Blended learning of radiology improves medical students’ performance, satisfaction, and engagement

Purpose To evaluate the impact of blended learning using a combination of educational resources (flipped classroom and short videos) on medical students’ (MSs) for radiology learning. Material and methods A cohort of 353 MSs from 2015 to 2018 was prospectively evaluated. MSs were assigned to four groups (high, high-intermediate, low-intermediate, and low achievers) based on their results to a 20-MCQs performance evaluation referred to as the pretest. MSs had then free access to a self-paced course totalizing 61 videos based on abdominal imaging over a period of 3 months. Performance was evaluated using the change between posttest (the same 20 MCQs as pretest) and pretest results. Satisfaction was measured using a satisfaction survey with directed and spontaneous feedbacks. Engagement was graded according to audience retention and attendance on a web content management system. Results Performance change between pre and posttest was significantly different between the four categories (ANOVA, P = 10−9): low pretest achievers demonstrated the highest improvement (mean ± SD, + 11.3 ± 22.8 points) while high pretest achievers showed a decrease in their posttest score (mean ± SD, − 3.6 ± 19 points). Directed feedback collected from 73.3% of participants showed a 99% of overall satisfaction. Spontaneous feedback showed that the concept of “pleasure in learning” was the most cited advantage, followed by “flexibility.” Engagement increased over years and the number of views increased of 2.47-fold in 2 years. Conclusion Learning formats including new pedagogical concepts as blended learning, and current technologies allow improvement in medical student’s performance, satisfaction, and engagement

Prise de décision individualisée en immuno-oncologie guidée par l'intelligence artificielle et l'analyse du phénotype tumoral en imagerie médicale

