Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma
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) i...
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Nature Portfolio
2021
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oai:doaj.org-article:8d415bf272d94264979172564903d21c2021-12-02T14:06:51ZAutomated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma10.1038/s41598-021-82305-12045-2322https://doaj.org/article/8d415bf272d94264979172564903d21c2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82305-1https://doaj.org/toc/2045-2322Abstract 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.Michael R. MooreIsabel D. FriesnerEmanuelle M. RizkBenjamin T. FullertonManas MondalMegan H. TragerKaren MendelsonIjeuru ChikekaTahsin KurcRajarsi GuptaBethany R. RohrEric J. RobinsonBalazs AcsRui ChangHarriet KlugerBret TabackLarisa J. GeskinBasil HorstKevin GardnerGeorge NiedtJulide T. CelebiRobyn D. Gartrell-CorradoJane MessinaTammie FerringerDavid L. RimmJoel SaltzJing WangRami VanguriYvonne M. SaengerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021) |
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DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Medicine R Science Q |
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Medicine R Science Q Michael R. Moore Isabel D. Friesner Emanuelle M. Rizk Benjamin T. Fullerton Manas Mondal Megan H. Trager Karen Mendelson Ijeuru Chikeka Tahsin Kurc Rajarsi Gupta Bethany R. Rohr Eric J. Robinson Balazs Acs Rui Chang Harriet Kluger Bret Taback Larisa J. Geskin Basil Horst Kevin Gardner George Niedt Julide T. Celebi Robyn D. Gartrell-Corrado Jane Messina Tammie Ferringer David L. Rimm Joel Saltz Jing Wang Rami Vanguri Yvonne M. Saenger Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma |
| description |
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. |
| format |
article |
| author |
Michael R. Moore Isabel D. Friesner Emanuelle M. Rizk Benjamin T. Fullerton Manas Mondal Megan H. Trager Karen Mendelson Ijeuru Chikeka Tahsin Kurc Rajarsi Gupta Bethany R. Rohr Eric J. Robinson Balazs Acs Rui Chang Harriet Kluger Bret Taback Larisa J. Geskin Basil Horst Kevin Gardner George Niedt Julide T. Celebi Robyn D. Gartrell-Corrado Jane Messina Tammie Ferringer David L. Rimm Joel Saltz Jing Wang Rami Vanguri Yvonne M. Saenger |
| author_facet |
Michael R. Moore Isabel D. Friesner Emanuelle M. Rizk Benjamin T. Fullerton Manas Mondal Megan H. Trager Karen Mendelson Ijeuru Chikeka Tahsin Kurc Rajarsi Gupta Bethany R. Rohr Eric J. Robinson Balazs Acs Rui Chang Harriet Kluger Bret Taback Larisa J. Geskin Basil Horst Kevin Gardner George Niedt Julide T. Celebi Robyn D. Gartrell-Corrado Jane Messina Tammie Ferringer David L. Rimm Joel Saltz Jing Wang Rami Vanguri Yvonne M. Saenger |
| author_sort |
Michael R. Moore |
| title |
Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma |
| title_short |
Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma |
| title_full |
Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma |
| title_fullStr |
Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma |
| title_full_unstemmed |
Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma |
| title_sort |
automated digital til analysis (adta) adds prognostic value to standard assessment of depth and ulceration in primary melanoma |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/8d415bf272d94264979172564903d21c |
| work_keys_str_mv |
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