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Liu B, Zhang ZX, Nie XY, Sun WL, Yan YJ, Fu WH. Clinical outcome and prognostic factors of T4N0M0 colon cancer after R0 resection: A retrospective study. World J Gastrointest Oncol 2024; 16:1869-1877. [PMID: 38764842 PMCID: PMC11099430 DOI: 10.4251/wjgo.v16.i5.1869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 02/14/2024] [Accepted: 03/28/2024] [Indexed: 05/09/2024] Open
Abstract
BACKGROUND Paradoxically, patients with T4N0M0 (stage II, no lymph node metastasis) colon cancer have a worse prognosis than those with T2N1-2M0 (stage III). However, no previous report has addressed this issue. AIM To screen prognostic risk factors for T4N0M0 colon cancer and construct a prognostic nomogram model for these patients. METHODS Two hundred patients with T4N0M0 colon cancer were treated at Tianjin Medical University General Hospital between January 2017 and December 2021, of which 112 patients were assigned to the training cohort, and the remaining 88 patients were assigned to the validation cohort. Differences between the training and validation groups were analyzed. The training cohort was subjected to multivariate analysis to select prognostic risk factors for T4N0M0 colon cancer, followed by the construction of a nomogram model. RESULTS The 3-year overall survival (OS) rates were 86.2% and 74.4% for the training and validation cohorts, respectively. Enterostomy (P = 0.000), T stage (P = 0.001), right hemicolon (P = 0.025), irregular review (P = 0.040), and carbohydrate antigen 199 (CA199) (P = 0.011) were independent risk factors of OS in patients with T4N0M0 colon cancer. A nomogram model with good concordance and accuracy was constructed. CONCLUSION Enterostomy, T stage, right hemicolon, irregular review, and CA199 were independent risk factors for OS in patients with T4N0M0 colon cancer. The nomogram model exhibited good agreement and accuracy.
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Affiliation(s)
- Bang Liu
- Department of General Surgery, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin 300052, China
| | - Zhao-Xiong Zhang
- Department of General Surgery, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin 300052, China
| | - Xin-Yang Nie
- Department of General Surgery, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin 300052, China
| | - Wei-Lin Sun
- Department of General Surgery, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin 300052, China
| | - Yong-Jia Yan
- Department of General Surgery, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin 300052, China
| | - Wei-Hua Fu
- Department of General Surgery, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin 300052, China
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Benzekry S, Schlicke P, Mogenet A, Greillier L, Tomasini P, Simon E. Computational markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain metastases. Clin Exp Metastasis 2024; 41:55-68. [PMID: 38117432 DOI: 10.1007/s10585-023-10245-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 11/07/2023] [Indexed: 12/21/2023]
Abstract
Intracranial progression after curative treatment of early-stage non-small cell lung cancer (NSCLC) occurs from 10 to 50% and is difficult to manage, given the heterogeneity of clinical presentations and the variability of treatments available. The objective of this study was to develop a mechanistic model of intracranial progression to predict survival following a first brain metastasis (BM) event occurring at a time [Formula: see text]. Data included early-stage NSCLC patients treated with a curative intent who had a BM as the first and single relapse site (N = 31). We propose a mechanistic mathematical model able to derive computational markers from primary tumor and BM data at [Formula: see text] and estimate the amount and sizes of (visible and invisible) BMs, as well as their future behavior. These two key computational markers are [Formula: see text], the proliferation rate of a single tumor cell; and [Formula: see text], the per day, per cell, probability to metastasize. The predictive value of these individual computational biomarkers was evaluated. The model was able to correctly describe the number and size of metastases at [Formula: see text] for 20 patients. Parameters [Formula: see text] and [Formula: see text] were significantly associated with overall survival (OS) (HR 1.65 (1.07-2.53) p = 0.0029 and HR 1.95 (1.31-2.91) p = 0.0109, respectively). Adding the computational markers to the clinical ones significantly improved the predictive value of OS (c-index increased from 0.585 (95% CI 0.569-0.602) to 0.713 (95% CI 0.700-0.726), p < 0.0001). We demonstrated that our model was applicable to brain oligoprogressive patients in NSCLC and that the resulting computational markers had predictive potential. This may help lung cancer physicians to guide and personalize the management of NSCLC patients with intracranial oligoprogression.
