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Kuang B, Zhang J, Zhang M, Xia H, Qiang G, Zhang J. Advancing NSCLC pathological subtype prediction with interpretable machine learning: a comprehensive radiomics-based approach. Front Med (Lausanne) 2024; 11:1413990. [PMID: 38841579 PMCID: PMC11150591 DOI: 10.3389/fmed.2024.1413990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 05/10/2024] [Indexed: 06/07/2024] Open
Abstract
Objective This research aims to develop and assess the performance of interpretable machine learning models for diagnosing three histological subtypes of non-small cell lung cancer (NSCLC) utilizing CT imaging data. Methods A retrospective cohort of 317 patients diagnosed with NSCLC was included in the study. These individuals were randomly segregated into two groups: a training set comprising 222 patients and a validation set with 95 patients, adhering to a 7:3 ratio. A comprehensive extraction yielded 1,834 radiomic features. For feature selection, statistical methodologies such as the Mann-Whitney U test, Spearman's rank correlation, and one-way logistic regression were employed. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was utilized. The study designed three distinct models to predict adenocarcinoma (ADC), squamous cell carcinoma (SCC), and large cell carcinoma (LCC). Six different classifiers, namely Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, eXtreme Gradient Boosting (XGB), and LightGBM, were deployed for model training. Model performance was gauged through accuracy metrics and the area under the receiver operating characteristic (ROC) curves (AUC). To interpret the diagnostic process, the Shapley Additive Explanations (SHAP) approach was applied. Results For the ADC, SCC, and LCC groups, 9, 12, and 8 key radiomic features were selected, respectively. In terms of model performance, the XGB model demonstrated superior performance in predicting SCC and LCC, with AUC values of 0.789 and 0.848, respectively. For ADC prediction, the Random Forest model excelled, showcasing an AUC of 0.748. Conclusion The constructed machine learning models, leveraging CT imaging, exhibited robust predictive capabilities for SCC, LCC, and ADC subtypes of NSCLC. These interpretable models serve as substantial support for clinical decision-making processes.
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Affiliation(s)
- Bingling Kuang
- Department of Pathology, Affiliated Cancer Hospital and Institution of Guangzhou Medical University, Guangzhou, China
- Nanshan College, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Jingxuan Zhang
- Nanshan College, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Mingqi Zhang
- The Second Clinical School of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Haoming Xia
- School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Guangliang Qiang
- Department of Thoracic Surgery, Peking University Third Hospital, Beijing, China
| | - Jiangyu Zhang
- Department of Pathology, Affiliated Cancer Hospital and Institution of Guangzhou Medical University, Guangzhou, China
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Huang D, Lin C, Jiang Y, Xin E, Xu F, Gan Y, Xu R, Wang F, Zhang H, Lou K, Shi L, Hu H. Radiomics model based on intratumoral and peritumoral features for predicting major pathological response in non-small cell lung cancer receiving neoadjuvant immunochemotherapy. Front Oncol 2024; 14:1348678. [PMID: 38585004 PMCID: PMC10996281 DOI: 10.3389/fonc.2024.1348678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 03/06/2024] [Indexed: 04/09/2024] Open
Abstract
Objective To establish a radiomics model based on intratumoral and peritumoral features extracted from pre-treatment CT to predict the major pathological response (MPR) in patients with non-small cell lung cancer (NSCLC) receiving neoadjuvant immunochemotherapy. Methods A total of 148 NSCLC patients who underwent neoadjuvant immunochemotherapy from two centers (SRRSH and ZCH) were retrospectively included. The SRRSH dataset (n=105) was used as the training and internal validation cohort. Radiomics features of intratumoral (T) and peritumoral regions (P1 = 0-5mm, P2 = 5-10mm, and P3 = 10-15mm) were extracted from pre-treatment CT. Intra- and inter- class correlation coefficients and least absolute shrinkage and selection operator were used to feature selection. Four single ROI models mentioned above and a combined radiomics (CR: T+P1+P2+P3) model were established by using machine learning algorithms. Clinical factors were selected to construct the combined radiomics-clinical (CRC) model, which was validated in the external center ZCH (n=43). The performance of the models was assessed by DeLong test, calibration curve and decision curve analysis. Results Histopathological type was the only independent clinical risk factor. The model CR with eight selected radiomics features demonstrated a good predictive performance in the internal validation (AUC=0.810) and significantly improved than the model T (AUC=0.810 vs 0.619, p<0.05). The model CRC yielded the best predictive capability (AUC=0.814) and obtained satisfactory performance in the independent external test set (AUC=0.768, 95% CI: 0.62-0.91). Conclusion We established a CRC model that incorporates intratumoral and peritumoral features and histopathological type, providing an effective approach for selecting NSCLC patients suitable for neoadjuvant immunochemotherapy.
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Affiliation(s)
- Dingpin Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Chen Lin
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Yangyang Jiang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Enhui Xin
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Fangyi Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yi Gan
- Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Rui Xu
- DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China
- DUT-RU Co-Research Center of Advanced Information Computing Technology (ICT) for Active Life, Dalian University of Technology, Dalian, Liaoning, China
| | - Fang Wang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Haiping Zhang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Kaihua Lou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lei Shi
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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Al-Awadhi A, Alnaqbi S, Albawardi A. Long-Lasting Complete Remission in a Patient With Metastatic Metaplastic Breast Cancer Treated With Immune Checkpoint Inhibitor and Chemotherapy: A Case Report and a Review of the Literature. Cureus 2024; 16:e53419. [PMID: 38314378 PMCID: PMC10834218 DOI: 10.7759/cureus.53419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/01/2024] [Indexed: 02/06/2024] Open
Abstract
Metaplastic breast cancer (MpBC) is a rare form of breast cancer known for suboptimal response to chemotherapy, high recurrence rate, poor prognosis, and limited treatment options. Recent studies have reported that MpBC has high expression of programmed death ligand 1 and tumor-infiltrating lymphocytes, indicating the potential effectiveness of immunotherapy (IO) in MpBC. In addition, several reports have demonstrated the activity of IO in MpBC. In this case report, we present a case of recurrent MpBC that achieved durable, rapid, complete remission with atezolizumab (anti-PD-L1) and nab-paclitaxel with a continued response even after discontinued therapy.
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Affiliation(s)
| | - Safia Alnaqbi
- Department of Medical Oncology, Tawam Hospital, Al Ain, ARE
| | - Alia Albawardi
- Department of Pathology, United Arab Emirates University, Al Ain, ARE
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