Natural language processing of radiology reports to assess survival in patients with advanced melanoma Journal Article


Authors: Das, J. P.; Eichholz, J.; Sevilimedu, V.; Gangai, N.; Khalil, D. N.; Postow, M. A.; Do, R. K. G.
Article Title: Natural language processing of radiology reports to assess survival in patients with advanced melanoma
Abstract: Background/Objectives: To use natural language processing (NLP) to extract large-scale data from the CT radiology reports of patients with advanced melanoma treated with immunotherapy and to determine whether liver metastases affect survival. Methods: Patient criteria (M1 disease subclassified into M1a, M1b, or M1c) as well as alternative criteria (M1 with advanced melanoma, imaged with CT chest, abdomen, and pelvis from July 2014–March 2019) were included retrospectively. NLP was used to identify metastases from CT reports, and then patients were classified according to American Joint Committee on Cancer (AJCC) staging disease subclassified into M1L+ or M1L−, indicating whether liver metastases were present or not). Statistical analysis included constructing Kaplan–Meier survival curves and calculating hazard ratios (HRs). Results: 2239 patients were included (mean age, 63 years). Whether using AJCC or alternative criteria, overall survival (OS) was poorest for M1L+ (entire cohort median OS, 0.69 years [95% CI: 0.60–0.82]; immunotherapy cohort median OS, 1.4 years [95% CI: 0.92–2.0]) compared to M1L− (entire cohort median OS, 1.8 years [95% CI: 1.4–2.2]; immunotherapy cohort median OS; M1L−, 2.9 years [95% CI: 2.3–3.9]). The median HR for M1L+ (median HR, 5.35 [95% CI: 4.59–6.24]) was higher than that for M0 (p < 0.001). The median HR for M1L+ (median HR, 2.13 [95% CI: 1.65–2.64]) was higher than that for M0 (p < 0.01). Conclusions: Patients with advanced melanoma, particularly those with liver metastases, demonstrated inferior survival, even when treated with immunotherapy. © 2025 by the authors.
Keywords: melanoma; oncology; immunotherapy; natural language processing; nlp
Journal Title: Cancers
Volume: 17
Issue: 9
ISSN: 2072-6694
Publisher: MDPI  
Date Published: 2025-05-01
Start Page: 1595
Language: English
DOI: 10.3390/cancers17091595
PROVIDER: scopus
PMCID: PMC12071518
PUBMED: 40361518
DOI/URL:
Notes: The MSK Cancer Center Support Grant (P30 CA008748) is acknowledge in the PDF -- Corresponding authors is MSK author: Jeeban P. Das -- Source: Scopus
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MSK Authors
  1. Michael Andrew Postow
    362 Postow
  2. Kinh Gian Do
    257 Do
  3. Danny Nejad Khalil
    64 Khalil
  4. Natalie Gangai
    61 Gangai
  5. Jeeban Paul Das
    43 Das