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Background: The proportion of childhood cancer survivors (CCS) in low/middle-income countries (LMICs) is rising. CCS often develop several physical and psycho-social long-term adverse effects, with unique healthcare needs. Primary healthcare providers (primary care physicians (PCPs)), especially in LMICs, are often not equipped to handle survivorship care. This study aimed to assess knowledge, and attitude among trainee healthcare providers concerning major issues of paediatric survivorship care. Methods: A multi-centre, cross-sectional, questionnaire-based study was conducted among nursing and medical undergraduate students, and postgraduate medical residents across three tertiary-care teaching hospitals in India--All India Institute of Medical Sciences, New Delhi; Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry; and Maulana Azad Medical College, New Delhi. A questionnaire with total of 24 questions (14 knowledge-based and 10 attitude-based) was finalised after validation by expert review and piloting. The major domains covered in the questionnaire included knowledge and attitude regarding long-term adverse effects and psychosocial, employment-related issues faced by the survivors. It was administered to the study participants electronically. The knowledge-based questions had true/false responses (scored as 0 or 1 if incorrect or correct, respectively). Attitude-based questions were scored as 5-point Likert scale. Results: Total 898 responses were collected (median age: 21 years, 64% (576/898) female). Among the respondents, 44% were undergraduate medical students, 42% were nursing students and 14% were postgraduate medical residents. The mean (SD) of knowledge score was 8.72 (2.04) (out of 14). On multivariable analysis, only discipline of training predicted knowledge scores regarding survivorship care. Postgraduate medical residents (9.08) as well as undergraduate medical students (8.85), had significantly higher mean knowledge scores than nursing students (8.47) (p=0.004). Two questions were answered incorrectly by the majority; children and siblings of CCS need additional genetic screening (79% incorrectly answered true), and CCS face intimacy issues in relation to normal sexual functioning (59% incorrectly answered false). Nearly half (48%) of respondents believed that their knowledge of cancer survivorship issues was inadequate. Majority of respondents (84%) suggested that oncologists should handle long-term survivorship care rather than PCPs. Conclusion: Trainee healthcare providers in India reported inadequate knowledge regarding survivorship care. Improving awareness by incorporating survivorship in teaching curriculum is imperative to equip future PCPs to provide survivorship care across the country. [ABSTRACT FROM AUTHOR]

The presence of lung metastases in patients with primary malignancies is an important criterion for treatment management and prognostication. Computed tomography (CT) of the chest is the preferred method to detect lung metastasis. However, CT has limited efficacy in differentiating metastatic nodules from benign nodules (e.g., granulomas due to tuberculosis) especially at early stages (<5 mm). There is also a significant subjectivity associated in making this distinction, leading to frequent CT follow-ups and additional radiation exposure along with financial and emotional burden to the patients and family. Even 18F-fluoro-deoxyglucose positron emission technology-computed tomography (18F-FDG PET-CT) is not always confirmatory for this clinical problem. While pathological biopsy is the gold standard to demonstrate malignancy, invasive sampling of small lung nodules is often not clinically feasible. Currently, there is no non-invasive imaging technique that can reliably characterize lung metastases. The lung is one of the favored sites of metastasis in sarcomas. Hence, patients with sarcomas, especially from tuberculosis prevalent developing countries, can provide an ideal platform to develop a model to differentiate lung metastases from benign nodules. To overcome the lack of optimal specificity of CT scan in detecting pulmonary metastasis, a novel artificial intelligence (AI)-based protocol is proposed utilizing a combination of radiological and clinical biomarkers to identify lung nodules and characterize it as benign or metastasis. This protocol includes a retrospective cohort of nearly 2,000–2,250 sample nodules (from at least 450 patients) for training and testing and an ambispective cohort of nearly 500 nodules (from 100 patients; 50 patients each from the retrospective and prospective cohort) for validation. Ground-truth annotation of lung nodules will be performed using an inhouse- built segmentation tool. Ground-truth labeling of lung nodules (metastatic/benign) will be performed based on histopathological results or baseline and/or follow-up radiological findings along with clinical outcome of the patient. Optimal methods for data handling and statistical analysis are included to develop a robust protocol for early detection and classification of pulmonary metastasis at baseline and at follow-up and identification of associated potential clinical and radiological markers. [ABSTRACT FROM AUTHOR]