Machine Learning-Driven Cancer Diagnostics with Improved Robustness and Interpretability

Abstract

Cancer remains one of the leading causes of death worldwide, underscoring the critical need for early diagnosis to improve long-term survival outcomes and reduce mortality rates. Despite significant progress, the development of effective cancer diagnostics continues to face two major challenges. First, the design and optimization of diagnostic assays and analytical workflows largely rely on empirical, trial-and-error approaches, which are inefficient and often yield limited robustness and generalizability. Second, the interpretation of high-dimensional and heterogeneous clinical imaging and molecular profiling data remains complicated, hindering interpretability and the translation of results into clinically actionable insights. Machine learning (ML), with its advanced capabilities in pattern recognition, optimization, and prediction, offers a promising approach to address both challenges for accelerating the development of next-generation cancer diagnostics. In this perspective, we briefly outline the widely used ML algorithms in cancer diagnostics and critically compare their strengths and limitations in real-world applications, considering factors such as data scale, class imbalance, feature structure, generalization performance, and model interpretability. We then summarize recent advances enabled by ML, ranging from analytical platform optimization to multiscale data interpretation. Finally, we discuss remaining challenges and propose a roadmap for future research in ML-driven cancer diagnostics.