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Li Y, Han D, Shen C, Duan X. Construction of a comprehensive predictive model for axillary lymph node metastasis in breast cancer: a retrospective study. BMC Cancer 2023; 23:1028. [PMID: 37875818 PMCID: PMC10594862 DOI: 10.1186/s12885-023-11498-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/09/2023] [Indexed: 10/26/2023] Open
Abstract
PURPOSE The accurate assessment of axillary lymph node metastasis (LNM) in early-stage breast cancer (BC) is of great importance. This study aimed to construct an integrated model based on clinicopathology, ultrasound, PET/CT, and PET radiomics for predicting axillary LNM in early stage of BC. MATERIALS AND METHODS 124 BC patients who underwent 18 F-fluorodeoxyglucose (18 F-FDG) PET/CT and whose diagnosis were confirmed by surgical pathology were retrospectively analyzed and included in this study. Ultrasound, PET and clinicopathological features of all patients were analyzed, and PET radiomics features were extracted to establish an ultrasound model (clinicopathology and ultrasound; model 1), a PET model (clinicopathology, ultrasound, and PET; model 2), and a comprehensive model (clinicopathology, ultrasound, PET, and radiomics; model 3), and the diagnostic efficacy of each model was evaluated and compared. RESULTS The T stage, US_BIRADS, US_LNM, and PET_LNM in the positive axillary LNM group was significantly higher than that of in the negative LNM group (P = 0.013, P = 0.049, P < 0.001, P < 0.001, respectively). Radiomics score for predicting LNM (RS_LNM) for the negative LNM and positive LNM were statistically significant difference (-1.090 ± 0.448 vs. -0.693 ± 0.344, t = -4.720, P < 0.001), and the AUC was 0.767 (95% CI: 0.674-0.861). The ROC curves showed that model 3 outperformed model 1 for the sensitivity (model 3 vs. model 1, 82.86% vs. 48.57%), and outperformed model 2 for the specificity (model 3 vs. model 2, 82.02% vs. 68.54%) in the prediction of LNM. The AUC of mode 1, model 2 and model 3 was 0.687, 0.826 and 0.874, and the Delong test showed the AUC of model 3 was significantly higher than that of model 1 and model 2 (P < 0.05). Decision curve analysis showed that model 3 resulted in a higher degree of net benefit for all the patients than model 1 and model 2. CONCLUSION The use of a comprehensive model based on clinicopathology, ultrasound, PET/CT, and PET radiomics can effectively improve the diagnostic efficacy of axillary LNM in BC. TRIAL REGISTRATION This study was registered at ClinicalTrials Gov (number NCT05826197) on 7th, May 2023.
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Affiliation(s)
- Yan Li
- PET/CT Center, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an Shaanxi, 710061, China
| | - Dong Han
- PET/CT Center, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an Shaanxi, 710061, China
| | - Cong Shen
- PET/CT Center, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an Shaanxi, 710061, China
| | - Xiaoyi Duan
- PET/CT Center, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an Shaanxi, 710061, China.
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Incremental value of PET primary lesion-based radiomics signature to conventional metabolic parameters and traditional risk factors for preoperative prediction of lymph node metastases in gastric cancer. Abdom Radiol (NY) 2023; 48:510-518. [PMID: 36418614 DOI: 10.1007/s00261-022-03738-4] [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: 04/26/2022] [Revised: 10/30/2022] [Accepted: 10/31/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND Precise preoperative prediction of lymph node metastasis (LNM) is crucial for optimal diagnosis and treatment in patients with gastric cancer (GC), in which existing imaging methods have certain limitations. We hypothesized that PET primary lesion-based radiomics signature could provide incremental value to conventional metabolic parameters and traditional risk indicators in predicting LNM in patients with GC. METHODS This retrospective study was performed in 127 patients with GC who underwent preoperative PET/CT. Basic clinical data and PET conventional metabolic parameters were collected. Radiomics signature was constructed by the least absolute shrinkage and selection operator algorithm (LASSO) logistic regression. Based on the postoperative histological results, the patients were divided into LNM group and non-lymph node metastasis (NLNM) group. Receiver-operating characteristic (ROC) was used to evaluate the discriminatory ability of Radiomics score (Rad-score) for predicting LNM and determine whether adding Rad-score to PET conventional metabolic parameters and traditional risk factors could improve the predictive value in LNM. The Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were calculated to further confirm the incremental value of Rad-score for predicting LNM in GC. RESULTS The LNM group had higher Rad-score than NLNM group [(0.35 (-0.13-0.85) vs. -0.61 (-1.92-0.18), P < 0 .001)]. After adjusted for gender, age, BMI, and FBG, multivariable logistic regression analysis illustrated that Rad-score (OR: 6.38, 95% CI: 2.73-14.91, P < 0.0001) was independent risk factors for LNM in GC. Adding PET conventional parameters to traditional risk factors increased the predictive value of LNM in GC (AUC 0.751 vs 0.651, P = 0.02). Additional inclusion of Rad-score to conventional metabolic parameters and traditional risk indicators significantly improved the AUC (0.882 vs 0.751; P = 0.006). Bootstrap resampling (times = 500) was used for internal verification, 95% confidence interval (CI) was 0.802-0.948, with the sensitivity equaled to 89.5%, and positive predictive value (PPV) was 93.5%. When Rad-score was added to conventional metabolic parameters and traditional risk indicators, net reclassification improvement (NRI) was 0.293 (P = 0.0040) and integrated discrimination improvement (IDI) was 0.293 (P = 0.0045). CONCLUSION In GC patients, PET Radiomics signature of the primary lesion-based was significantly associated with LNM and could improve the prediction of LNM above PET conventional metabolic parameters and traditional risk factors, which could provide incremental value for individual diagnosis and treatment of GC.