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Affiliation(s)
- Sébastien Benzekry
- COMPutational Pharmacology and Clinical Oncology Department, Inria Sophia Antipolis - Méditerranée, Faculté de Pharmacie, Cancer Research Center of Marseille, Inserm UMR1068, CNRS UMR7258, Aix Marseille University UM105, 27 Boulevard Jean Moulin, 13005, Marseille, France.
| | - Pirmin Schlicke
- Department of Mathematics, TUM School of Computation, Information and Technology, Technical University of Munich, Garching (Munich), Germany
| | - Alice Mogenet
- Multidisciplinary Oncology and Therapeutic Innovations Department, Assistance Publique - Hôpitaux de Marseille, Aix Marseille University, Marseille, France
| | - Laurent Greillier
- Multidisciplinary Oncology and Therapeutic Innovations Department, Assistance Publique - Hôpitaux de Marseille, Aix Marseille University, Marseille, France
- Aix Marseille University, CNRS, INSERM, CRCM, Marseille, France
| | - Pascale Tomasini
- Multidisciplinary Oncology and Therapeutic Innovations Department, Assistance Publique - Hôpitaux de Marseille, Aix Marseille University, Marseille, France
- Aix Marseille University, CNRS, INSERM, CRCM, Marseille, France
| | - Eléonore Simon
- Multidisciplinary Oncology and Therapeutic Innovations Department, Assistance Publique - Hôpitaux de Marseille, Aix Marseille University, Marseille, France
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Wang TW, Chao HS, Chiu HY, Lu CF, Liao CY, Lee Y, Chen JR, Shiao TH, Chen YM, Wu YT. Radiomics of metastatic brain tumor as a predictive image biomarker of progression-free survival in patients with non-small-cell lung cancer with brain metastasis receiving tyrosine kinase inhibitors. Transl Oncol 2024; 39:101826. [PMID: 37984256 PMCID: PMC10689936 DOI: 10.1016/j.tranon.2023.101826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 10/18/2023] [Accepted: 11/07/2023] [Indexed: 11/22/2023] Open
Abstract
BACKGROUND AND OBJECTIVE Epidermal growth factor receptor (EGFR)-targeted tyrosine kinase inhibitors (TKIs) are the first-line therapy for EGFR-mutant non-small-cell lung cancer (NSCLC). Early prediction of treatment failure in patients with brain metastases treated with EGFR-TKIs may help in making decisions for systemic drug therapy or local brain tumor control. This study examined the predictive power of the radiomics of both brain metastasis tumors and primary lung tumors. We propose a deep learning based CoxCC model based on quantitative brain magnetic resonance imaging (MRI), a prognostic index and clinical data; the model can be used to predict progression-free survival (PFS) after EGFR-TKI therapy in advanced EGFR-mutant NSCLC. METHODS This retrospective single-center study included 271 patients receiving first-line EGFR-TKI targeted therapy in 2018-2019. Among them, 72 patients who had brain metastases before receiving first-line EGFR-TKI treatment. Three radiomic features were extracted from pretreatment brain MRI images. A CoxCC model for the progression risk stratification of EGFR-TKI treatment was proposed on the basis of MRI radiomics, clinical features, and a prognostic index. We performed time-dependent PFS predictions to evaluate the performance of the CoxCC model. RESULTS The CoxCC model based on a prognostic index, clinical features, and radiomic features of brain metastasis exhibited higher performance than clinical features combined with indexes previously proposed for determining the prognosis of brain metastasis, including recursive partitioning analysis, diagnostic-specific graded prognostic assessment, graded prognostic assessment for lung cancer using molecular markers (lung-molGPA), and modified lung-molGPA, with c-index values of 0.75, 0.67, 0.66, 0.65, and 0.65, respectively. The model achieved areas under the curve of 0.88, 0.73, 0.92, and 0.90 for predicting PFS at 3, 6, 9 and 12 months, respectively. PFS significantly differed between the high- and low-risk groups (p < 0.001). CONCLUSIONS For patients with advanced-stage NSCLC with brain metastasis, MRI radiomics of brain metastases may predict PFS. The CoxCC model integrating brain metastasis radiomics, clinical features, and a prognostic index provided reliable multi-time-point PFS predictions for patients with advanced NSCLC and brain metastases receiving EGFR-TKI treatment.
