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  1. Abas Mohamed Y, Ee Khoo B, Shahrimie Mohd Asaari M, Ezane Aziz M, Rahiman Ghazali F
    Int J Med Inform, 2024 Nov 04;193:105689.
    PMID: 39522406 DOI: 10.1016/j.ijmedinf.2024.105689
    OBJECTIVE: Explainable Artificial Intelligence (XAI) is increasingly recognized as a crucial tool in cancer care, with significant potential to enhance diagnosis, prognosis, and treatment planning. However, the holistic integration of XAI across all stages of cancer care remains underexplored. This review addresses this gap by systematically evaluating the role of XAI in these critical areas, identifying key challenges and emerging trends.

    MATERIALS AND METHODS: Following the PRISMA guidelines, a comprehensive literature search was conducted across Scopus and Web of Science, focusing on publications from January 2020 to May 2024. After rigorous screening and quality assessment, 69 studies were selected for in-depth analysis.

    RESULTS: The review identified critical gaps in the application of XAI within cancer care, notably the exclusion of clinicians in 83% of studies, which raises concerns about real-world applicability and may lead to explanations that are technically sound but clinically irrelevant. Additionally, 87% of studies lacked rigorous evaluation of XAI explanations, compromising their reliability in clinical practice. The dominance of post-hoc visual methods like SHAP, LIME and Grad-CAM reflects a trend toward explanations that may be inherently flawed due to specific input perturbations and simplifying assumptions. The lack of formal evaluation metrics and standardization constrains broader XAI adoption in clinical settings, creating a disconnect between AI development and clinical integration. Moreover, translating XAI insights into actionable clinical decisions remains challenging due to the absence of clear guidelines for integrating these tools into clinical workflows.

    CONCLUSION: This review highlights the need for greater clinician involvement, standardized XAI evaluation metrics, clinician-centric interfaces, context-aware XAI systems, and frameworks for integrating XAI into clinical workflows for informed clinical decision-making and improved outcomes in cancer care.

  2. Faisham WI, Mat Saad AZ, Alsaigh LN, Nor Azman MZ, Kamarul Imran M, Biswal BM, et al.
    Asia Pac J Clin Oncol, 2017 Apr;13(2):e104-e110.
    PMID: 25870979 DOI: 10.1111/ajco.12346
    AIM: Osteosarcoma is a highly malignant primary bone tumor. The study aim to evaluate the prognostic factors influencing the survival rate in our center.

    METHODS: This was a retrospective cohort study of all patients treated between January 2005 and December 2010.

    RESULTS: We included 163 patients with an age range of 6-59 years (median = 19). The median follow-up was 47 months (range 36-84). The overall survival in patients who completed chemotherapy and surgery (n = 117) was 72% at 2 years and 44% at 5 years. Histologically, 99 (85%) had osteoblastic, 6 (5%) had chondroblastic and 3 (2.5%) had telangiectatic osteosarcoma. Limb salvage surgery was performed in 80 (49%) and 41 (25%) underwent amputation. However, 46 patients (28%) underwent no surgical intervention and incomplete chemotherapy. In total, 38/79 patients had a good chemotherapy response. There was a significantly better survival rate for limb salvage versus amputation. Independent prognostic factors for survival are compliance to treatment and presence of lung metastasis.

    CONCLUSION: The overall survival of osteosarcoma patients was influenced by the presence of pulmonary metastases and compliance to treatment. Histological subtype, different chemotherapy regimens and histological necrosis after chemotherapy did not significantly influence survival. The patients who did not complete treatment had significantly poorer survival.

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