Sarcopenia represents one of the hallmarks of all chronic diseases, including cancer, and was already investigated as a prognostic marker in the pre-immunotherapy era. Sarcopenia can be evaluated using cross-sectional image analysis of CT-scans, at the level of the third lumbar vertebra (L3), to estimate the skeletal muscle index (SMI), a surrogate of skeletal muscle mass, and to evaluate the skeletal muscle density (SMD). We performed a retrospective analysis of consecutive advanced cancer patient treated with PD-1/PD-L1 checkpoint inhibitors. Baseline SMI and SMD were evaluated and optimal cut-offs for survival, according to sex and BMI (+/−25) were computed. The evaluated clinical outcomes were: objective response rate (ORR), immune-related adverse events (irAEs), progression free survival (PFS) and overall survival (OS). From April 2015 to April 2019, 100 consecutive advanced cancer patients were evaluated. 50 (50%) patients had a baseline low SMI, while 51 (51%) had a baseline low SMD according to the established cut offs. We found a significant association between SMI and ECOG-PS (p = 0.0324), while no correlations were found regarding SMD and baseline clinical factors. The median follow-up was 20.3 months. Patients with low SMI had a significantly shorter PFS (HR = 1.66 [95% CI: 1.05-2.61]; p = 0.0291) at univariate analysis, but not at the multivariate analysis. They also had a significantly shorter OS (HR = 2.19 [95% CI: 1.31-3.64]; p = 0.0026). The multivariate analysis confirmed baseline SMI as an independent predictor for OS (HR = 2.19 [1.31-3.67]; p = 0.0027). We did not find significant relationships between baseline SMD and clinical outcomes, nor between ORR, irAEs and baseline SMI (data not shown). Low SMI is associated with shortened survival in advanced cancer patients treated with PD1/PDL1 checkpoint inhibitors. However, the lack of an association between SMI and clinical response suggests that sarcopenia may be generally prognostic in this setting rather than specifically predictive of response to immunotherapy.Sarcopenia is the condition of loss of muscle mass, with decreased muscle power, and it is one of the hallmarks of cancer, which negatively affects the most of clinical outcomes such as toxicities and survival 1 . The interactions must recognize also the lack of other adiposity metrics, such as the waist circumference, the waist-to-height ratio, and the body fat percentage. Moreover, the CT imaging analysis was limited by the data availability; indeed, the acquisition protocol was planned according to the presence of previous examination. conclusionOur finding of a significant shorter OS for low-SMI patients treated with PD-1/PD-L1 checkpoint inhibitors, suggests that sarcopenia might have a prognostic role, rather than predictive. However, to properly weighing our results, we must consider the significant association between poorer PS and low-SMI. Without making conclusive considerations, we can assume that after the advent of ICIs, we should give back further relevance to baseline nut...

Ground-glass opacities (GGOs) are a non-specific high-resolution computed tomography (HRCT) finding tipically observed in early Coronavirus disesase 19 (COVID-19) pneumonia. However, GGOs are also seen in other acute lung diseases, thus making challenging the differential diagnosis. To this aim, we investigated the performance of a radiomics-based machine learning method to discriminate GGOs due to COVID-19 from those due to other acute lung diseases. Two sets of patients were included: a first set of 28 patients (COVID) diagnosed with COVID-19 infection confirmed by real-time polymerase chain reaction (RT-PCR) between March and April 2020 having (a) baseline HRCT at hospital admission and (b) predominant GGOs pattern on HRCT; a second set of 30 patients (nCOVID) showing (a) predominant GGOs pattern on HRCT performed between August 2019 and April 2020 and (b) availability of final diagnosis. Two readers independently segmented GGOs on HRCTs using a semi-automated approach, and radiomics features were extracted using a standard open source software (PyRadiomics). Partial least square (PLS) regression was used as the multivariate machine-learning algorithm. A leave-one-out nested cross-validation was implemented. PLS β-weights of radiomics features, including the 5% features with the largest β-weights in magnitude (top 5%), were obtained. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. The Youden’s test assessed sensitivity and specificity of the classification. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The predictive model delivered an AUC of 0.868 (Youden’s index = 0.68, sensitivity = 93%, specificity 75%, p = 4.2 × 10–7). Of the seven features included in the top 5% features, five were texture-related. A radiomics-based machine learning signature showed the potential to accurately differentiate GGOs due to COVID-19 pneumonia from those due to other acute lung diseases. Most of the discriminant radiomics features were texture-related. This approach may assist clinician to adopt the appropriate management early, while improving the triage of patients.

Pancoast’s syndrome may be the result of neoplastic, inflammatory or infectious disease. We report an unusual case of Pancoast’s syndrome in a patient with metastatic breast cancer. A 54-year-old woman, affected by metastatic breast cancer, presented for severe shoulder pain, paraesthesia and numbness in the right arm. Despite further multiple lines of systemic chemotherapy, she developed a progressive enlargement of retropectoral, supraclavicular and infraclavicular lymph node metastases, which involved brachial plexus, apex of lung and anterior mediastinum. Physical examination revealed severe weakness of proximal muscles of the right arm. Neuropathic pain was managed with pharmacological treatment. Lastly, the patient has been treated with intrathecal analgesia with morphine and ziconotide with a good control of pain. The patient died after 3 months.

Ground-Glass Opacities (GGOs) are a non-specific CT finding observed in the early phase of COVID-19 pneumonia. However, GGOs are also seen in other acute interstitial and alveolar lung diseases, thus making the differential diagnosis a diagnostic challenge. In this poof-of-concept study, we aimed to differentiate COVID-19 pneumonia presenting with GGOs from acute non-COVID-19 lung disease using a novel radiomic-based model in patients who underwent a high-resolution CT (HRCT) scan at hospital admission during the first pandemic peak in Italy. HRCT scans of 28 RT-PCR diagnosed COVID-19 pneumonia (COVID) and 30 acute non-COVID-lung disease (nCOVID) were retrospectively included. All patients showed GGOs as the predominant CT pattern. Two readers, blinded to the final diagnosis, independently segmented GGOs on CT scans by using a semi-automated approach, and radiomic features were extracted from segmented images. Partial least square (PLS) regression was used as the multivariate machine-learning algorithm. A leave-one-out nested cross-validation was implemented to optimize the hyperparameter of PLS and to assess the model generalization. The diagnostic performance of the radiomic model to differentiate between COVID and nCOVID lung disease was assessed through receiver operating characteristic (ROC) analysis. The radiomics-based machine learning model differentiated COVID and nCOVID with an AUC = 0.868 (p = 4.2·10− 7). After a careful prospective evaluation in larger multicentric studies, it may help radiologists to rule out COVID-19 pneumonia thus improving the COVID-19 triaging in epidemic areas.