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Affiliation(s)
- Ting-Wei Wang
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Heng-Sheng Chao
- Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Hwa-Yen Chiu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Chia-Feng Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chien-Yi Liao
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yen Lee
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jyun-Ru Chen
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Tsu-Hui Shiao
- Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Yuh-Min Chen
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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Wang Q, Wang Y, He M, Cao H, Zhao J. Research: Construction and validation of elbow function prediction model after supracondylar humerus fracture in children. Medicine (Baltimore) 2023; 102:e36775. [PMID: 38206691 PMCID: PMC10754596 DOI: 10.1097/md.0000000000036775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 12/04/2023] [Indexed: 01/13/2024] Open
Abstract
This article's objectives are to develop a model to predict children's recovery of elbow function following supracondylar fracture, analyze the risk factors affecting those children's elbow function after surgery, and propose a individualized treatment strategy for elbow function in various children. We retrospectively analyzed clinical data from 410 children with supracondylar humerus fracture. A modeling set and a validation set of kids in the included studies were arbitrarily split into 2 groups on a 7:3 basis. To identify statistically significant risk factors, univariate logistic regression analysis was used. Then, multivariate logistic regression was used with the risk factors, and the best logistic regression model was chosen based on sensitivity and accuracy to create a nomogram; A total of 410 children were included in the study according to the inclusion criteria. Among them, there were 248 males and 162 females, and the fracture type: 147 cases of type IIb and 263 cases of type III. There were no significant changes in the afflicted limb's lateral difference, surgical method, onset season, and number of K-wires, according to univariate logistic regression analysis. Age (P < .001), weight (P < .001), height (P < .001), preoperative elbow soft tissue injury (OR = 1.724, 95% CI: 1.040-2.859, P = .035), sex (OR = 2.220, 95% CI: 1.299-3.794, P = .004), fracture classification (Gartland IIb) (OR = 0.252, 95% CI: 0.149-0.426, P < .001), no nerve injury before surgery (OR = 0.304, 95% CI: 0.155-0.596, P = .001), prying technique (OR = 0.464, 95% CI: 0.234-0.920, P = .028), postoperative daily light time > 2 hours (OR = 0.488, 95% CI: 0.249-0.955, P = .036) has a significant difference in univariate analysis; Multivariate regression analysis yielded independent risk factors: fracture classification; No nerve injury before surgery; The daily light duration after surgery was > 2 hours; soft tissue injury; Age, postoperative cast fixation time. The establishment of predictive model is of significance for pediatric orthopedic clinicians in the daily diagnosis and treatment of supracondylar humerus fracture.
