ArticleMethods in molecular biology (Clifton, N.J.)2026
Artificial Intelligence for CELLSEARCH Image Analysis.
Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The number of circulating tumor cells (CTC) and tumor-derived extracellular vesicles (tdEV) is an independent predictor of survival in patients with metastatic carcinomas. Being one order of magnitude more abundant than CTC, tdEV can provide complementary prognostic value. On CellSearch immunofluorescent images, operators can identify CTC and tdEV based on morphology, DNA, cytokeratin, and CD45 fluorescent intensity. This manual process is time-consuming and potentially affected by subjective interpretations. To overcome these limitations and maximize standardization among different research centers, we introduce artificial intelligence (AI) based methods for the automated analysis of CellSearch images. These methods were evaluated on fluorescent images of metastatic breast, colorectal, and prostate cancer studies. This chapter will present Contrast Maximization (CM), an AI-based software solution for automated CTC and tdEV identification, and the concept of Blood Tumor Load (BTL), which combines CTC and tdEV counts into a single interpretable biomarker ranging from 0 (favorable) to 1 (unfavorable). CM-CTC demonstrated performance comparable to or better than human operators in predicting overall survival in metastatic cancer patients, enabling rapid and reproducible enumeration of clinically relevant CTCs. Similarly, CM-tdEV outperformed tdEV classification based on human-designed gating strategies. Survival analysis further revealed that BTL outperformed CTC and tdEV counts alone, underscoring the added value of combining these two complementary biomarkers. Notably, BTL derived from CM outperformed BTL based on manual counts, highlighting the advantages of CM in automating and standardizing the analysis process, ultimately enhancing accuracy and strengthening the relationship with clinical outcomes.
Indexed as
Identifiers
42426468What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.