AN, gastromegaly was present in 12/15 patients without digestive symptoms (80.0%) and in 25/30 patients with digestive complaints (83.3%) at time of UGR (p = 0.99). In the AN group, no significant relationship was found between SSI and body mass index. CONCLUSION: Gastromegaly is frequent in AN and could influence AN recovery. This anatomical modification could partially explain the alterations of gastric motility previously reported in AN.

CT and MRI of Gastrointestinal Stromal Tumors: New Trends and Perspectives.

Gastrointestinal stromal tumors (GISTs) are defined as mesenchymal tumors of the gastrointestinal tract that express positivity for CD117, which is a c-KIT proto-oncogene antigen. Expression of the c-KIT protein, a tyrosine kinase growth factor receptor, allows the distinction between GISTs and other mesenchymal tumors such as leiomyoma, leiomyosarcoma, schwannoma and neurofibroma. GISTs can develop anywhere in the gastrointestinal tract, as well as in the mesentery and omentum. Over the years, the management of GISTs has improved due to a better knowledge of their behaviors and risk or recurrence, the identification of specific mutations and the use of targeted therapies. This has resulted in a better prognosis for patients with GISTs. In parallel, imaging of GISTs has been revolutionized by tremendous progress in the field of detection, characterization, survival prediction and monitoring during therapy. Recently, a particular attention has been given to radiomics for the characterization of GISTs using analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence with the aim of better characterizing GISTs and providing a more precise assessment of tumor burden. This article sums up recent advances in computed tomography and magnetic resonance imaging of GISTs in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning.

CT and MRI of abdominal cancers: current trends and perspectives in the era of radiomics and artificial intelligence.

Abdominal cancers continue to pose daily challenges to clinicians, radiologists and researchers. These challenges are faced at each stage of abdominal cancer management, including early detection, accurate characterization, precise assessment of tumor spread, preoperative planning when surgery is anticipated, prediction of tumor aggressiveness, response to therapy, and detection of recurrence. Technical advances in medical imaging, often in combination with imaging biomarkers, show great promise in addressing such challenges. Information extracted from imaging datasets owing to the application of radiomics can be used to further improve the diagnostic capabilities of imaging. However, the analysis of the huge amount of data provided by these advances is a difficult task in daily practice. Artificial intelligence has the potential to help radiologists in all these challenges. Notably, the applications of AI in the field of abdominal cancers are expanding and now include diverse approaches for cancer detection, diagnosis and classification, genomics and detection of genetic alterations, analysis of tumor microenvironment, identification of predictive biomarkers and follow-up. However, AI currently has some limitations that need further refinement for implementation in the clinical setting. This review article sums up recent advances in imaging of abdominal cancers in the field of image/data acquisition, tumor detection, tumor characterization, prognosis, and treatment response evaluation.

Three-Dimensional Measurement of the Uterus on Magnetic Resonance Images: Development and Performance Analysis of an Automated Deep-Learning Tool.

Uterus measurements are useful for assessing both the treatment and follow-ups of gynaecological patients. The aim of our study was to develop a deep learning (DL) tool for fully automated measurement of the three-dimensional size of the uterus on magnetic resonance imaging (MRI). In this single-centre retrospective study, 900 cases were included to train, validate, and test a VGG-16/VGG-11 convolutional neural network (CNN). The ground truth was manual measurement. The performance of the model was evaluated using the objective key point similarity (OKS), the mean difference in millimetres, and coefficient of determination R2. The OKS of our model was 0.92 (validation) and 0.96 (test). The average deviation and R2 coefficient between the AI measurements and the manual ones were, respectively, 3.9 mm and 0.93 for two-point length, 3.7 mm and 0.94 for three-point length, 2.6 mm and 0.93 for width, 4.2 mm and 0.75 for thickness. The inter-radiologist variability was 1.4 mm. A three-dimensional automated measurement was obtained in 1.6 s. In conclusion, our model was able to locate the uterus on MRIs and place measurement points on it to obtain its three-dimensional measurement with a very good correlation compared to manual measurements.

Pretreatment CT Texture Parameters as Predictive Biomarkers of Progression-Free Survival in Follicular Lymphoma Treated with Immunochemotherapy and Rituximab Maintenance.

