Deep learning based automated epidermal growth factor receptor and anaplastic lymphoma kinase status prediction of brain metastasis in non-small cell lung cancer.

Aim: The aim of this study was to investigate the feasibility of developing a deep learning (DL) algorithm for classifying brain metastases from non-small cell lung cancer (NSCLC) into epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement groups and to compare the accuracy with classification based on semantic features on imaging. Methods: Data set of 117 patients was analysed from 2014 to 2018 out of which 33 patients were EGFR positive, 43 patients were ALK positive and 41 patients were negative for either mutation. Convolutional neural network (CNN) architecture efficient net was used to study the accuracy of classification using T1 weighted (T1W) magnetic resonance imaging (MRI) sequence, T2 weighted (T2W) MRI sequence, T1W post contrast (T1post) MRI sequence, fluid attenuated inversion recovery (FLAIR) MRI sequences. The dataset was divided into 80% training and 20% testing. The associations between mutation status and semantic features, specifically sex, smoking history, EGFR mutation and ALK rearrangement status, extracranial metastasis, performance status and imaging variables of brain metastasis were analysed using descriptive analysis [chi-square test (χ2)], univariate and multivariate logistic regression analysis assuming 95% confidence interval (CI). Results: In this study of 117 patients, the analysis by semantic method showed 79.2% of the patients belonged to ALK positive were non-smokers as compared to double negative groups (P = 0.03). There was a 10-fold increase in ALK positivity as compared to EGFR positivity in ring enhancing lesions patients (P = 0.015) and there was also a 6.4-fold increase in ALK positivity as compared to double negative groups in meningeal involvement patients (P = 0.004). Using CNN Efficient Net DL model, the study achieved 76% accuracy in classifying ALK rearrangement and EGFR mutations without manual segmentation of metastatic lesions. Analysis of the manually segmented dataset resulted in improved accuracy of 89% through this model. Conclusions: Both semantic features and DL model showed comparable accuracy in classifying EGFR mutation and ALK rearrangement. Both methods can be clinically used to predict mutation status while biopsy or genetic testing is undertaken.

Demographics, Pattern of Care, and Outcome Analysis of Malignant Melanomas - Experience From a Tertiary Cancer Centre in India.

BACKGROUND: Treatment of malignant melanoma has undergone a paradigm shift with the advent of immune checkpoint inhibitors (ICI) and targeted therapies. However, access to ICI is limited in low-middle income countries (LMICs). PATIENTS AND METHODS: Histologically confirmed malignant melanoma cases registered from 2013 to 2019 were analysed for pattern of care, safety, and efficacy of systemic therapies (ST). RESULTS: There were 659 patients with a median age of 53 (range 44-63) years; 58.9% were males; 55.2% were mucosal melanomas. Most common primary sites were extremities (36.6%) and anorectum (31.4%). Nearly 10.8% of the metastatic cohort were BRAF mutated. Among 368 non-metastatic patients (172 prior treated, 185 de novo, and 11 unresectable), with a median follow-up of 26 months (0-83 months), median EFS and OS were 29.5 (95% CI: 22-40) and 33.3 (95% CI: 29.5-41.2) months, respectively. In the metastatic cohort, with a median follow up of 24 (0-85) months, the median EFS for BSC was 3.1 (95% CI 1.9-4.8) months versus 3.98 (95% CI 3.2-4.7) months with any ST (HR: 0.69, 95% CI: 0.52-0.92; P = 0.011). The median OS was 3.9 (95% CI 3.3-6.4) months for BSC alone versus 12.0 (95% CI 10.5-15.1) months in any ST (HR: 0.38, 95% CI: 0.28-0.50; P < 0.001). The disease control rate was 51.55%. Commonest grade 3-4 toxicity was anemia with chemotherapy (9.5%) and ICI (8.8%). In multivariate analysis, any ST received had a better prognostic impact in the metastatic cohort. CONCLUSIONS: Large real-world data reflects the treatment patterns adopted in LMIC for melanomas and poor access to expensive, standard of care therapies. Other systemic therapies provide meaningful clinical benefit and are worth exploring especially when the standard therapies are challenging to administer.

Percutaneous Radiofrequency Ablation of Appendicular Skeleton Chondroblastoma-an Experience from a Tertiary Care Cancer Center.

PURPOSE: To evaluate safety and long-term efficacy of radiofrequency (RF) ablation in treatment of chondroblastoma. MATERIALS AND METHODS: This retrospective analysis comprised 27 consecutive patients with histopathologically proven chondroblastoma treated by RF ablation. The tumors were located in the proximal humerus (n = 6), proximal tibia (n = 8), proximal femur (n = 6), distal femur (n = 5), acromion process (n = 1), and lunate (n = 1). In 19 patients (70.3%), the tumor was in the weight-bearing area of the bone. Clinical response was assessed by comparing pain scores and functional assessment by Musculoskeletal Tumor Society (MSTS) score before and after ablation. Patients were followed for a minimum of 1 year to rule out complications and recurrence. RESULTS: Technical success rate was 100%. Mean pain score before the procedure was 7.34 (range, 7-9); all patients experienced a reduction in pain, with 25 (92.6%) patients reporting complete pain relief at 6 weeks. Mean MSTS score before the procedure was 15.4, whereas mean MSTS score at 6 weeks after the procedure was 28.6, suggesting significant functional improvement (P < .0001). Two patients developed osteonecrosis and collapse of the treated bone. There were no recurrences. CONCLUSIONS: Percutaneous RF ablation is a safe and effective option for treating chondroblastoma of the appendicular skeleton.