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Affiliation(s)
- Qian Wang
- Department of Orthopedics, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, P. R. China
| | - Yu Wang
- Department of Orthopedics, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, P. R. China
| | - Man He
- Department of Rehabilitation, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, P. R. China
| | - Haiying Cao
- Department of Orthopedics, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, P. R. China
| | - Jingxin Zhao
- Department of Orthopedics, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, P. R. China
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Hou F, Hou Y, Sun XD, lv J, Jiang HM, Zhang M, Liu C, Deng ZY. Establishment of a prognostic risk prediction modelfor non-small cell lung cancer patients with brainmetastases: a retrospective study. PeerJ 2023; 11:e15678. [PMID: 37456882 PMCID: PMC10349557 DOI: 10.7717/peerj.15678] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 06/13/2023] [Indexed: 07/18/2023] Open
Abstract
Background Patients with non-small cell lung cancer (NSCLC) who develop brain metastases (BM) have a poor prognosis. This study aimed to construct a clinical prediction model to determine the overall survival (OS) of NSCLC patients with BM. Methods A total of 300 NSCLC patients with BM at the Yunnan Cancer Centre were retrospectively analysed. The prediction model was constructed using the least absolute shrinkage and selection operator-Cox regression. The bootstrap sampling method was employed for internal validation. The performance of our prediction model was compared using recursive partitioning analysis (RPA), graded prognostic assessment (GPA), the update of the graded prognostic assessment for lung cancer using molecular markers (Lung-molGPA), the basic score for BM (BSBM), and tumour-lymph node-metastasis (TNM) staging. Results The prediction models comprising 15 predictors were constructed. The area under the curve (AUC) values for the 1-year, 3-year, and 5-year time-dependent receiver operating characteristic (curves) were 0.746 (0.678-0.814), 0.819 (0.761-0.877), and 0.865 (0.774-0.957), respectively. The bootstrap-corrected AUC values and Brier scores for the prediction model were 0.811 (0.638-0.950) and 0.123 (0.066-0.188), respectively. The time-dependent C-index indicated that our model exhibited significantly greater discrimination compared with RPA, GPA, Lung-molGPA, BSBM, and TNM staging. Similarly, the decision curve analysis demonstrated that our model displayed the widest range of thresholds and yielded the highest net benefit. Furthermore, the net reclassification improvement and integrated discrimination improvement analyses confirmed the enhanced predictive power of our prediction model. Finally, the risk subgroups identified by our prognostic model exhibited superior differentiation of patients' OS. Conclusion The clinical prediction model constructed by us shows promise in predicting OS for NSCLC patients with BM. Its predictability is superior compared with RPA, GPA, Lung-molGPA, BSBM, and TNM staging.
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Affiliation(s)
- Fei Hou
- Department of Nuclear Medicine, Yunnan Cancer Hospital (The Third Affiliated Hospital of Kunming Medical University), Kunming, Yunnan, China
| | - Yan Hou
- Department of General Practice, China Medical University, Shenyang, Liaoning, China
| | - Xiao-Dan Sun
- Department of Publicity, Yunnan Cancer Hospital (The Third Affiliated Hospital of Kunming Medical University), Kunming, Yunnan, China
| | - Jia lv
- Department of Nuclear Medicine, Yunnan Cancer Hospital (The Third Affiliated Hospital of Kunming Medical University), Kunming, Yunnan, China
| | - Hong-Mei Jiang
- Department of Nuclear Medicine, Yunnan Cancer Hospital (The Third Affiliated Hospital of Kunming Medical University), Kunming, Yunnan, China
| | - Meng Zhang
- Department of Nuclear Medicine, Yunnan Cancer Hospital (The Third Affiliated Hospital of Kunming Medical University), Kunming, Yunnan, China
| | - Chao Liu
- Department of Nuclear Medicine, Yunnan Cancer Hospital (The Third Affiliated Hospital of Kunming Medical University), Kunming, Yunnan, China
| | - Zhi-Yong Deng
- Department of Nuclear Medicine, Yunnan Cancer Hospital (The Third Affiliated Hospital of Kunming Medical University), Kunming, Yunnan, China
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Zeng H, Tohidinezhad F, De Ruysscher DKM, Willems YCP, Degens JHRJ, van Kampen-van den Boogaart VEM, Pitz C, Cortiula F, Brandts L, Hendriks LEL, Traverso A. The Association of Gross Tumor Volume and Its Radiomics Features with Brain Metastases Development in Patients with Radically Treated Stage III Non-Small Cell Lung Cancer. Cancers (Basel) 2023; 15:cancers15113010. [PMID: 37296973 DOI: 10.3390/cancers15113010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 05/22/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
PURPOSE To identify clinical risk factors, including gross tumor volume (GTV) and radiomics features, for developing brain metastases (BM) in patients with radically treated stage III non-small cell lung cancer (NSCLC). METHODS Clinical data and planning CT scans for thoracic radiotherapy were retrieved from patients with radically treated stage III NSCLC. Radiomics features were extracted from the GTV, primary lung tumor (GTVp), and involved lymph nodes (GTVn), separately. Competing risk analysis was used to develop models (clinical, radiomics, and combined model). LASSO regression was performed to select radiomics features and train models. Area under the receiver operating characteristic curves (AUC-ROC) and calibration were performed to assess the models' performance. RESULTS Three-hundred-ten patients were eligible and 52 (16.8%) developed BM. Three clinical variables (age, NSCLC subtype, and GTVn) and five radiomics features from each radiomics model were significantly associated with BM. Radiomic features measuring tumor heterogeneity were the most relevant. The AUCs and calibration curves of the models showed that the GTVn radiomics model had the best performance (AUC: 0.74; 95% CI: 0.71-0.86; sensitivity: 84%; specificity: 61%; positive predictive value [PPV]: 29%; negative predictive value [NPV]: 95%; accuracy: 65%). CONCLUSION Age, NSCLC subtype, and GTVn were significant risk factors for BM. GTVn radiomics features provided higher predictive value than GTVp and GTV for BM development. GTVp and GTVn should be separated in clinical and research practice.