The purpose of this study was to determine whether texture analysis features present on pretreatment unenhanced computed tomography (CT) images, derived from 18F-fluorodeoxyglucose positron emission/computed tomography (18-FDG PET/CT), can predict progression-free survival (PFS), progression-free survival at 24 months (PFS 24), time to next treatment (TTNT), and overall survival in patients with high-tumor-burden follicular lymphoma treated with immunochemotherapy and rituximab maintenance. Seventy-two patients with follicular lymphoma were retrospectively included. Texture analysis was performed on unenhanced CT images extracted from 18-FDG PET/CT examinations that were obtained within one month before treatment. Skewness at a fine texture scale (SSF = 2) was an independent predictor of PFS (hazard ratio = 3.72 (95% CI: 1.15, 12.11), p = 0.028), PFS 24 (hazard ratio = 13.38; 95% CI: 1.29, 138.13; p = 0.029), and TTNT (hazard ratio = 5.11; 95% CI: 1.18, 22.13; p = 0.029). Skewness values above -0.015 at SSF = 2 were significantly associated with lower PFS, PFS 24, and TTNT. Kurtosis without filtration was an independent predictor of PFS (SSF = 0; HR = 1.22 (95% CI: 1.04, 1.44), p = 0.013), and TTNT (SSF = 0; hazard ratio = 1.23; 95% CI: 1.04, 1.46; p = 0.013). This study shows that pretreatment unenhanced CT texture analysis-derived tumor skewness and kurtosis may be used as predictive biomarkers of PFS and TTNT in patients with high-tumor-burden follicular lymphoma treated with immunochemotherapy and rituximab maintenance.

MRI restaging of rectal cancer: The RAC (Response-Anal canal-CRM) analysis joint consensus guidelines of the GRERCAR and GRECCAR groups.

PURPOSE: To develop guidelines by international experts to standardize data acquisition, image interpretation, and reporting in rectal cancer restaging with magnetic resonance imaging (MRI). MATERIALS AND METHODS: Evidence-based data and experts' opinions were combined using the RAND-UCLA Appropriateness Method to attain consensus guidelines. Experts provided recommendations for reporting template and protocol for data acquisition were collected; responses were analysed and classified as "RECOMMENDED" versus "NOT RECOMMENDED" (if ≥ 80% consensus among experts) or uncertain (if < 80% consensus among experts). RESULTS: Consensus regarding patient preparation, MRI sequences, staging and reporting was attained using the RAND-UCLA Appropriateness Method. A consensus was reached for each reporting template item among the experts. Tailored MRI protocol and standardized report were proposed. CONCLUSION: These consensus recommendations should be used as a guide for rectal cancer restaging with MRI.

Synthetic MR image generation of macrotrabecular-massive hepatocellular carcinoma using generative adversarial networks.

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.

The study of predictive factors for the evolution of vestibular schwannomas.

PURPOSE: The primary objective was to determine whether the analysis of textural heterogeneity of vestibular schwannomas on MRI at diagnosis was predictive of their radiological evolutivity. The secondary objective was to determine whether some clinical or radiological factors could also be predictive of growth. METHODS: We conducted a pilot, observational and retrospective study of patients with a vestibular schwannoma, initially monitored, between April 2001 and November 2019 within the Oto-Neurosurgical Institute of Champagne Ardenne, Texture analysis was performed on gadolinium injected T1 and CISS T2 MRI sequences and six parameters were extracted: mean greyscale intensity, standard deviation of the greyscale histogram distribution, entropy, mean positive pixels, skewness and kurtosis, which were analysed by the Lasso method, using statistically penalised Cox models. Extrameatal location, tumour necrosis, perceived hearing loss < 2 years with objectified tone audiometry asymmetry, tinnitus at diagnosis, were investigated by the Log-Rank test to obtain univariate survival analyses. RESULTS: 78 patients were included and divided into 2 groups: group A comprising 39 "stable patients", and B comprising the remaining 39 "progressive patients". Independent analysis of the texture factors did not predict the growth potential of vestibular schwannomas. Among the clinical or radiological signs of interest, hearing loss < 2 years was identified as a prognostic factor for tumour progression with a significant trend (p = 0.05). CONCLUSIONS: This study did not identify an association between texture analysis and vestibular schwannomas growth. Decreased hearing in the 2 years prior to diagnosis appears to predict potential radiological progression.

Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances.

