Artificial Intelligence Estimates the Importance of Baseline Factors in Predicting Response to Anti-PD1 in Metastatic Melanoma. (August 2019)
- Record Type:
- Journal Article
- Title:
- Artificial Intelligence Estimates the Importance of Baseline Factors in Predicting Response to Anti-PD1 in Metastatic Melanoma. (August 2019)
- Main Title:
- Artificial Intelligence Estimates the Importance of Baseline Factors in Predicting Response to Anti-PD1 in Metastatic Melanoma
- Authors:
- Indini, Alice
Di Guardo, Lorenza
Cimminiello, Carolina
De Braud, Filippo
Del Vecchio, Michele - Abstract:
- Abstract : Objective: Prognosis of patients with metastatic melanoma has dramatically improved over recent years because of the advent of antibodies targeting programmed cell death protein-1 (PD1). However, the response rate is ~40% and baseline biomarkers for the outcome are yet to be identified. Here, we aimed to determine whether artificial intelligence might be useful in weighting the importance of baseline variables in predicting response to anti-PD1. Methods: This is a retrospective study evaluating 173 patients receiving anti-PD1 for melanoma. Using an artificial neuronal network analysis, the importance of different variables was estimated and used in predicting response rate and overall survival. Results: After a mean follow-up of 12.8 (±11.9) months, disease control rate was 51%. Using artificial neuronal network, we observed that 3 factors predicted response to anti-PD1: neutrophil-to-lymphocyte ratio (NLR) (importance: 0.195), presence of ≥3 metastatic sites (importance: 0.156), and baseline lactate dehydrogenase (LDH) > upper limit of normal (importance: 0.154). Looking at connections between different covariates and overall survival, the most important variables influencing survival were: presence of ≥3 metastatic sites (importance: 0.202), age (importance: 0.189), NLR (importance: 0.164), site of primary melanoma (cutaneous vs. noncutaneous) (importance: 0.112), and LDH > upper limit of normal (importance: 0.108). Conclusions: NLR, presence of ≥3 metastaticAbstract : Objective: Prognosis of patients with metastatic melanoma has dramatically improved over recent years because of the advent of antibodies targeting programmed cell death protein-1 (PD1). However, the response rate is ~40% and baseline biomarkers for the outcome are yet to be identified. Here, we aimed to determine whether artificial intelligence might be useful in weighting the importance of baseline variables in predicting response to anti-PD1. Methods: This is a retrospective study evaluating 173 patients receiving anti-PD1 for melanoma. Using an artificial neuronal network analysis, the importance of different variables was estimated and used in predicting response rate and overall survival. Results: After a mean follow-up of 12.8 (±11.9) months, disease control rate was 51%. Using artificial neuronal network, we observed that 3 factors predicted response to anti-PD1: neutrophil-to-lymphocyte ratio (NLR) (importance: 0.195), presence of ≥3 metastatic sites (importance: 0.156), and baseline lactate dehydrogenase (LDH) > upper limit of normal (importance: 0.154). Looking at connections between different covariates and overall survival, the most important variables influencing survival were: presence of ≥3 metastatic sites (importance: 0.202), age (importance: 0.189), NLR (importance: 0.164), site of primary melanoma (cutaneous vs. noncutaneous) (importance: 0.112), and LDH > upper limit of normal (importance: 0.108). Conclusions: NLR, presence of ≥3 metastatic sites, LDH levels, age, and site of primary melanoma are important baseline factors influencing response and survival. Further studies are warranted to estimate a model to drive the choice to administered anti-PD1 treatments in patients with melanoma. … (more)
- Is Part Of:
- American journal of clinical oncology. Volume 42:Number 8(2019)
- Journal:
- American journal of clinical oncology
- Issue:
- Volume 42:Number 8(2019)
- Issue Display:
- Volume 42, Issue 8 (2019)
- Year:
- 2019
- Volume:
- 42
- Issue:
- 8
- Issue Sort Value:
- 2019-0042-0008-0000
- Page Start:
- Page End:
- Publication Date:
- 2019-08
- Subjects:
- metastatic melanoma -- anti-PD1 -- artificial intelligence -- artificial neural network -- response
Cancer -- Treatment -- Periodicals
Oncology -- Periodicals
Tumors -- Periodicals
616.994005 - Journal URLs:
- http://ovidsp.ovid.com/ovidweb.cgi?T=JS&NEWS=n&CSC=Y&PAGE=toc&D=yrovft&AN=00000421-000000000-00000 ↗
http://www.amjclinicaloncology.com ↗
http://journals.lww.com ↗ - DOI:
- 10.1097/COC.0000000000000566 ↗
- Languages:
- English
- ISSNs:
- 0277-3732
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0823.500000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 14181.xml