دورية أكاديمية
A Novel Artificial Intelligence-based Parameterization Approach of the Stromal Landscape in Merkel Cell Carcinoma: a Multi-Institutional Study.
| العنوان: | A Novel Artificial Intelligence-based Parameterization Approach of the Stromal Landscape in Merkel Cell Carcinoma: a Multi-Institutional Study. |
|---|---|
| المؤلفون: | Bui CM; Department of Pathology, University of Rochester, 6001 Elmwood Ave, Rochester, NY 14642. Electronic address: chaubuiminh1@gmail.com., Le MK; Department of Pathology, University of Yamanashi, 1110 Shimokato Chuo Yamanashi 409-3898., Kawai M; Department of Pathology, University of Yamanashi, 1110 Shimokato Chuo Yamanashi 409-3898 Japan., Vuong HG; Department of Pathology, University of Iowa Hospitals and Clinics, 200 Hawkins Dr, Iowa, City, IA 52242., Rybski KJ; Department of Pathology, University of Rochester, 6001 Elmwood Ave, Rochester, NY 14642., Mannava K; Department of Dermatology, University of Rochester, 40 Celebration Dr., Rochester, NY 14623., Kondo T; Department of Pathology, University of Yamanashi, 1110 Shimokato Chuo Yamanashi 409-3898 Japan., Okamoto T; Department of Dermatology, University of Yamanashi, 1110 Shimokato Chuo Yamanashi 409-3898 Japan., Laageide L; Department of Dermatology, University of Rochester, 40 Celebration Dr., Rochester, NY 14623., Swick B; Department of Dermatology, University of Iowa Hospitals and Clinics, 200 Hawkins Dr, Iowa, City, IA 52242., Balzer B; Department of Pathology, Cedars Sinai Medical Center, 87000 Beverly Blvd. Los Angeles, CA 90048., Smoller BR; Department of Pathology, University of Rochester, 6001 Elmwood Ave, Rochester, NY 14642. |
| المصدر: | Laboratory investigation; a journal of technical methods and pathology [Lab Invest] 2024 Aug 13, pp. 102123. Date of Electronic Publication: 2024 Aug 13. |
| Publication Model: | Ahead of Print |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Inc Country of Publication: United States NLM ID: 0376617 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1530-0307 (Electronic) Linking ISSN: 00236837 NLM ISO Abbreviation: Lab Invest Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: 2023- : [New York] : Elsevier Inc. Original Publication: Baltimore : Williams & Wilkins |
| مستخلص: | Background: Tumor-Stroma Ratio (TSR) has been recognized as a valuable prognostic indicator in various solid tumors. This study aimed to examine the clinicopathological relevance of TSR in Merkel cell carcinoma (MCC) using artificial intelligence (AI)-based parameterization of the stromal landscape and validate TSR scores generated by our AI model versus human-assessed. Methods: 112 MCC cases with Whole Slide Images (WSIs) were collected from four different institutions. WSIs were first partitioned into 128x128-pixel "mini-patches" then classified by a novel framework, termed Pre-TumOr And STroma (Pre-TOAST) and TOAST, whose output equaled the probability of the mini-patch representing tumor cells rather than stroma. Hierarchical random samplings of 50 mini-patches per region were performed throughout 50 regions per slide. TSR and Tumor-Stromal Landscape (TSL) parameters were estimated by the maximum-likelihood algorithm. Results: Receiver Operating Characteristic (ROC) curves showed the areas under the curve (AUCs) of Pre-TOAST in discriminating classed of interest (COI) including tumor cells, collagenous stroma, and lymphocytes from non-classes of interest (non-COI) including hemorrhage, space, and necrosis were 1.00. AUCs of TOAST in differentiating tumor cells from related stroma were 0.93. MCC stroma was categorized into TSR-high (TSR≥50%) and TSR-low (TSR<50%) using both AI- and human pathology-based methods. AI-based TSR-high subgroup exhibited notably shorter Metastasis-Free Survival (MFS) with a statistical significance of p=0.029. Interestingly, pathologist-determined TSR subgroups lacked statistical significance in Recurrence-Free Survival (RFS), MFS, and Overall Survival (OS) (p>0.05). Density-based spatial clustering of applications with noise (DBSCAN) analysis identified two distinct Tumor-Stroma Landscape (TSL) clusters: TSL1 and TSL2. TSL2 showed significantly shorter RFS (p=0.045) and markedly reduced MFS (p<0.001) compared to TSL1. Conclusion: TSL classification appears to offer better prognostic discrimination than traditional TSR evaluation in MCC. TSL can be reliably calculated using an AI-based classification framework and predict various prognostic features of MCC. (Copyright © 2024. Published by Elsevier Inc.) |
| تواريخ الأحداث: | Date Created: 20240815 Latest Revision: 20240815 |
| رمز التحديث: | 20240816 |
| DOI: | 10.1016/j.labinv.2024.102123 |
| PMID: | 39147033 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 1530-0307 |
|---|---|
| DOI: | 10.1016/j.labinv.2024.102123 |