Pancreatic ductal carcinoma (PDAC) is one of the leading causes of cancer-related death worldwide. Computed tomography (CT) remains the primary imaging modality for diagnosis of PDAC. However, CT has limitations for early pancreatic tumor detection and tumor characterization so that it is currently challenged by magnetic resonance imaging. More recently, a particular attention has been given to radiomics for the characterization of pancreatic lesions using extraction and analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence (AI) with the aim of better characterizing pancreatic lesions and providing a more precise assessment of tumor burden. This review article sums up recent advances in imaging of PDAC in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning. In addition, current applications of radiomics and AI in the field of PDAC are discussed.

Generative adversarial networks (GAN)-based data augmentation of rare liver cancers: The SFR 2021 Artificial Intelligence Data Challenge.

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 11th, 2021 and February 13th, 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.

Artificial intelligence in adrenal imaging: A critical review of current applications.

In the elective field of adrenal imaging, artificial intelligence (AI) can be used for adrenal lesion detection, characterization, hypersecreting syndrome management and patient follow-up. Although a perfect AI tool that includes all required steps from detection to analysis does not exist yet, multiple AI algorithms have been developed and tested with encouraging results. However, AI in this setting is still at an early stage. In this regard, most published studies about AI in adrenal gland imaging report preliminary results that do not have yet daily applications in clinical practice. In this review, recent developments and current results of AI in the field of adrenal imaging are presented. Limitations and future perspectives of AI are discussed.

Interobserver Variability and Accuracy of Preoperative CT and MRI in Pancreatic Ductal Adenocarcinoma Size Estimation: A Retrospective Cohort Study.

Purpose: To assess interobserver variability and accuracy of preoperative computed tomography (CT) and magnetic resonance imaging (MRI) in pancreatic ductal adenocarcinoma (PDAC) size estimation using surgical specimens as standard of reference. Methods: Patients with PDAC who underwent preoperative CT and MRI examinations before surgery were included. PDAC largest axial dimension was measured by 2 readers on 8 MRI sequence and 2 CT imaging phases (pancreatic parenchymal and portal venous). Measurements were compared to actual tumour size at pathologic examination. Interobserver variability was assessed using intraclass correlation coefficients (ICC) and Bland-Altman plots. Differences in tumour size (Δdiameter) between imaging and actual tumour size were searched using Wilcoxon rank sum test. Results: Twenty-nine patients (16 men; median age, 70 years) with surgically resected PDAC were included. Interobserver reproducibility was good to excellent for all MRI sequences and the 2 CT imaging phases with ICCs between .862 (95%CI: .692-.942) for fat-saturated in-phase T1-weighted sequence and .955 (95%CI: .898-.980) for portal venous phase CT images. Best accuracy in PDAC size measurement was obtained with pancreatic parenchymal phase CT images with median Δdiameters of -2 mm for both readers, mean relative differences of -9% and -6% and no significant differences with dimensions at histopathological analysis (P = .051). All MRI sequences led to significant underestimation of PDAC size (median Δdiameters, -6 to -1 mm; mean relative differences, -21% to -11%). Conclusions: Most accurate measurement of PDAC size is obtained with CT images obtained during the pancreatic parenchymal phase. MRI results in significant underestimation of PDAC size.

Pregnancy in a rudimentary horn: multicenter's MRI features of a rare condition.

PURPOSE: To describe the MRI features of rudimentary horn pregnancy (RHP) with surgical correlations. METHODS: Nine women with a RHP underwent preoperative pelvic MRI. MRI protocol included T2- (n = 9), T1- (n = 7), and fat-suppressed contrast-enhanced T1-weighted sequences (n = 4). Two pelvic radiologists retrospectively analyzed MR images to assess the following MRI features: presence of a myometrium around the gestational sac (GS) and characteristics of its wall, GS surrounded by myometrium in contact with the round ligament, communication of the GS with the endometrial cavity of the main horn, continuity of the GS with the cervix, fibrous or muscular GS attachment to the main horn, lateral deviation, and endometrial thickness of the main horn. Ovaries and tubes were also assessed. MRI features were correlated with surgical findings. RESULTS: Seven of the nine women [29 ± 6 SD years (range 16-37 years)] underwent surgical management. The first US diagnosed RHP in only 1/9 patients. All pregnancies were diagnosed using MRI. RHP was all located in the rudimentary horn of a unicornuate uterus. All the GS was surrounded by myometrium in contact with the round ligament. None of the RHP displayed communication with the endometrial cavity of the main horn nor with the cervix. An attachment between the RHP and the main horn was seen in 3/9 patients. All the main horns were lateralized and empty. CONCLUSION: MRI diagnosed RHP in all patients by identifying the GS surrounded by myometrium in contact with the round ligament and the absence of continuity between the GS and the cervix. LEVEL OF EVIDENCE: IV-retrospective study.

