Meniscal lesion detection and characterization in adult knee MRI: A deep learning model approach with external validation.

PURPOSE: Evaluation of a deep learning approach for the detection of meniscal tears and their characterization (presence/absence of migrated meniscal fragment). METHODS: A large annotated adult knee MRI database was built combining medical expertise of radiologists and data scientists' tools. Coronal and sagittal proton density fat suppressed-weighted images of 11,353 knee MRI examinations (10,401 individual patients) paired with their standardized structured reports were retrospectively collected. After database curation, deep learning models were trained and validated on a subset of 8058 examinations. Algorithm performance was evaluated on a test set of 299 examinations reviewed by 5 musculoskeletal specialists and compared to general radiologists' reports. External validation was performed using the publicly available MRNet database. Receiver Operating Characteristic (ROC) curves results and Area Under the Curve (AUC) values were obtained on internal and external databases. RESULTS: A combined architecture of meniscal localization and lesion classification 3D convolutional neural networks reached AUC values of 0.93 (95% CI 0.82, 0.95) for medial and 0.84 (95% CI 0.78, 0.89) for lateral meniscal tear detection, and 0.91 (95% CI 0.87, 0.94) for medial and 0.95 (95% CI 0.92, 0.97) for lateral meniscal tear migration detection. External validation of the combined medial and lateral meniscal tear detection models resulted in an AUC of 0.83 (95% CI 0.75, 0.90) without further training and 0.89 (95% CI 0.82, 0.95) with fine tuning. CONCLUSION: Our deep learning algorithm demonstrated high performance in knee menisci lesion detection and characterization, validated on an external database.

ß-blockers increase response to chemotherapy via direct antitumour and anti-angiogenic mechanisms in neuroblastoma.

BACKGROUND: The use of ß-blockers for the management of hypertension has been recently associated with significant clinical benefits in cancer patients. Herein, we investigated whether ß-blockers could be used in combination with chemotherapy for the treatment of neuroblastoma. METHODS: Seven ß-blockers were tested for their antiproliferative and anti-angiogenic properties alone, and in combination with chemotherapy in vitro; the most potent drug combinations were evaluated in vivo in the TH-MYCN mouse model of neuroblastoma. RESULTS: Three ß-blockers (i.e., carvedilol, nebivolol and propranolol) exhibited potent anticancer properties in vitro and interacted synergistically with vincristine, independently of P-glycoprotein expression. ß-blockers potentiated the anti-angiogenic, antimitochondrial, antimitotic and ultimately pro-apoptotic effects of vincristine. In vivo, ß-blockers alone transiently slowed tumour growth as compared with vehicle only (P<0.01). More importantly, when used in combination, ß-blockers significantly increased the tumour regression induced by vincristine (P<0.05). This effect was associated with an increase in tumour angiogenesis inhibition (P<0.001) and ultimately resulted in a four-fold increase in median survival, as compared with vincristine alone (P<0.01). CONCLUSION: ß-blockers can increase treatment efficacy against neuroblastoma, and their combination with chemotherapy may prove beneficial for the treatment of this disease and other drug-refractory cancers.