Abstract 3650: Deep learning-based image cytometry and co-localization index in tumor immune microenvironment
Bibliographic record
Abstract
Abstract Background: In pathology, digitizing tissue slides has prompted a remarkable development in image analysis using deep learning. This technological advancement is anticipated to aid in pathological diagnosis and to enhance patient management. Deep learning-based image cytometry (DL-IC) enables accurate cell identification and counting and the acquisition of vast amounts of location information from tissue slides. DL-IC can capture information about the diverse and complex tumor immune microenvironment(TIME) and its constituent cells and help identify biomarkers to predict patient treatment efficacy and prognosis. This study will introduce a spatial interaction map and co-localization index (CLI) for the analysis of TIME using DL-IC. Materials and Methods: Cu-Cyto, a deep learning-based image analysis technology, was used in this study; bit-pattern kernel filtering technology, which can accurately count cells while avoiding the determination of multiple cell counts, was used by Cu-Cyto (Abe T, et al. Anticancer Res. 43:3755, 2023). First, the accuracy of cell counting using Cu-Cyto was evaluated. Second, tumor tissue slides with immunohistochemical (IHC) and hematoxylin-eosin (H&E) staining were prepared from surgical specimens of patients with rectal cancer who had undergone neoadjuvant chemoradiotherapy (NACRT), and the relationship between the co-localization index (CLI) of cancer cells and CD8+T cells and prognosis was investigated. CLI was defined to predict cell- cell interactions on the basis of the relative distances between different cell types (Nagasaka T. PCT/JP 2021/021455). Results: The performances of three versions of Cu-Cyto were evaluated according to their learning stages. In the early stage of learning, the F1 score for immunostained CD8+ T cells (0.343) was higher than that for non-immunostained cells (adenocarcinoma cells [0.040] and lymphocytes [0.002]). In the latest stage of learning, the F1 scores for adenocarcinoma cells, lymphocytes, and CD8+ T cells were 0.589, 0.889, and 0.911, respectively. Next, we examined the correlation of CLI between cancer cells and CD8+ T cells with prognosis: patients with a higher CLI significantly prolonged five-year disease-free survival (P=0.038), while there was no substantial difference in five-year overall survival (P=0.57). Conclusion]: Cu-Cyto performed well in cell determination. In particular, IHC was able to increase the learning efficiencies in the early stages of learning. The CLI calculated using Cu-Cyto ts an objective, reproducible, and innovative quantitative approach for assessing cell-cell interactions, which has been shown to be associated with recurrence-free survival in patients with rectal cancer after NACRT. Its performance is expected to improve even further with continuous learning, and the DL-IC can contribute to the implementation of precision oncology. Citation Format: Tomoki Abe, Kimihiro Yamashita, Toru Nagasaka, Tomosuke Mukoyama, Souichirou Miyake, Yasuhiro Ueda, Masayuki Ando, Yuki Okazoe, Takao Tsuneki, Yukari Adachi, Ryunosuke Konaka, Ryuichiro Sawada, Hironobu Goto, Hiroshi Hasegawa, Shingo Kanaji, Takeru Matsuda, Takumi Fukumoto, Yoshihiro Kakeji. Deep learning-based image cytometry and co-localization index in tumor immune microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3650.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".