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Affiliation(s)
- Haiyan Zeng
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
| | - Fariba Tohidinezhad
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
| | - Dirk K M De Ruysscher
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
| | - Yves C P Willems
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
| | - Juliette H R J Degens
- Department of Respiratory Medicine, Zuyderland Medical Center, 6419 PC Heerlen, The Netherlands
| | | | - Cordula Pitz
- Department of Pulmonary Diseases, Laurentius Hospital, 6043 CV Roermond, The Netherlands
| | - Francesco Cortiula
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
- Department of Medical Oncology, University Hospital of Udine, 33100 Udine, Italy
| | - Lloyd Brandts
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands
| | - Lizza E L Hendriks
- Department of Pulmonary Diseases, Maastricht, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, 6202 AZ Maastricht, The Netherlands
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
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Wang Y, Lang J, Zuo JZ, Dong Y, Hu Z, Xu X, Zhang Y, Wang Q, Yang L, Wong STC, Wang H, Li H. The radiomic-clinical model using the SHAP method for assessing the treatment response of whole-brain radiotherapy: a multicentric study. Eur Radiol 2022; 32:8737-8747. [PMID: 35678859 DOI: 10.1007/s00330-022-08887-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 05/11/2022] [Accepted: 05/16/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To develop and validate a pretreatment magnetic resonance imaging (MRI)-based radiomic-clinical model to assess the treatment response of whole-brain radiotherapy (WBRT) by using SHapley Additive exPlanations (SHAP), which is derived from game theory, and can explain the output of different machine learning models. METHODS We retrospectively enrolled 228 patients with brain metastases from two medical centers (184 in the training cohort and 44 in the validation cohort). Treatment responses of patients were categorized as a non-responding group vs. a responding group according to the Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria. For each tumor, 960 features were extracted from the MRI sequence. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. A support vector machine (SVM) model incorporating clinical factors and radiomic features wase used to construct the radiomic-clinical model. SHAP method explained the SVM model by prioritizing the importance of features, in terms of assessment contribution. RESULTS Three radiomic features and three clinical factors were identified to build the model. Radiomic-clinical model yielded AUCs of 0.928 (95%CI 0.901-0.949) and 0.851 (95%CI 0.816-0.886) for assessing the treatment response in the training cohort and validation cohort, respectively. SHAP summary plot illustrated the feature's value affected the feature's impact attributed to model, and SHAP force plot showed the integration of features' impact attributed to individual response. CONCLUSION The radiomic-clinical model with the SHAP method can be useful for assessing the treatment response of WBRT and may assist clinicians in directing personalized WBRT strategies in an understandable manner. KEY POINTS • Radiomic-clinical model can be useful for assessing the treatment response of WBRT. • SHAP could explain and visualize radiomic-clinical machine learning model in a clinician-friendly way.
