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Abstract
Accurate prognostic biomarkers in early-stage melanoma are urgently needed to stratify patients for clinical trials of adjuvant therapy. We applied a previously developed open source deep learning algorithm to detect tumor-infiltrating lymphocytes (TILs) in hematoxylin and eosin (H&E) images of early-stage melanomas. We tested whether automated digital (TIL) analysis (ADTA) improved accuracy of prediction of disease specific survival (DSS) based on current pathology standards. ADTA was applied to a training cohort (n = 80) and a cutoff value was defined based on a Receiver Operating Curve. ADTA was then applied to a validation cohort (n = 145) and the previously determined cutoff value was used to stratify high and low risk patients, as demonstrated by Kaplan–Meier analysis (p ≤ 0.001). Multivariable Cox proportional hazards analysis was performed using ADTA, depth, and ulceration as co-variables and showed that ADTA contributed to DSS prediction (HR: 4.18, CI 1.51–11.58, p = 0.006). ADTA provides an effective and attainable assessment of TILs and should be further evaluated in larger studies for inclusion in staging algorithms.
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1 Columbia University Irving Medical Center, Department of Medicine, New York, USA (GRID:grid.21729.3f) (ISNI:0000000419368729)
2 NYU School of Medicine, NYULMC, Department of Anesthesiology, Perioperative Care and Pain Medicine, New York, USA (GRID:grid.137628.9) (ISNI:0000 0004 1936 8753)
3 Columbia University, Vagelos College of Physicians and Surgeons, New York, USA (GRID:grid.21729.3f) (ISNI:0000000419368729)
4 Icahn School of Medicine at Mount Sinai, Department of Dermatology, Pathology, and Oncological Sciences, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351)
5 Columbia University Irving Medical Center, Department of Dermatology, New York, USA (GRID:grid.21729.3f) (ISNI:0000000419368729)
6 Stony Brook Medicine, Department of Biomedical Informatics, Stony Brook, USA (GRID:grid.459987.e)
7 University Hospitals Cleveland Medical Center/Case Western Reserve University School of Medicine, Department of Dermatology, Cleveland, USA (GRID:grid.67105.35) (ISNI:0000 0001 2164 3847)
8 Oregon Health and Science University School of Medicine, Portland, USA (GRID:grid.5288.7) (ISNI:0000 0000 9758 5690)
9 Yale School of Medicine, Department of Pathology, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Karolinska Institute, Department of Oncology and Pathology, Stockholm, Sweden (GRID:grid.4714.6) (ISNI:0000 0004 1937 0626)
10 University of Arizona, Department of Neurology, Tucson, USA (GRID:grid.134563.6) (ISNI:0000 0001 2168 186X)
11 Yale School of Medicine, Department of Medicine, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710)
12 Columbia University Irving Medical Center, Department of Surgery, New York, USA (GRID:grid.21729.3f) (ISNI:0000000419368729)
13 University of British Columbia, Department of Pathology, Vancouver, Canada (GRID:grid.17091.3e) (ISNI:0000 0001 2288 9830)
14 Columbia University Irving Medical Center, Department of Pathology, New York, USA (GRID:grid.21729.3f) (ISNI:0000000419368729)
15 Columbia University Irving Medical Center, Department of Pediatrics, New York, USA (GRID:grid.21729.3f) (ISNI:0000000419368729)
16 The Moffitt Cancer Center, Department of Cutaneous Oncology, Tampa, USA (GRID:grid.468198.a) (ISNI:0000 0000 9891 5233)
17 Geisinger Health System, Department of Pathology, Danville, USA (GRID:grid.280776.c) (ISNI:0000 0004 0394 1447)
18 Yale School of Medicine, Department of Pathology, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710)
19 NYU School of Medicine, NYULMC, Department of Anesthesiology, Perioperative Care and Pain Medicine, New York, USA (GRID:grid.137628.9) (ISNI:0000 0004 1936 8753); NYU School of Medicine, Department of Neuroscience and Physiology, New York, USA (GRID:grid.137628.9) (ISNI:0000 0004 1936 8753)