Cerebellum/liver index in pretherapeutic 18F-FDG PET/CT as a predictive marker of progression-free survival in follicular lymphoma treated by immunochemotherapy and rituximab maintenance.

ABSTRACT: The purpose of this study was to investigate the value of the "cerebellum/ liver index for prognosis" (CLIP) as a new prognostic marker in pretherapeutic 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) in patients with follicular lymphoma treated by immunochemotherapy and rituximab maintenance, focusing on progression-free survival (PFS).Clinicobiological and imaging data from patients with follicular lymphoma between March 2010 and September 2015 were retrospectively collected and 5-year PFS was determined. The conventional PET parameters (maximum standardized uptake value and total metabolic tumor volume) and the CLIP, corresponding to the ratio of the cerebellum maximum standardized uptake value over the liver SUVmean, were extracted from the pretherapeutic 18F-FDG PET.Forty-six patients were included. Eighteen patients (39%) progressed within the 5 years after treatment initiation. Five-year PFS was 78.6% when CLIP was >4.0 and 42.0% when CLIP was <4.0 (P = .04). CLIP was a significant predictor of PFS on univariate analysis (hazard ratio 3.1, P = .049) and was near-significant on multivariate analysis (hazard ratio 2.8, P = .07) with ECOG PS as a cofactor.The CLIP derived from pretherapeutic 18F-FDG PET seems to be an interesting predictive marker of PFS in follicular lymphoma treated by immunochemotherapy and rituximab maintenance. These results should be evaluated prospectively in a larger cohort.

Adrenal Mass Characterization in the Era of Quantitative Imaging: State of the Art.

Detection and characterization of adrenal lesions have evolved during the past two decades. Although the role of imaging in adrenal lesions associated with hormonal secretion is usually straightforward, characterization of non-functioning adrenal lesions may be challenging to confidently identify those that need to be resected. Although many adrenal lesions can be readily diagnosed when they display typical imaging features, the diagnosis may be challenging for atypical lesions. Computed tomography (CT) remains the cornerstone of adrenal imaging, but other morphological or functional modalities can be used in combination to reach a diagnosis and avoid useless biopsy or surgery. Early- and delayed-phase contrast-enhanced CT images are essential for diagnosing lipid-poor adenoma. Ongoing studies are evaluating the capabilities of dual-energy CT to provide valid virtual non-contrast attenuation and iodine density measurements from contrast-enhanced examinations. Adrenal lesions with attenuation values between 10 and 30 Hounsfield units (HU) on unenhanced CT can be characterized by MRI when iodinated contrast material injection cannot be performed. 18F-FDG PET/CT helps differentiate between atypical benign and malignant adrenal lesions, with the adrenal-to-liver maximum standardized uptake value ratio being the most discriminative variable. Recent studies evaluating the capabilities of radiomics and artificial intelligence have shown encouraging results.

Differentiation between heterogeneous adrenal adenoma and non-adenoma adrenal lesion with CT and MRI.