Les immunothérapies ciblant les voies du récepteur de la mort cellulaire programmée 1 et de son ligand (anti-PD(L)1) se sont révélées être un traitement efficace pour de nombreux cancers. Le traitement par anti-PD(L)1 constitue un changement de paradigme en oncologie puisque son activité repose sur la restauration d'une réponse efficace des cellules T antitumorales. Deux raisons principales expliquent la nécessité d’identifier des biomarqueurs permettant de prédire la survie et l'efficacité anticancéreuse des anti-PD(L)1. Premièrement, un excès de décès a été observé dans le groupe expérimental d’essais de phase III randomisés comparant les immunothérapies anti-PD(L)1 à la chimiothérapie. Parmi les hypothèses controversées pouvant expliquer cette observation, le manque d'efficacité de l'anti-PD(L)1 chez les patients atteints d'une maladie à croissance rapide (appelés "progresseurs rapides") par rapport à un effet paradoxal de l'accélération de la maladie sous immunothérapie (dits "hyperprogresseurs") sont souvent mentionnés. Deuxièmement, les critères de réponse en imagerie jouent un rôle essentiel dans la prise en charge des patients cancéreux et définissent une "stratégie attentiste" pour les patients avec une maladie évolutive en imagerie. Le mécanisme d’action distinct des anti-PD(L)1, qui restaurent la capacité anti-tumorale du système immunitaire, conduisent à la survenue de profils de réponse non conventionnels tels que la pseudoprogression, l’hyperprogression, l’effet abscopal et les toxicités liées au système immunitaire. Nous avons tiré parti de l’apprentissage automatique pour confronter différents facteurs pronostiques / prédictifs et identifier les biomarqueurs d'imagerie associés à la mort prématurée sous immunothérapie anti-PD(L)1. Nous avons exploité des données transcriptomiques pour déterminer les voies biologiques liées à ces facteurs pronostiques / prédictifs. Nos résultats démontrent qu'un sous-ensemble limité de biomarqueurs d'imagerie peut prévoir la survie globale des patients. La classification de ces biomarqueurs d'imagerie en caractéristiques distinctives fournit une structure conceptuelle et une cohérence logique délimitant les interconnexions entre eux. Ces caractéristiques distinctives peuvent être comprises comme des circuits physiologiques distincts perturbées par le cancer et liés à une survie plus courte : organotropisme hépatique, charge tumorale élevée, hétérogénéité importante dans la vascularisation ou le métabolisme de la tumeur, infiltration le long des bordures de la tumeur, irrégularité de la forme tumorale, forte consommation de glucose, sarcopénie, et métabolisme élevé de la moelle osseuse. En utilisant l’apprentissage automatique, nous avons démontré que l’augmentation de la lactate déshydrogénase sérique et la présence de métastases hépatiques au scanner étaient deux facteurs indépendants de décès prématuré après l’initiation du traitement anti-PD(L)1. L'analyse transcriptomique a identifié des voies de signalisations susceptibles de donner lieu à de nouveaux traitements, et d'améliorer l'efficacité des anti-PD(L)1. Dans une perspective plus large, cela démontre la nécessité de continuer à développer une technologie d'imagerie de pointe pour améliorer la surveillance des patients atteints de cancer traités avec des immunothérapeutiques. Cela implique l'analyse et la liaison des données en pathologie, en oncologie, en radiologie et en médecine nucléaire, ainsi que la capacité de travailler avec de larges ensembles de données. Par conséquent, il est nécessaire de développer des programmes de radiomique pour développer des outils prédictifs utiles au diagnostic, à l'évaluation et à la gestion de tous les types de patients cancéreux. En conclusion, les approches de médecine de précision axées sur la radiomique pourraient améliorer la vie des patients cancéreux traités par immunothérapie anticancéreuse.Immunotherapies targeting the programmed cell death receptor-1 and ligand-1 pathways (anti-PD(L)1) have emerged as an effective treatment for a variety of cancers. Anti-PD(L)1 is a paradigm shift in the treatment of cancers since its activity relies on restoring an efficient anti-tumor T-cell response. Two main reasons explain the need to investigate biomarkers forecasting survival and predicting the anti-cancer efficacy of anti-PD(L)1. First, an excess of death has been observed in the experimental arm of randomized phase III trials comparing anti-PD(L)1 immunotherapies to chemotherapy for multiple cancers. Among the controversial hypotheses that would explain this observation are frequently mentioned the lack of effectiveness of anti-PD(L)1 in patients with a fast-growing disease (so-called "fast progressors") vs. a paradoxical effect of disease acceleration under immunotherapy (so-called "hyperprogressors"). Second, imaging response criteria play a pivotal role in guiding cancer patient management and define a "wait and see strategy" for patients treated with anti-PD(L)1 in monotherapy with progressive disease. The distinct mechanisms of anti-PD(L)1, which restore the immune system's anti-tumor capacity, leads to unconventional immune-related phenomena. From a medical imaging standpoint, it translates into pseudoprogression, hyperprogression, abscopal effect, and immune-related adverse events. We leveraged machine learning approaches to challenge the prognostic/predictive factors and identify which imaging biomarkers are associated with early death upon anti-PD(L)1 immunotherapy. We mined transcriptomic data to determine the biological pathways related to these prognostic/predictive factors. Our results demonstrate that a limited subset of imaging biomarkers can forecast overall survival. The classification of these imaging biomarkers into distinct hallmarks provides a conceptual structure and logical coherence delineating the interconnections between them. These hallmarks can be understood as distinct physiological circuits disrupted by cancer that are linked to shorter survival: liver organotropism, high tumor burden, high heterogeneity in tumor vascularity or metabolism, infiltration along tumor boundaries, irregularity in tumor shape, high glucose consumption, sarcopenia, and high bone marrow metabolism. Using machine-learning, we demonstrated that increased baseline serum lactate dehydrogenase and the presence of liver metastasis on CT-scan are two independent drivers of premature death after anti-PD(L)1 initiation. Transcriptomic analysis identified actionable pathways amenable to novel treatments, which could improve anti-PD(L)1 efficacy. From a broader perspective, this demonstrates the need to continue to develop advanced imaging technology to enhance the monitoring of cancer patients treated with immunotherapeutics. This involves analyzing and linking data in pathology, oncology, and radiology, and the ability to work with extensive datasets. Therefore, there is a need to develop comprehensive programs of radiomics for predictive tools that benefit diagnosis, assessment, and management of all types of cancer patients. In conclusion, radiomics driven precision medicine approaches could improve the lives of cancer patients treated with cancer immunotherapy

Future role of [18F]-FDG PET/CT in patients with bladder cancer in the new era of neoadjuvant immunotherapy?