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Prognostic value of the metabolic score obtained via [ 18F]FDG PET/CT and a new prognostic staging system for gastric cancer. Sci Rep 2022; 12:20681. [PMID: 36450778 PMCID: PMC9712281 DOI: 10.1038/s41598-022-24877-0] [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: 06/13/2022] [Accepted: 11/22/2022] [Indexed: 12/05/2022] Open
Abstract
We developed and validated a new staging system that includes metabolic information from pretreatment [18F]Fluorodeoxyglucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) for predicting disease-specific survival (DSS) in gastric cancer (GC) patients. Overall, 731 GC patients undergoing preoperative [18F]FDG PET/CT were enrolled and divided into the training (n = 543) and validation (n = 188) cohorts. A metabolic score (MS) was developed by combining the maximum standardized uptake value (SUVmax) of the primary tumor (T_SUVmax) and metastatic lymph node (N_SUVmax). A new staging system incorporating the MS and tumor-node-metastasis (TNM) stage was developed using conditional inference tree analysis. The MS was stratified as follows: score 1 (T_SUVmax ≤ 4.5 and N_SUVmax ≤ 1.9), score 2 (T_SUVmax > 4.5 and N_SUVmax ≤ 1.9), score 3 (T_SUVmax ≤ 4.5 and N_SUVmax > 1.9), and score 4 (T_SUVmax > 4.5 and N_SUVmax > 1.9) in the training cohort. The new staging system yielded five risk categories: category I (TNM I, II and MS 1), category II (TNM I, II and MS 2), category III (TNM I, II and MS ≥ 3), category IV (TNM III, IV and MS ≤ 3), and category V (TNM III, IV and MS 4) in the training cohort. DSS differed significantly between both staging systems; the new staging system showed better prognostic performance in both training and validation cohorts. The MS was an independent prognostic factor for DSS, and discriminatory power of the new staging system for DSS was better than that of the conventional TNM staging system alone.
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Ma D, Zhang Y, Shao X, Wu C, Wu J. PET/CT for Predicting Occult Lymph Node Metastasis in Gastric Cancer. Curr Oncol 2022; 29:6523-6539. [PMID: 36135082 PMCID: PMC9497704 DOI: 10.3390/curroncol29090513] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/11/2022] [Accepted: 09/06/2022] [Indexed: 11/28/2022] Open
Abstract
A portion of gastric cancer patients with negative lymph node metastasis at an early stage eventually die from tumor recurrence or advanced metastasis. Occult lymph node metastasis (OLNM] is a potential risk factor for the recurrence and metastasis in these patients, and it is highly important for clinical prognosis. Positron emission tomography (PET)/computed tomography (CT) is used to assess lymph node metastasis in gastric cancer due to its advantages in anatomical and functional imaging and non-invasive nature. Among the major metabolic parameters of PET, the maximum standardized uptake value (SUVmax) is commonly used for examining lymph node status. However, SUVmax is susceptible to interference by a variety of factors. In recent years, the exploration of new PET metabolic parameters, new PET imaging agents and radiomics, has become an active research topic. This paper aims to explore the feasibility and predict the effectiveness of using PET/CT to detect OLNM. The current landscape and future trends of primary metabolic parameters and new imaging agents of PET are reviewed. For gastric cancer patients, the possibility to detect OLNM non-invasively will help guide surgeons to choose the appropriate lymph node dissection area, thereby reducing unnecessary dissections and providing more reasonable, personalized and comprehensive treatments.