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Affiliation(s)
- Yixin Wang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China.,University of Science and Technology of China, Hefei, 230026, People's Republic of China.,Department of Oncology, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China
| | - Jinwei Lang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China.,University of Science and Technology of China, Hefei, 230026, People's Republic of China
| | - Joey Zhaoyu Zuo
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China.,University of Science and Technology of China, Hefei, 230026, People's Republic of China
| | - Yaqin Dong
- Department of Radiation Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, People's Republic of China
| | - Zongtao Hu
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China.,Department of Oncology, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China
| | - Xiuli Xu
- Department of Oncology, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China
| | - Yongkang Zhang
- Department of Oncology, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China
| | - Qinjie Wang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China.,University of Science and Technology of China, Hefei, 230026, People's Republic of China
| | - Lizhuang Yang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China.,University of Science and Technology of China, Hefei, 230026, People's Republic of China.,Department of Oncology, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China
| | - Stephen T C Wong
- Department of Systems Medicine and Bioengineering, Houston Methodist Cancer Center, Weill Cornell Medical College, Houston, TX, 77030, USA
| | - Hongzhi Wang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China. .,University of Science and Technology of China, Hefei, 230026, People's Republic of China. .,Department of Oncology, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China.
| | - Hai Li
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China. .,University of Science and Technology of China, Hefei, 230026, People's Republic of China. .,Department of Oncology, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China.
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8
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Cong P, Qiu Q, Li X, Sun Q, Yu X, Yin Y. Development and validation a radiomics nomogram for diagnosing occult brain metastases in patients with stage IV lung adenocarcinoma. Transl Cancer Res 2022; 10:4375-4386. [PMID: 35116296 PMCID: PMC8797466 DOI: 10.21037/tcr-21-702] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 08/09/2021] [Indexed: 12/27/2022]
Abstract
Background To develop and validate a radiomics model using computed tomography (CT) images acquired from the first diagnosis to estimate the status of occult brain metastases (BM) in patients with stage IV lung adenocarcinoma (LADC). Methods One hundred and ninety-three patients who were first diagnosed with stage IV LADC were enrolled and divided into a training cohort (n=135) and a validation cohort (n=58). Then, 725 radiomic features were extracted from contoured primary tumor volumes of LADCs. Intra- and interobserver reliabilities were calculated, and the least absolute shrinkage and selection operator (LASSO) was applied for feature selection. Subsequently, a radiomics signature (Rad-Score) was built. To improve performance, a nomogram incorporating a radiomics signature and an independent clinical predictor was developed. Finally, the established signature and nomogram were assessed using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Both empirical and α-binomial model-based ROCs and PRCs were plotted, and the area under the curve (AUC) and average precision (AP) of ROCs and PRCs were calculated and compared. Results A radiomics signature and Rad-Score were constructed using eight radiomic features, and these had significant correlations with occult BM status. A nomogram was developed by incorporating a Rad-Score and the primary tumor location. The nomogram yielded an optimal AUC of 0.911 [95% confidence interval (CI): 0.903–0.919] and an AP of 0.885 (95% CI: 0.876–0.894) in the training cohort, and an AUC of 0.873 (95% CI: 0.866–0.80) and an AP of 0.827 (95% CI: 0.820–0.834) in the validation cohort using α-binomial model-based method. The calibration curve demonstrated that the nomogram showed high agreement between the actual occult BM probability and predicted by the nomogram (P=0.427). Conclusions The nomogram incorporating a radiomics signature and a clinical risk factor achieved optimal performance after holistic assessment using unbiased indexes for diagnosing occult BM of patients who were first diagnosed with stage IV LADC.
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Affiliation(s)
- Ping Cong
- Department of Oncology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qingtao Qiu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xingchao Li
- Department of Oncology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qian Sun
- Department of Oncology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xiaoming Yu
- Department of Oncology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yong Yin
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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Luo Y, Li Y, Zhang Y, Zhang J, Liang M, Jiang L, Guo L. Parameter tuning in machine learning based on radiomics biomarkers of lung cancer. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:477-490. [PMID: 35342074 DOI: 10.3233/xst-211096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
BACKGROUND Lung cancer is one of the most common cancers, and early diagnosis and intervention can improve cancer cure rate. OBJECTIVE To improve predictive performance of radiomics features for lung cancer by tuning the machine learning model parameters. METHODS Using a dataset involving 263 cases (125 benign and 138 malignant) acquired from our hospital, each classifier model is trained and tested using 237 and 26 cases, respectively. We initially extract 867 radiomics features of CT images for model development and then test 10 feature selections and 7 models to determine the best method. We further tune the parameter of the final model to reach the best performance. The adjusted final model is then validated using 224 cases acquired from Lung Image Database Consortium (LIDC) dataset (64 benign and 160 malignant) with the same set of selected radiomics features. RESULTS During model development, the feature selection via concave minimization method show the best performance of area under ROC curve (AUC = 0.765), followed by l0-norm regularization (AUC = 0.741) and Fisher discrimination criterion (AUC = 0.734). Support vector machine (SVM) and random forest (RF) are the top two machine learning algorithms showing the best performance (AUC = 0.765 and 0.734, respectively), using by the default parameter. After parameter tuning, SVM with linear kernel achieves the best performance (AUC = 0.837), whereas the best tuned RF with the number of trees is 510 and yields a slightly lower performance (AUC = 0.775) in 26 test samples data. During model validation, the SVM and RF models yield AUC = 0.78 and 0.77, respectively. CONCLUSION Appropriate quantitative radiomics features and accurate parameters can improve the model's performance to predict lung cancer.
