Academic Journal
Histopathologic Basis for a Chest CT Deep Learning Survival Prediction Model in Patients with Lung Adenocarcinoma
| العنوان: | Histopathologic Basis for a Chest CT Deep Learning Survival Prediction Model in Patients with Lung Adenocarcinoma |
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| المؤلفون: | Nam, Ju Gang, Park, Samina, Park, Chang Min, Jeon, Yoon Kyung, Chung, Doo Hyun, Goo, Jin Mo, Kim, Young Tae, Kim, Hyungjin |
| المساهمون: | Park, Samina, Park, Chang Min, Jeon, Yoon Kyung, Chung, Doo Hyun, Goo, Jin Mo, Kim, Young Tae |
| بيانات النشر: | Radiological Society of North America |
| سنة النشر: | 2023 |
| المجموعة: | Seoul National University: S-Space |
| مصطلحات موضوعية: | POSITRON-EMISSION-TOMOGRAPHY, STAGING PROJECT PROPOSALS, FORTHCOMING 8TH EDITION, TNM CLASSIFICATION, TUMOR SIZE, CANCER, EGFR, CHEMOTHERAPY |
| الوصف: | Background: A preoperative CT-based deep learning (DL) prediction model was proposed to estimate disease-free survival in patients with resected lung adenocarcinoma. However, the black-box nature of DL hinders interpretation of its results. Purpose: To provide histopathologic evidence underpinning the DL survival prediction model and to demonstrate the feasibility of the model in identifying patients with histopathologic risk factors through unsupervised clustering and a series of regression analyses. Materials and Methods: For this retrospective study, data from patients who underwent curative resection for lung-adenocarcinoma without neoadjuvant therapy from January 2016 to September 2020 were collected from a tertiary care center. Seven-histopathologic risk factors for the resected adenocarcinoma were documented: the aggressive adenocarcinoma subtype (cribriform, morular, solid, or micropapillary-predominant subtype); mediastinal nodal metastasis (pN2); presence of lymphatic, venous, and perineural invasion; visceral pleural invasion (VPI); and EGFR mutation status. Unsupervised clustering using 80 DL modeldriven CT-features was performed, and associations between the patient clusters and the histopathologic features were analyzed. Multivariable regression analyses were performed to investigate the added value of the DL model output to the semantic CT -features (clinical T category and radiologic nodule type [ie, solid or subsolid]) for histopathologic associations. Results: A total of 1667 patients (median age, 64 years [IQR, 57-71 years]; 975 women) were evaluated. Unsupervised patient clusters 3 and 4 were associated with all histopathologic risk factors (P<.01) except for EGFR mutation status (P =.30 for cluster 3). After multivariable adjustment, model output was associated with the aggressive adenocarcinoma subtype (odds ratio [OR], 1.03; 95% CI: 1.002, 1.05; P =.03), venous invasion (OR, 1.03; 95% CI: 1.004, 1.06; P =.02), and VPI (OR, 1.08; 95% CI: 1.06, 1.10; P<.001), independently of the ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | unknown |
| تدمد: | 0033-8419 |
| Relation: | Radiology, Vol.305 No.2, pp.441-451; https://hdl.handle.net/10371/205414; 000923601600038; 2-s2.0-85140658854; 179753 |
| DOI: | 10.1148/radiol.213262 |
| الاتاحة: | https://hdl.handle.net/10371/205414 https://doi.org/10.1148/radiol.213262 |
| رقم الانضمام: | edsbas.E1BC57ED |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 00338419 |
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| DOI: | 10.1148/radiol.213262 |