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Affiliation(s)
- Danyu Ma
- Department of Oncology, The Third Affiliated Hospital of Soochow University, Changzhou 213003, China
| | - Ying Zhang
- Department of Oncology, The Third Affiliated Hospital of Soochow University, Changzhou 213003, China
- Institute of Cell Therapy, Soochow University, Changzhou 213003, China
| | - Xiaoliang Shao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou 213003, China
| | - Chen Wu
- Department of Oncology, The Third Affiliated Hospital of Soochow University, Changzhou 213003, China
- Institute of Cell Therapy, Soochow University, Changzhou 213003, China
- Correspondence: (C.W.); (J.W.)
| | - Jun Wu
- Department of Oncology, The Third Affiliated Hospital of Soochow University, Changzhou 213003, China
- Correspondence: (C.W.); (J.W.)
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Li Y, Xie F, Xiong Q, Lei H, Feng P. Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis. Front Oncol 2022; 12:946038. [PMID: 36059703 PMCID: PMC9433672 DOI: 10.3389/fonc.2022.946038] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/01/2022] [Indexed: 01/19/2023] Open
Abstract
Objective To evaluate the diagnostic performance of machine learning (ML) in predicting lymph node metastasis (LNM) in patients with gastric cancer (GC) and to identify predictors applicable to the models. Methods PubMed, EMBASE, Web of Science, and Cochrane Library were searched from inception to March 16, 2022. The pooled c-index and accuracy were used to assess the diagnostic accuracy. Subgroup analysis was performed based on ML types. Meta-analyses were performed using random-effect models. Risk of bias assessment was conducted using PROBAST tool. Results A total of 41 studies (56182 patients) were included, and 33 of the studies divided the participants into a training set and a test set, while the rest of the studies only had a training set. The c-index of ML for LNM prediction in training set and test set was 0.837 [95%CI (0.814, 0.859)] and 0.811 [95%CI (0.785-0.838)], respectively. The pooled accuracy was 0.781 [(95%CI (0.756-0.805)] in training set and 0.753 [95%CI (0.721-0.783)] in test set. Subgroup analysis for different ML algorithms and staging of GC showed no significant difference. In contrast, in the subgroup analysis for predictors, in the training set, the model that included radiomics had better accuracy than the model with only clinical predictors (F = 3.546, p = 0.037). Additionally, cancer size, depth of cancer invasion and histological differentiation were the three most commonly used features in models built for prediction. Conclusion ML has shown to be of excellent diagnostic performance in predicting the LNM of GC. One of the models covering radiomics and its ML algorithms showed good accuracy for the risk of LNM in GC. However, the results revealed some methodological limitations in the development process. Future studies should focus on refining and improving existing models to improve the accuracy of LNM prediction. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022320752
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Xue XQ, Yu WJ, Shi X, Shao XL, Wang YT. 18F-FDG PET/CT-based radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer. Front Oncol 2022; 12:911168. [PMID: 36003788 PMCID: PMC9393365 DOI: 10.3389/fonc.2022.911168] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 07/13/2022] [Indexed: 11/27/2022] Open
Abstract
Objective Lymph node metastasis (LNM) is not only one of the important factors affecting the prognosis of gastric cancer but also an important basis for treatment decisions. The purpose of this study was to investigate the value of the radiomics nomogram based on preoperative 18F-deoxyglucose (FDG) PET/CT primary lesions and clinical risk factors for predicting LNM in gastric cancer (GC). Methods We retrospectively analyzed radiomics features of preoperative 18F-FDG PET/CT images in 224 gastric cancer patients from two centers. The prediction model was developed in the training cohort (n = 134) and validated in the internal (n = 59) and external validation cohorts (n = 31). The least absolute shrinkage and selection operator (LASSO) regression was used to select features and build radiomics signatures. The radiomics feature score (Rad-score) was calculated and established a radiomics signature. Multivariate logistic regression analysis was used to screen independent risk factors for LNM. The minimum Akaike’s information criterion (AIC) was used to select the optimal model parameters to construct a radiomics nomogram. The performance of the nomogram was assessed with calibration, discrimination, and clinical usefulness. Results There was no significant difference between the internal verification and external verification of the clinical data of patients (all p > 0.05). The areas under the curve (AUCs) (95% CI) for predicting LNM based on the 18F-FDG PET/CT radiomics signature in the training cohort, internal validation cohort, and external validation cohort were 0.792 (95% CI: 0.712–0.870), 0.803 (95% CI: 0.681–0.924), and 0.762 (95% CI: 0.579–0.945), respectively. Multivariate logistic regression showed that carbohydrate antigen (CA) 19-9 [OR (95% CI): 10.180 (1.267–81.831)], PET/CT diagnosis of LNM [OR (95% CI): 6.370 (2.256–17.984)], PET/CT Rad-score [OR (95% CI): 16.536 (5.506–49.660)] were independent influencing factors of LNM (all p < 0.05), and a radiomics nomogram was established based on those factors. The AUCs (95% CI) for predicting LNM were 0.861 (95% CI: 0.799–0.924), 0.889 (95% CI: 0.800–0.976), and 0.897 (95% CI: 0.683–0.948) in the training cohort, the internal validation cohort, and the external validation cohort, respectively. Decision curve analysis (DCA) indicated that the 18F-FDG PET/CT-based radiomics nomogram has good clinical utility. Conclusions Radiomics nomogram based on the primary tumor of 18F-FDG PET/CT could facilitate the preoperative individualized prediction of LNM, which is helpful for risk stratification in GC patients.
