Elaboration of Multiparametric <scp>MRI</scp>‐Based Radiomics Signature for the Preoperative Quantitative Identification of the Histological Grade in Patients With Non‐Small‐Cell Lung Cancer

Tang, Xing; Bai, Guoyan; Wang, Hong; Guo, Fan; Yin, Hong

doi:10.1002/jmri.28051

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…Instead, the radiomics approach was used to predict the micropapillary pattern that was reported to have a poor prognosis in a previous study ( 31 ). In comparison to multiparametric MRI-based radiomics approach for NSCLC grading (AUC 0.767) and contrast-enhanced CT-based radiomics signature for prediction of tumor differentiation degree (low and high degree, AUC 0.782) ( 32 , 33 ), the selected representative LR algorithm for IAC grade stratification in this study achieved better performance on both internal and independent testing sets (averaged AUC 0.928 and 0.837) and equivalent performance on external test set (averaged AUC 0.748), indicating the potential of CT-based radiomics approach in predicting histologic grades of IAC. Meanwhile, we noticed a dramatically decreased Kappa coefficient of LR algorithm on external test set, which caused by the miss classifications of grade1 and 3 into grade 2, suggesting the need of further improvement for IAC grading stratification algorithms by including more balanced data.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-enhanced radiomics for histologic classification and grade stratification of stage IA lung adenocarcinoma: a multicenter study

et al. 2023

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BackgroundPreoperative prediction models for histologic subtype and grade of stage IA lung adenocarcinoma (LUAD) according to the update of the WHO Classification of Tumors of the Lung in 2021 and the 2020 new grade system are yet to be explored. We aim to develop the noninvasive pathology and grade evaluation approach for patients with stage IA LUAD via CT-based radiomics approach and evaluate their performance in clinical practice.MethodsChest CT scans were retrospectively collected from patients who were diagnosed with stage IA LUAD and underwent complete resection at two hospitals. A deep learning segmentation algorithm was first applied to assist lesion delineation. Expansion strategies such as bounding-box annotations were further applied. Radiomics features were then extracted and selected followed by radiomics modeling based on four classic machine learning algorithms for histologic subtype classification and grade stratification. The area under the receiver operating characteristic curve (AUC) was used to evaluate model performance.ResultsThe study included 294 and 145 patients with stage IA LUAD from two hospitals for radiomics analysis, respectively. For classification of four histological subtypes, multilayer perceptron (MLP) algorithm presented no annotation strategy preference and achieved the average AUC of 0.855, 0.922, and 0.720 on internal, independent, and external test sets with 1-pixel expansion annotation. Bounding-box annotation strategy also enabled MLP an acceptable and stable accuracy among test sets. Meanwhile, logistic regression was selected for grade stratification and achieved the average AUC of 0.928, 0.837, and 0.748 on internal, independent, and external test sets with optimal annotation strategies.ConclusionsDL-enhanced radiomics models had great potential to predict the fine histological subtypes and grades of early-stage LUADs based on CT images, which might serve as a promising noninvasive approach for the diagnosis and management of early LUADs.

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Section: Discussionmentioning

confidence: 99%