Combined Nivolumab and Ipilimumab in Octogenarian and Nonagenarian Melanoma Patients.

Data regarding elderly melanoma patients treated with anti-PD-1 or anti-CTLA-4 antibodies are in favor of tolerability outcomes that are similar to those of younger counterparts. However, there are very few studies focusing on elderly patients receiving nivolumab combined with ipilimumab (NIVO + IPI). Here, we ask what are the current prescribing patterns of NIVO + IPI in the very elderly population and analyze the tolerance profile. This French multicenter retrospective study was conducted on 60 melanoma patients aged 80 years and older treated with NIVO + IPI between January 2011 and June 2022. The mean age at first NIVO + IPI administration was 83.7 years (range: 79.3-93.3 years). Fifty-five patients (92%) were in good general condition and lived at home. Two dosing regimens were used: NIVO 1 mg/kg + IPI 3 mg/kg Q3W (NIVO1 + IPI3) in 27 patients (45%) and NIVO 3 mg/kg + IPI 1 mg/kg Q3W (NIVO3 + IPI1) in 33 patients (55%). NIVO + IPI was a first-line treatment in 39 patients (65%). The global prevalence of immune-related adverse events was 63% (38/60), with 27% (16/60) being of grade 3 or higher. Grade ≥ 3 adverse events were less frequent in patients treated with NIVO3 + IPI1 compared with those treated with NIVO1 + IPI3 (12% versus 44%, p = 0.04). In conclusion, the prescribing patterns of NIVO + IPI in very elderly patients are heterogeneous in terms of the dosing regimen and line of treatment. The safety profile of NIVO + IPI is reassuring; whether or not the low-dose regimen NIVO3 + IPI1 should be preferred over NIVO1 + IPI3 in patients aged 80 years or older remains an open question.

Heterotopic ossification and COVID 19: Imaging analysis of ten consecutive cases.

PURPOSE: Heterotopic ossification (HO) is defined by the formation of mature lamellar bone in periarticular soft tissue due to prolonged immobility. This study aimed to explore the imaging features of HOs in immobilized COVID-19 patients compared to other causes previously described in the literature. METHOD: This retrospective single centre study included patients with severe COVID-19 hospitalized in intensive care unit (ICU) with mechanical ventilation and affected by HOs between March 2020 and December 2021. Two radiologists reviewed imaging features of biphasic CT-scans using a standardized template including morphological findings and anatomical relationship of the HO with the joint, vessels and nerves. RESULTS: 10 COVID-19 patients with 19 analyzed HOs following ICU hospitalization were including. Biphasic CT imaging characteristics were analyzed. The hips were the most commonly affected joint (n = 14/19; 74%). The distribution was mainly posterior (n = 7/19; 38%). HOs were located away from main arteries. No case of severe demineralization was observed. Capsular disruption was observed for three HOs (n = 3/19; 16%). One patient presented concomitant venous thrombosis ipsilateral to the HO. CT-scan demonstrated neural involvement of the sciatic nerve in 3 patients with HO (n = 3/19; 16%). CONCLUSION: Severe COVID-19 patients with a biphasic CT imaging presented HO mainly located around the hips, with rare vessel and nerve invasion and no severe demineralization. Some features such as a lower level of local invasion differ from HOs related to other disorders as described in the literature whereas morphological aspects are similar.

Validation of a deep learning segmentation algorithm to quantify the skeletal muscle index and sarcopenia in metastatic renal carcinoma.

OBJECTIVES: To validate a deep learning (DL) algorithm for measurement of skeletal muscular index (SMI) and prediction of overall survival in oncology populations. METHODS: A retrospective single-center observational study included patients with metastatic renal cell carcinoma between 2007 and 2019. A set of 37 patients was used for technical validation of the algorithm, comparing manual vs DL-based evaluations. Segmentations were compared using mean Dice similarity coefficient (DSC), SMI using concordance correlation coefficient (CCC) and Bland-Altman plots. Overall survivals (OS) were compared using log-rank (Kaplan-Meier) and Mann-Whitney tests. Generalizability of the prognostic value was tested in an independent validation population (N = 87). RESULTS: Differences between two manual segmentations (DSC = 0.91, CCC = 0.98 for areas) or manual vs. automated segmentation (DSC = 0.90, CCC = 0.98 for areas, CCC = 0.97 for SMI) had the same order of magnitude. Bland-Altman plots showed a mean difference of -3.33 cm2 [95%CI: -15.98, 9.1] between two manual segmentations, and -3.28 cm2 [95% CI: -14.77, 8.21] for manual vs. automated segmentations. With each method, 20/37 (56%) patients were classified as sarcopenic. Sarcopenic vs. non-sarcopenic groups had statistically different survival curves with median OS of 6.0 vs. 12.5 (p = 0.008) and 6.0 vs. 13.9 (p = 0.014) months respectively for manual and DL methods. In the independent validation population, sarcopenic patients according to DL had a lower OS (10.7 vs. 17.3 months, p = 0.033). CONCLUSION: A DL algorithm allowed accurate estimation of SMI compared to manual reference standard. The DL-calculated SMI demonstrated a prognostic value in terms of OS. KEY POINTS: • A deep learning algorithm allows accurate estimation of skeletal muscle index compared to a manual reference standard with a concordance correlation coefficient of 0.97. • Sarcopenic patients according to SMI thresholds after segmentation by the deep learning algorithm had statistically significantly lower overall survival compared to non-sarcopenic patients.