Response heterogeneity as a new biomarker of treatment response in patients with neuroendocrine tumors.

Aim: The RAISE project aimed to find a surrogate end point to predict treatment response early in patients with enteropancreatic neuroendocrine tumors (NET). Response heterogeneity, defined as the coexistence of responding and non-responding lesions, has been proposed as a predictive marker for progression-free survival (PFS) in patients with NETs. Patients & methods: Computerized tomography scans were analyzed from patients with multiple lesions in CLARINET (NCT00353496; n = 148/204). Cox regression analyses evaluated association between response heterogeneity, estimated using the standard deviation of the longest diameter ratio of target lesions, and NET progression. Results: Greater response heterogeneity at a given visit was associated with earlier progression thereafter: week 12 hazard ratio (HR; 95% confidence interval): 1.48 (1.20-1.82); p < 0.001; n = 148; week 36: 1.72 (1.32-2.24); p < 0.001; n = 108. HRs controlled for sum of longest diameter ratio: week 12: 1.28 (1.04-1.59); p = 0.020 and week 36: 1.81 (1.20-2.72); p = 0.005. Conclusion: Response heterogeneity independently predicts PFS in patients with enteropancreatic NETs. Further validation is required.

Neuroendocrine tumors (NET) are rare, slow-growing cancers that can grow in various parts of the body. By understanding how NETs are responding to treatment, doctors can choose the best treatment for a patient and monitor whether the treatment needs to be changed. Treatment response is determined using 'Response Evaluation Criteria in Solid Tumors (RECIST)': a technique which measures the size of tumors to assess whether they are shrinking. However, RECIST is not always useful in NETs, which grow slowly and rarely shrink. 'Response heterogeneity' describes the situation in which some tumors respond well to treatment, while other tumors in the same patient do not. Response heterogeneity may be important in understanding how tumors are responding to treatment and predicting outcomes for patients. Until now, the link between response heterogeneity and treatment response has not been studied in patients with NETs. The RAISE project examined data from a clinical trial of patients with NETs treated with lanreotide. In RAISE, response heterogeneity was estimated using imaging scans of NETs. Response heterogeneity was compared with factors such as tumor size and amounts of certain molecules found in the blood, to see how well response heterogeneity could predict outcomes for patients with NETs. In this study, response heterogeneity was linked with worse outcomes for patients. Therefore, it may be useful in understanding how NETs respond to treatment. Further research is needed in a different group of patients with NETs, and in patients receiving other treatments, to better understand response heterogeneity.

A deep learning method for predicting knee osteoarthritis radiographic progression from MRI.

BACKGROUND: The identification of patients with knee osteoarthritis (OA) likely to progress rapidly in terms of structure is critical to facilitate the development of disease-modifying drugs. METHODS: Using 9280 knee magnetic resonance (MR) images (3268 patients) from the Osteoarthritis Initiative (OAI) database , we implemented a deep learning method to predict, from MR images and clinical variables including body mass index (BMI), further cartilage degradation measured by joint space narrowing at 12 months. RESULTS: Using COR IW TSE images, our classification model achieved a ROC AUC score of 65%. On a similar task, trained radiologists obtained a ROC AUC score of 58.7% highlighting the difficulty of the classification task. Additional analyses conducted in parallel to predict pain grade evaluated by the WOMAC pain index achieved a ROC AUC score of 72%. Attention maps provided evidence for distinct specific areas as being relevant in those two predictive models, including the medial joint space for JSN progression and the intra-articular space for pain prediction. CONCLUSIONS: This feasibility study demonstrates the interest of deep learning applied to OA, with a potential to support even trained radiologists in the challenging task of identifying patients with a high-risk of disease progression.