International audienceComment on: [18F]Fluoro-Deoxy-Glucose positron emission tomography to evaluate lymph node involvement in patients with muscle-invasive bladder cancer receiving neoadjuvant pembrolizumab. Marandino L, Capozza A, Bandini M, Raggi D, Farè E, Pederzoli F, Gallina A, Capitanio U, Bianchi M, Gandaglia G, Fossati N, Colecchia M, Giannatempo P, Serafini G, Padovano B, Salonia A, Briganti A, Montorsi F, Alessi A, Necchi A. Urol Oncol. 2021 Apr;39(4):235.e15-235.e21. doi: 10.1016/j.urolonc.2020.09.035. Epub 2020 Oct 16. PMID: 3307110

Utility of Routine Preoperative F-18-Fluorodeoxyglucose Positron Emission Tomography/Computerized Tomography in Identifying Pathological Lymph Node Metastases at Radical Cystectomy. Letter

International audienc

Advances in PET/CT Imaging for Breast Cancer Patients and Beyond

Breast cancer is the most common cancer in women around the world and the fifth leading cause of cancer-related death [...

Current and Future Role of Medical Imaging in Guiding the Management of Patients With Relapsed and Refractory Non-Hodgkin Lymphoma Treated With CAR T-Cell Therapy

International audienceChimeric antigen receptor (CAR) T-cells are a novel immunotherapy available for patients with refractory/relapsed non-Hodgkin lymphoma. In this indication, clinical trials have demonstrated that CAR T-cells achieve high rates of response, complete response, and long-term response (up to 80%, 60%, and 40%, respectively). Nonetheless, the majority of patients ultimately relapsed. This review provides an overview about the current and future role of medical imaging in guiding the management of non-Hodgkin lymphoma patients treated with CAR T-cells. It discusses the value of predictive and prognostic biomarkers to better stratify the risk of relapse, and provide a patient-tailored therapeutic strategy. At baseline, high tumor volume (assessed on CT-scan or on [18F]-FDG PET/CT) is a prognostic factor associated with treatment failure. Response assessment has not been studied extensively yet. Available data suggests that current response assessment developed on CT-scan or on [18F]-FDG PET/CT for cytotoxic systemic therapies remains relevant to estimate lymphoma response to CAR T-cell therapy. Nonetheless, atypical patterns of response and progression have been observed and should be further analyzed. The potential advantages as well as limitations of artificial intelligence and radiomics as tools providing high throughput quantitative imaging features is described

Efficacy of anti-PD1 re-treatment in patients with Hodgkin lymphoma who relapsed after anti-PD1 discontinuation

International audiencePatients with relapsed/refractory Hodgkin lymphoma (R/R HL) experience high response rates upon anti-PD1 therapy. In these patients, there is limited data about the optimal duration of treatment and the risk of relapse after anti-PD1 discontinuation. We have previously reported the outcome of 11 patients with R/R HL who discontinued anti-PD1 therapy after achieving a complete response (CR) upon nivolumab1 . These patients experienced favorable outcome as only 2 of them had relapsed after a median follow-up of 21.2 months from discontinuation. Despite the low relapse rate observed in that study, physicians may be worried about the possibility to further rescue these heavily pre-treated patients in case of relapse after anti-PD1 discontinuation. Notably, it is still unknown whether these patients will remain sensitive to a 2nd course of anti-PD1

Radiomics Response Signature for Identification of Metastatic Colorectal Cancer Sensitive to Therapies Targeting EGFR Pathway.

