Longitudinal Phenotyping of Circulating Tumor Cells using a Scalable Deep Learning Framework.
A new deep learning system accurately identifies circulating tumor cells from blood samples and connects their patterns to how patients actually fare, advancing liquid biopsy's clinical utility.
This multi-institutional study introduces SEE-TC, a scalable deep learning system for CTC phenotyping validated on 8.5 million individual cells from 3,386 blood samples spanning six cancer types, achieving human-level segmentation accuracy while eliminating the subjectivity and low-throughput limitations of manual CTC identification. Longitudinal SEE-TC readouts of CTC burden were significantly associated with clinical outcomes, establishing this as a platform-agnostic liquid biopsy tool ready for broader clinical investigation.
What the study was
- Study design
- Prospective multi-cancer validation cohort study
- Population
- 3,386 blood samples from patients across 6 cancer types (including prostate, breast, lung, bladder cancers)
- Sample size
- 3386
- Category
- Early Detection
- Maturity
- Validated
- Journal
- Clinical cancer research
Why it surfaced
Large multi-cancer CTC deep learning validation (8.5M cells, 3,386 samples, 6 cancer types) with longitudinal outcomes correlation in Clinical Cancer Research; technically rigorous but clinical translation still under investigation. Flagged PROMISING_PRELIMINARY as downstream clinical utility requires further prospective trials.
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