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In recent studies, small cell lung cancer (SCLC) treatment guidelines based on Veterans’ Administration Lung Study Group limited/extensive disease staging and resulted in broad and inseparable prognostic subgroups. Evidence suggests that the eight versions of tumor, node, and metastasis (TNM) staging can play an important role to address this issue. The aim of the present study was to improve the detection of prognostic subgroups from a real-word data (RWD) cohort of patients and analyze their patterns using a development pipeline with thoracic oncologists and machine learning methods. The method detected subgroups of patients informing unsupervised learning (partition around medoids) including the impact of covariates on prognosis (Cox regression and random survival forest). An analysis was carried out using patients with SCLC (n = 636) with stage IIIA–IVB according to TNM classification. The analysis yielded k = 7 compacted and well-separated clusters of patients. Performance status (Eastern Cooperative Oncology Group-Performance Status), lactate dehydrogenase, spreading of metastasis, cancer stage, and CRP were the baselines that characterized the subgroups. The selected clustering method outperformed standard clustering techniques, which were not capable of detecting meaningful subgroups. From the analysis of cluster treatment decisions, we showed the potential of future RWD applications to understand disease, develop individualized therapies, and improve healthcare decision making. QC 20221115

In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7-64years). We introduce a structural connectivity model of the human brain: MRI scans are divided in rectangular boxes and Pearson's correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r=0.89and Mean Absolute Error MAE =2.19years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE =2.52. The only imaging analysis steps required in the proposed framework are brain extraction and linear registration, hence robust results are obtained with a low computational cost. In addition, the network model provides a novel insight on aging patterns within the brain and specific information about anatomical districts displaying relevant changes with aging.

Synovial sarcoma is a rare malignant mesenchymal tumour of soft tissues. It accounts for 8-10% of all soft-tissue sarcomas. The clinical symptoms at onset are often subtle and the course of the disease is slow. Therefore, diagnostic imaging is essential for the early diagnosis of a malignant tumoral lesion. The aim of this study was to assess the role and usefulness of the different imaging procedures in the diagnosis of synovial sarcoma and to present their findings.Between 1985 and 2002, we retrospectively reviewed 35 patients (21 men and 14 women, aged 14-66 years) with synovial sarcoma treated in the Orthopaedic Oncological Surgery Division of our hospital. All patients had previously undergone conventional radiography, B-mode ultrasound, computed tomography and magnetic resonance imaging.Conventional radiography showed indirect signs of the neoplasm including soft-tissue swelling, calcifications and bone erosions. Ultrasound allowed the detection of focal nodular lesions but was non-specific in distinguishing malignant features. CT after intravenous injection of contrast medium demonstrated inhomogeneous enhancement in 90% of cases, suggesting an alteration in tumour microcirculation. In all cases examined, MRI enabled detection of the intrinsic structural alterations of the mass indicative of an aggressive lesion.Contrast-enhanced CT and MRI provide useful information on the intrinsic structure of the neoplasm, suggesting a presumptive diagnosis. Furthermore, they are necessary for tumour staging, surgical planning and follow-up. The definitive diagnosis is provided by biopsy and histology.