PURPOSE: To assess whether heterogeneous adrenal adenomas can be distinguished from heterogeneous non-adenomas with Computed Tomography (CT) and/or Magnetic Resonance Imaging (MRI). METHOD: From 2009 to 2019, 980 consecutive adrenalectomies were retrospectively identified. Patients without adequate CT/MRI, with homogeneous and/or < 1 cm lesions were excluded. Differences between adenomas and non-adenomas were analyzed using Chi-square, Student t or Fischer tests, and interobserver agreement using weighted kappa test or intraclass correlation coefficient. Independent variables associated with adenomas were searched for using multivariable analysis. Area under the receiver operating characteristic curve (AUC) of the final model and its diagnostic performances were calculated. RESULTS: Final population comprised 183 patients (106 women, 77 men, mean age 53.2 ± 14.4 years) with 124 non-adenomas and 59 heterogeneous adenomas. Macroscopic or microscopic fat on CT/MRI allowed diagnosis of adenoma with 98% specificity and 63% sensitivity. Interobserver agreement was almost perfect for macroscopic fat (k = 0.82; 95% CI 0.66; 0.94) and substantial for microscopic fat (k = 0.75; 95% CI 0.62; 0.86). A multivariable model including micro- or macroscopic fat [Odds ratio (OR) 81.19; 95% CI 20.17; 572.27], diameter < 5.5 cm (OR 7.32; 95% CI 2.17; 31.28), calcifications (OR 5.68; 95% CI 2.08; 16.18), and hemorrhage (OR 3.10; 95% CI 0.70; 15.35) had an AUC of 0.91 (95% CI 0.86; 0.96), 71% (42/59, 95% CI 58; 82) sensitivity, 93% (115/124; 95% CI 87; 97) specificity, and 86% (157/183; 95% CI 79; 90) accuracy for the diagnosis of adenoma. CONCLUSION: A multivariable model enables CT/MR diagnosis of heterogeneous adenomas. Presence of microscopic fat, even if partial, in a heterogeneous mass is highly specific of adenoma.

CT texture analysis as a predictor of favorable response to anti-PD1 monoclonal antibodies in metastatic skin melanoma.

PURPOSE: The purpose of this study was to determine whether texture analysis features on pretreatment contrast-enhanced computed tomography (CT) images and their evolution can predict treatment response of metastatic skin melanoma (SM) treated with anti-PD1 monoclonal antibodies. MATERIALS AND METHODS: Sixty patients (29 men, 31 women; median age, 56 years; age range: 27-91 years) with metastatic SM treated with pembrolizumab (43/60; 72%) or nivolumab (17/60; 28%) were included. Texture analysis of SM metastases was performed on baseline and first post-treatment evaluation CT examinations. Mean gray-level, entropy, kurtosis, skewness, and standard deviation values were derived from the pixel distribution histogram before and after spatial filtration at different anatomic scales, ranging from fine to coarse. Lasso penalized Cox regression analyses were performed to identify independent variables associated with favorable response to treatment. RESULTS: A total of 127 metastases were analyzed, with a median of two metastases per patient. Skewness at fine texture scale (spatial scale filtration [SSF] = 2; Hazard ratio [HR]: 3.51; 95% CI: 2.08-8.57; P = 0.010), skewness at medium texture scale (SSF = 3; HR: 0.56; 95% CI: 0.11-1.59; P = 0.014), variation of entropy at fine texture scale (SSF = 2; HR: 37.76; 95% CI: 3.48-496.22; P = 0.008) and LDH above the threshold of 248 UI/L (HR: 3.56; 95% CI: 1.78-21.35; P = 0.032] were independent predictors of response to treatment. CONCLUSION: Pretreatment CT texture analysis-derived tumor skewness and variation of entropy between baseline and first control CT examination may be used as predictors of favorable response to anti-PD1 monoclonal antibodies in patients with metastatic SM.

Structured and shared MRI staging lexicon and report of rectal cancer: A consensus proposal by the French Radiology Group (GRERCAR) and Surgical Group (GRECCAR) for rectal cancer.

PURPOSE: To develop French guidelines by experts to standardize data acquisition, image interpretation, and reporting in rectal cancer staging with magnetic resonance imaging (MRI). MATERIALS AND METHODS: Evidence-based data and opinions of experts of GRERCAR (Groupe de REcherche en Radiologie sur le CAncer du Rectum [i.e., Rectal Cancer Imaging Research Group]) and GRECCAR (Groupe de REcherche en Chirurgie sur le CAncer du Rectum [i.e., Rectal Cancer Surgery Research Group]) were combined using the RAND-UCLA Appropriateness Method to attain consensus guidelines. Experts scoring of reporting template and protocol for data acquisition were collected; responses were analyzed and classified as "Recommended" versus "Not recommended" (when ≥ 80% consensus among experts) or uncertain (when < 80% consensus among experts). RESULTS: Consensus regarding patient preparation, MRI sequences, staging and reporting was attained using the RAND-UCLA Appropriateness Method. A consensus was reached for each reporting template item among the experts. Tailored MRI protocol and standardized report were proposed. CONCLUSION: These consensus recommendations should be used as a guide for rectal cancer staging with MRI.