Machine learning-based histological classification that predicts recurrence of peripheral lung squamous cell carcinoma.

Carcinoma, Squamous Cell / diagnosis Humans Lung / pathology Lung Neoplasms / diagnosis Machine Learning Neoplasm Recurrence, Local Neoplasm Staging Prognosis Retrospective Studies
Lung squamous cell carcinoma Machine learning Recurrence Stroma

Journal

Lung cancer (Amsterdam, Netherlands)
ISSN: 1872-8332
Titre abrégé: Lung Cancer
Pays: Ireland
ID NLM: 8800805

Informations de publication

Date de publication:
09 2020
Historique:
received: 06 05 2020
revised: 03 07 2020
accepted: 07 07 2020
pubmed: 9 8 2020
medline: 22 6 2021
entrez: 9 8 2020
Statut: ppublish

Résumé

Cancer tissue is composed of both a cancer cell component and a stromal component. The aim of this study was to investigate if the component ratio predicts a prognosis for lung squamous cell carcinoma (SqCC) patients by using a machine learning method. A total of 135 peripheral SqCC cases (tumor size: 3-5 cm) were enrolled in this study. The areas of the cancer cell component, the necrotic component, and the stromal component were accurately measured via a machine learning method. Each case was divided into the following three subtypes: 1) predominant cancer cell, 2) predominant necrosis, and 3) predominant stroma. The study examined if a particular subtype had prognostic significance. The number of cases per subtype of predominant cancer cell, predominant necrosis, and predominant stroma was 59, 6, and 70, respectively. Patients with the predominant stroma subtype had a significantly shorter recurrence free survival (RFS) than did those with the predominant cancer cell subtype (5-yr RFS: 42.3 % vs. 84.3 %,p < 0.01). Also, in pathological stage I patients, the 5-year RFS rate for the predominant stroma subtype was significantly shorter (5-yr RFS: 64.3 % vs. 88.4 %, p < 0.01). In the multivariate analysis of p-stage I patients, the predominant stroma subtype was confirmed to be an independent prognostic factor for RFS (p < 0.01). Using machine learning, the study confirmed that the predominant stroma subtype was an independent factor for RFS, suggesting that the ratio of the stromal component correlates with the malignant potential of SqCC.

Sections du résumé

BACKGROUND
Cancer tissue is composed of both a cancer cell component and a stromal component. The aim of this study was to investigate if the component ratio predicts a prognosis for lung squamous cell carcinoma (SqCC) patients by using a machine learning method.
METHODS
A total of 135 peripheral SqCC cases (tumor size: 3-5 cm) were enrolled in this study. The areas of the cancer cell component, the necrotic component, and the stromal component were accurately measured via a machine learning method. Each case was divided into the following three subtypes: 1) predominant cancer cell, 2) predominant necrosis, and 3) predominant stroma. The study examined if a particular subtype had prognostic significance.
RESULTS
The number of cases per subtype of predominant cancer cell, predominant necrosis, and predominant stroma was 59, 6, and 70, respectively. Patients with the predominant stroma subtype had a significantly shorter recurrence free survival (RFS) than did those with the predominant cancer cell subtype (5-yr RFS: 42.3 % vs. 84.3 %,p < 0.01). Also, in pathological stage I patients, the 5-year RFS rate for the predominant stroma subtype was significantly shorter (5-yr RFS: 64.3 % vs. 88.4 %, p < 0.01). In the multivariate analysis of p-stage I patients, the predominant stroma subtype was confirmed to be an independent prognostic factor for RFS (p < 0.01).
CONCLUSION
Using machine learning, the study confirmed that the predominant stroma subtype was an independent factor for RFS, suggesting that the ratio of the stromal component correlates with the malignant potential of SqCC.

Identifiants

DOI: 10.1016/j.lungcan.2020.07.011 PMID: 32763506
pubmed: 32763506
pii: S0169-5002(20)30522-5
doi: 10.1016/j.lungcan.2020.07.011
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

252-258

Informations de copyright

Copyright © 2020 Elsevier B.V. All rights reserved.

Auteurs

Yutaro Koike (Y)

Division of Pathology, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Chiba, Japan; Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Japan; Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Chiba, Japan. Electronic address: yukoike@east.ncc.go.jp.

Keiju Aokage (K)

Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Chiba, Japan.

Kosuke Ikeda (K)

Division of Pathology, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Chiba, Japan.

Tokiko Nakai (T)

Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Japan.

Kenta Tane (K)

Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Chiba, Japan.

Tomohiro Miyoshi (T)

Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Chiba, Japan.

Masato Sugano (M)

Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Japan.

Motohiro Kojima (M)

Division of Pathology, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Chiba, Japan.

Satoshi Fujii (S)

Division of Pathology, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Chiba, Japan.

Takeshi Kuwata (T)

Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Japan.

Atsushi Ochiai (A)

Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Chiba, Japan.

Toshiyuki Tanaka (T)

Department of Applied Physics and Physico-Informatics, Faculty of Science and Technology, Keio University, Japan.

Kenji Suzuki (K)

Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan.

Masahiro Tsuboi (M)

Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Chiba, Japan.

Genichiro Ishii (G)

Division of Pathology, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Chiba, Japan; Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Japan. Electronic address: gishii@east.ncc.go.jp.

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Classifications MeSH

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