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All Journal PHARMACOLOGY, MEDICAL REPORTS, ORTHOPEDIC, AND ILLNESS DETAILS (COMORBID)
Rafi, Muhammad Allam
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Biochemistry, Genetics & Molecular Biology Medicine & Pharmacology Neuroscience Nursing

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Comparing Robot-Assisted, Minimally Invasive, Transcervical, and Transhiatal Esophagectomy for Esophageal Cancer: A Causal Deep Learning Meta-Analysis with Neural Architecture Rafi, Muhammad Allam
PHARMACOLOGY, MEDICAL REPORTS, ORTHOPEDIC, AND ILLNESS DETAILS Vol. 4 No. 2 (2025): APRIL
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/comorbid.v4i2.1683

Abstract

This meta-analysis evaluates the effectiveness of four esophagectomy procedures—Robot-Assisted Minimally Invasive Esophagectomy (RAMIE), Minimally Invasive Esophagectomy (MIE), Transcervical Esophagectomy (TCE), and Transhiatal Esophagectomy (THE)—for treating esophageal cancer, utilizing causal deep learning techniques to assess key clinical outcomes. Data from 70,102 patients were analyzed, focusing on operative time, postoperative complications, mortality, hospital stay, and lymph node retrieval. Unlike traditional statistical methods, deep learning models capture non-linear relationships and adjust for multiple confounders, providing more accurate and reliable predictions. The results show RAMIE to be the most effective procedure, with an average operative time of 350 minutes, reduced blood loss (250 mL), and fewer complications (24%). MIE follows closely with 300 minutes of operative time, 200 mL of blood loss, and a 30% complication rate. TCE and THE have higher complication rates (up to 40% and 42%, respectively), alongside longer recovery times. THE, although less effective in clinical outcomes, proved to be more cost-efficient. SUCRA rankings confirmed RAMIE’s superiority (88%), compared to MIE (83%), TCE (76%), and THE (66%). Additionally, decision tree analysis with 95.63% accuracy and 96.17% cross-validation performance supported RAMIE as the optimal choice, highlighting its precision, fewer complications, and faster recovery, despite higher costs. This study underscores the significance of deep learning, enhancing surgical decision-making and optimizing patient outcomes, with machine learning offering a more robust and nuanced approach compared to traditional methods.
DE-ESCALATING INTENSITY AND PRESERVING OUTCOMES: A BAYESIAN-ML NETWORK META-ANALYSIS OF MULTIMODAL TREATMENT STRATEGIES IN HPV-POSITIVE OROPHARYNGEAL CANCER Rafi, Muhammad Allam
PHARMACOLOGY, MEDICAL REPORTS, ORTHOPEDIC, AND ILLNESS DETAILS Vol. 3 No. 3 (2024): JULY
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/comorbid.v3i3.1699

Abstract

The study presents the first integrative Bayesian-machine learning (ML) network meta-analysis, enhanced by machine learning algorithms, to evaluate and rank de-escalation treatment strategies for HPV-positive oropharyngeal squamous cell carcinoma (OPSCC). A total of 2,298 patients from 10 multicenter studies and randomized controlled trials were included, comprising randomized controlled trials, observational studies, and phase II investigations. Twelve distinct treatment strategies were analyzed, including TORS with de-escalated adjuvant RT, reduced-dose chemoradiotherapy, adaptive radiotherapy, and immunotherapy-based regimens. The SUCRA (Surface Under the Cumulative Ranking) scores indicated TORS + de-escalated RT as the top-ranked strategy (SUCRA = 0.91), followed by reduced-dose CRT (0.88) and adaptive RT (0.84). SHAP (SHapley Additive exPlanations) analysis from a Random Forest classifier confirmed that toxicity reduction (impact = 0.34) and quality of life (QOL) improvement (0.28) were the most critical factors driving high SUCRA rankings, with overall survival (OS) rates consistently above 90% in the top three strategies. Funnel plots suggested low publication bias, while cluster heatmaps demonstrated clear stratification of treatment profiles. The t-SNE visualization validated strong feature convergence among top-performing modalities. This analysis highlights the potential of machine learning-guided evidence synthesis to enhance clinical decision-making in personalized OPSCC therapy by balancing oncologic efficacy with functional outcomes.
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