Natural Language Processing of Computed Tomography Reports to Label Metastatic Phenotypes With Prognostic Significance in Patients With Colorectal Cancer

Abstract

PURPOSE Natural language processing (NLP) applied to radiology reports can help identify clinically relevant M1 subcategories of patients with colorectal cancer (CRC). The primary purpose was to compare the overall survival (OS) of CRC according to American Joint Committee on Cancer TNM staging and explore an alternative classification. The secondary objective was to estimate the frequency of metastasis for each organ. METHODS Retrospective study of CRC who underwent computed tomography (CT) chest, abdomen, and pelvis between July 1, 2009, and March 26, 2019, at a tertiary cancer center, previously labeled for the presence or absence of metastasis by an NLP prediction model. Patients were classified in M0, M1a, M1b, and M1c (American Joint Committee on Cancer), or an alternative classification on the basis of the metastasis organ number: M1, single; M2, two; M3, three or more organs. Cox regression models were used to estimate hazard ratios; Kaplan-Meier curves were used to visualize survival curves using the two M1 subclassifications. RESULTS Nine thousand nine hundred twenty-eight patients with a total of 48,408 CT chest, abdomen, and pelvis reports were included. On the basis of NLP prediction, the median OS of M1a, M1b, and M1c was 4.47, 1.72, and 1.52 years, respectively. The median OS of M1, M2, and M3 was 4.24, 2.05, and 1.04 years, respectively. Metastases occurred most often in liver (35.8%), abdominopelvic lymph nodes (32.9%), lungs (29.3%), peritoneum (22.0%), thoracic nodes (19.9%), bones (9.2%), and pelvic organs (7.5%). Spleen and adrenal metastases occurred in < 5%. CONCLUSION NLP applied to a large radiology report database can identify clinically relevant metastatic phenotypes and be used to investigate new M1 substaging for CRC. Patients with three or more metastatic disease organs have the worst prognosis, with an OS of 1 year.

Automatic Detection of Distant Metastasis Mentions in Radiology Reports in Spanish

“… 23 - 25 These systems aimed to determine metastasis or recurrence status and other information, using reports of BCa, 12 - 15 lung cancer, 22 , 23 hepatocellular carcinoma, 14 , 24 PCa, 18 , 20 melanoma, 11 or colorectal cancer (CRC). 21 Five studies did not select reports of patients affected with a cancer type but used single or multiple organ imaging reports of patients with different types of primary tumors to identify metastases and performed mostly binary classifications such as the presence of single versus multiple metastases or the presence versus absence of metastases across different organs. 7 , 16 , 17 , 19 , 25 …”

“…Regarding the report type, the main approach was the use of notes and texts of diverse nature and specialty (discharge summaries, progress notes, radiology and pathology reports, etc) extracted from electronic health records 13 - 15 , 18 , 24 , 25 and sometimes complemented with structured information. 11 , 12 Other studies used only one type of radiology report, such as bone scintigraphy, 16 magnetic resonance imaging (MRI), 17 CT, 7 , 19 , 21 , 23 PET-CT 22 reports, or a combination of these. 20 …”

An interpretable deep learning framework for predicting liver metastases in postoperative colorectal cancer patients using natural language processing and clinical data integration

“…Moreover, studies utilizing deep learning from free text to predict patient outcomes deserve further attention. More recently, Causa Andrieu et al 15 developed an NLP‐based radiology report analysis model to identify clinically meaningful CRC metastatic phenotypes and demonstrated a correlation between the phenotypes and overall clinical survival. Our previous study established a domain‐specific transfer learning pipeline to identify patients with clinically meaningful pathogenesis related to tinnitus 16 .…”