BACKGROUND: The authors sought to forecast survival and enhance treatment decisions for patients with liver metastatic colorectal cancer by using on-treatment radiomics signature to predict tumor sensitiveness to irinotecan, 5-fluorouracil, and leucovorin (FOLFIRI) alone (F) or in combination with cetuximab (FC). METHODS: We retrospectively analyzed 667 metastatic colorectal cancer patients treated with F or FC. Computed tomography quality was classified as high (HQ) or standard (SD). Four datasets were created using the nomenclature (treatment) - (quality). Patients were randomly assigned (2:1) to training or validation sets: FCHQ: 78:38, FCSD: 124:62, FHQ: 78:51, FSD: 158:78. Four tumor-imaging biomarkers measured quantitative radiomics changes between standard of care computed tomography scans at baseline and 8 weeks. Using machine learning, the performance of the signature to classify tumors as treatment sensitive or treatment insensitive was trained and validated using receiver operating characteristic (ROC) curves. Hazard ratio and Cox regression models evaluated association with overall survival (OS). RESULTS: The signature (area under the ROC curve [95% confidence interval (CI)]) used temporal decrease in tumor spatial heterogeneity plus boundary infiltration to successfully predict sensitivity to antiepidermal growth factor receptor therapy (FCHQ: 0.80 [95% CI = 0.69 to 0.94], FCSD: 0.72 [95% CI = 0.59 to 0.83]) but failed with chemotherapy (FHQ: 0.59 [95% CI = 0.44 to 0.72], FSD: 0.55 [95% CI = 0.43 to 0.66]). In cetuximab-containing sets, radiomics signature outperformed existing biomarkers (KRAS-mutational status, and tumor shrinkage by RECIST 1.1) for detection of treatment sensitivity and was strongly associated with OS (two-sided P < .005). CONCLUSIONS: Radiomics response signature can serve as an intermediate surrogate marker of OS. The signature outperformed known biomarkers in providing an early prediction of treatment sensitivity and could be used to guide cetuximab treatment continuation decisions

Machine learning defined diagnostic criteria for differentiating pituitary metastasis from autoimmune hypophysitis in patients undergoing immune checkpoint blockade therapy

International audiencePurpose : New-onset pituitary gland lesions are observed in up to 18% of cancer patients undergoing treatment with immune checkpoint blockers (ICB). We aimed to develop and validate an imaging-based decision-making algorithm for use by the clinician that helps differentiate pituitary metastasis (PM) from ICB-induced autoimmune hypophysitis (HP). Materials and methods : A systematic search was performed in the MEDLINE and EMBASE databases up to October 2018 to identify studies concerning PM and HP in patients treated with cytotoxic T–lymphocyte–associated protein 4 and programmed cell death (ligand) 1. The reference standard for diagnosis was confirmation by histology or response on follow-up imaging. Patients from included studies were randomly assigned to the training set or the validation set. Using machine learning (random forest tree algorithm) with the most-described six imaging and three clinical features, a multivariable prediction model (the signature) was developed and validated for diagnosing PM. Signature performance was evaluated using area under a receiver operating characteristic curves (AUCs).Results : Out of 3174 screened articles, 65 were included totalising 122 patients (HP: 60 pts, PM: 62 pts). Complete radiological data were available in 82 pts (Training: 62 pts, Validation: 20 pts). The signature reached an AUC = 0.91 (0.82, 1.00), P < 10−8 in the training set and AUC = 0.94 (0.80, 1.00), P = 0.001 in the validation set. The signature predicted PM in lesions either ≥ 2 cm in size or < 2 cm if associated with heterogeneous contrast enhancement and cavernous extension.Conclusion : An image-based signature was developed with machine learning and validated for differentiating PM from HP. This tool could be used by clinicians for enhanced decision-making in cancer patients undergoing ICB treatment with new-onset, concerning lesions of the pituitary gland

Radiomics in Nuclear Medicine Applied to Radiation Therapy: Methods, Pitfalls, and Challenges

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