Cancer Res Treat.  2025 Jan;57(1):57-69. 10.4143/crt.2024.251.

Enhancing Identification of High-Risk cN0 Lung Adenocarcinoma Patients Using MRI-Based Radiomic Features

• Kim H
• Kim J
• Hwang S
• Oh YJ
• Ahn JH
• Kim MJ
• Hong TH
• Park SG
• Choi JY
• Kim HK
• Kim J
• Shin S
• Lee HY

• KMID: 2564581
• DOI: http://doi.org/10.4143/crt.2024.251

Abstract

Purpose
This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns.
Materials and Methods
As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features.
Results
Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively).
Conclusion
Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Keyword

Lung neoplasms; Magnetic resonance imaging; Prospective studies; Machine learning

Figure

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  Fig. 1. Flow diagram. CT, computed tomography; MRI, magnetic resonance imging; PET, positron emission tomography; STAS, spread-through-air-space; SUVmax, maximum standardized uptake value. a)By most predominant type.

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  Fig. 2. Kaplan-Meier plot of 4-year disease-free survival in magnetic resonance (MR)–eligible and MR-non-eligible groups. Disease-free survival of MR-eligible groups are marked with red line and that of MR-non-eligible groups are marked with blue line. 95% Confidence interval is presented as red- and blue-colored areas around the line.

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  Fig. 3. Kaplan-Meier plot of 4-year overall survival in magnetic resonance (MR)–eligible and MR-non-eligible groups. Disease-free survival of MR-eligible groups are marked with red line and that of MR-non-eligible groups are marked with blue line. 95% confidence interval is presented as red- and blue-colored areas around the line.

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  Fig. 4. Pathology-radiology correlation for patient with poorly differentiated lung cancer containing micropapillary and acinar components. (A) Computed tomography (CT) axial image of tumor. (B) Positron emission tomography (PET) uptake of tumor with focal increased uptake (maximum standardized uptake value: 3.5) in the lower lateral margin (arrow). (C) Gross specimen showing good correlation with tumor margin in CT image. (D) Contrast-enhanced T1 image of tumor. (E) T2-weighted image of tumor showing focal low signal intensity in the lower lateral area. (F) Apparent diffusion coefficient (ADC) map of tumor showing focal low ADC value in the lower lateral area and relatively high ADC values in other regions. (G) Photomicrograph of whole slide image showing good correlation with CT and gross specimen. (H) High-resolution image (×200) of box marked with arrow in G. Histology shows micropapillary pattern, and outside the tumor margin, the spread through air space pattern is also noted (blue arrow). This area corresponds to the posterolateral area of the tumor with hot PET uptake in B (arrow) and a low ADC value in F (arrow). (I) High-resolution image (×100) of box marked with arrowhead in G. Histology shows an acinar pattern of lung adenocarcinoma, and this area corresponds to the posterior margin of the tumor with low PET uptake in B and a high ADC value (arrowhead) in F. Histology slides (G-I) were stained with hematoxylin and eosin.

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  Fig. 5. Pathology-radiology correlation for patient with poorly differentiated lung cancer containing solid and spread-through-air-space (STAS) components. (A) Computed tomography (CT) axial image of tumor. (B) Positron emission tomography uptake of tumor with homogeneous hot uptake (maximum standardized uptake value: 17.3). (C) Gross specimen showing good correlation of tumor margin with CT image in A. (D) Contrast-enhanced axial T1 image of tumor. (E) Axial T2-weighted image of tumor. (F) Apparent diffusion coeff icient (ADC) map of tumor showing focal low ADC value in the postero-medial and lateral area and relatively high ADC values in other regions. (G) Photomicrograph of whole slide image showing good correlation with CT and gross specimen. (H) High-resolution image (×200) of box marked with arrowhead in G. Histology shows a solid pattern, and this corresponds to the posterolateral area of the tumor with a low ADC value (arrowhead) in F. (I) High-resolution image (×100) of box marked with arrow in G. The STAS pattern (arrow) outside the tumor margin (blue line) corresponds to the posteromedial area of the tumor with a low ADC value (arrow) in F. Histology slides (G-I) were stained with hematoxylin and eosin.

Reference

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