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Affiliation(s)
- Yuan Luo
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Yifan Li
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Yuwei Zhang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Jianwei Zhang
- Department of Radiology, Tianjin Baodi Hospital, Tianjin, China
| | - Meng Liang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Lin Jiang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Li Guo
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
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10
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Lv J, Liu YY, Jia YT, He JL, Dai GY, Guo P, Zhao ZL, Zhang YN, Li ZX. A nomogram model for predicting prognosis of obstructive colorectal cancer. World J Surg Oncol 2021; 19:337. [PMID: 34857001 PMCID: PMC8638364 DOI: 10.1186/s12957-021-02445-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 10/23/2021] [Indexed: 02/07/2023] Open
Abstract
Background The prognosis of obstructive colorectal cancer (oCRC) is worse than that of nonobstructive colorectal cancer. However, no previous study has established an individualized prediction model for the prognosis of patients with oCRC. We aimed to screen the factors that affect the prognosis of oCRC and to use these findings to establish a nomogram model that predicts the individual prognosis of patients with oCRC. Methods This retrospective study collected data of 181 patients with oCRC from three medical hospitals between February 2012 and December 2017. Among them, 129 patients from one hospital were used as the training cohort. Univariate and multivariate analyses were used in this training cohort to select independent risk factors that affect the prognosis of oCRC, and a nomogram model was established. The other 52 patients from two additional hospitals were used as the validation cohort to verify the model. Results Multivariate analysis showed that carcinoembryonic antigen level (p = 0.037, hazard ratio [HR] = 2.872 [1.065–7.740]), N stage (N1 vs. N0, p = 0.028, HR = 3.187 [1.137–8.938]; N2 vs. N0, p = 0.010, HR = 4.098 [1.393–12.051]), and surgical procedures (p = 0.002, HR = 0.299 [0.139–0.643]) were independent prognostic factors of overall survival in patients with oCRC. These factors were used to construct the nomogram model, which showed good concordance and accuracy. Conclusion Carcinoembryonic antigen, N stage, and surgical method are independent prognostic factors for overall survival in patients with oCRC, and the nomogram model can visually display these results.
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Affiliation(s)
- Jian Lv
- Department of Emergency, Hebei General Hospital, No. 348 Heping West Road, Shijiazhuang, 050051, Hebei, China
| | - Yuan Yuan Liu
- Department of Anorectal Surgery, Huanghua General Hospital, No. 262 Xinhua Road, Huanghua, 061100, Hebei, China
| | - Yi Tao Jia
- Department of Oncology, Hebei General Hospital, Shijiazhuang, 050051, Hebei, China
| | - Jing Li He
- Second Department of Surgery, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Guang Yao Dai
- Department of Anorectal Surgery, The First Hospital of Shijiazhuang, No. 36, Fanxi Road, Shijiazhuang, 050011, Hebei, China
| | - Peng Guo
- Department of Plastic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, Hebei, China
| | - Zhao Long Zhao
- Department of Anesthesiology, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Yan Ni Zhang
- Department of General Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Zhong Xin Li
- Department of General Surgery, The First Affiliated Hospital of Hebei Medical University, No. 89 Donggang Road, Shijiazhuang, 050000, Hebei, China.
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