Work

Soft multiclass feature augmented deep learning to predict tumor origins using cytology or histology whole slide images

Year: 2026

Type: article

Abstract: Tailoring cancer treatment to the primary tumor site is essential for optimal outcomes, yet identifying the source of metastasis remains a significant clinical challenge. The primary tumor location influences not only the choice of treatment but also its delivery method, potential side effects and overall prognosis. Cancer of unknown primary (CUP) persists despite comprehensive diagnostic evaluations. We present the first fast, low-cost deep learning based cancer screening approach, i.e. a new data efficient soft multiclass feature augmentation deep learning, to identify the primary origin of malignant cells directly from whole slide images (WSIs) of cytological smears (CS), cell blocks (CB) or histopathology slides. Our framework was evaluated across three datasets: a multi-center histopathology collection of 69 sites (1,196 WSIs), a CS dataset (260 WSIs), and a CB dataset (129 WSIs). The proposed method achieved excellent accuracy, outperforming four state-of-the-art methods in identifying common metastatic origins. These findings demonstrate the potential of this deep learning tool to advance precision oncology by rapidly guiding clinicians toward appropriate, site-specific cancer treatments.

Source: npj Digital Medicine

DOI: 10.1038/s41746-026-02604-7

Authors: Ching-Wei Wang, Po-Jen Lai, Tzu-Chiao Chu, Tzu-Kang Wu, Chih-Hsuan Liang, Tai-Kuang Chao

Institutions: National Taiwan University of Science and Technology, Tri-Service General Hospital, National Defense Medical Center

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $4,090