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Affiliation(s)
- Xiu-qing Xue
- Department of Nuclear Medicine, The First People’s Hospital of Yancheng, Yancheng, China
- The Yancheng Clinical College of Xuzhou Medical University, Yancheng, China
| | - Wen-Ji Yu
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China
- Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou, China
| | - Xun Shi
- Department of Nuclear Medicine, The First People’s Hospital of Yancheng, Yancheng, China
- The Yancheng Clinical College of Xuzhou Medical University, Yancheng, China
| | - Xiao-Liang Shao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China
- Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou, China
| | - Yue-Tao Wang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China
- Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou, China
- *Correspondence: Yue-Tao Wang,
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Xue XQ, Wang B, Yu WJ, Zhang FF, Niu R, Shao XL, Shi YM, Yang YS, Wang JF, Li XF, Wang YT. Relationship between total lesion glycolysis of primary lesions based on 18F-FDG PET/CT and lymph node metastasis in gastric adenocarcinoma: a cross-sectional preliminary study. Nucl Med Commun 2022; 43:114-121. [PMID: 34406147 DOI: 10.1097/mnm.0000000000001475] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVES We explored the relationship between lymph node metastasis (LNM) and total lesion glycolysis (TLG) of primary lesions determined by 18fluoro-2-deoxyglucose PET/computed tomography (18F-FDG PET/CT) in patients with gastric adenocarcinoma, and evaluated the independent effect of this association. METHODS This retrospective study included 106 gastric adenocarcinoma patients who were examined by preoperative 18F-FDG PET/CT imaging between April 2016 and April 2020. We measured TLG of primary gastric lesions and evaluated its association with LNM. Multivariate logistic regression and a two-piece-wise linear regression were performed to evaluate the relationship between TLG of primary lesions and LNM. RESULTS Of the 106 patients, 75 cases (71%) had LNM and 31 cases (29%) did not have LNM. Univariate analyses revealed that a per-SD increase in TLG was independently associated with LNM [odds ratio (OR) = 2.37; 95% confidence interval (CI), 1.42-3.98; P = 0.0010]. After full adjustment of confounding factors, multivariate analyses exhibited that TLG of primary lesions was still significantly associated with LNM (OR per-SD: 2.20; 95% CI, 1.16-4.19; P = 0.0164). Generalized additive model indicated a nonlinear relationship and saturation effect between TLG of primary lesions and LNM. When TLG of primary lesions was <23.2, TLG was significantly correlated with LNM (OR = 1.26; 95% CI, 1.07-1.48; P = 0.0053), whereas when TLG of primary lesions was ≥ 23.2, the probability of LNM was greater than 60%, gradually reached saturation effect, as high as 80% or more. CONCLUSIONS In this preliminary study, there were saturation and segmentation effects between TLG of primary lesions determined by preoperative 18F-FDG PET/CT and LNM. When TLG of primary lesions was ≥ 23.2, the probability of LNM was greater than 60%, gradually reached saturation effect, as high as 80% or more. TLG of primary lesions is helpful in the preoperative diagnosis of LNM in patients with gastric adenocarcinoma.
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Affiliation(s)
- Xiu-Qing Xue
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou
- Department of Nuclear Medicine, The First People's Hospital of Yancheng City, Yancheng
- Department of Nuclear Medicine, Yancheng First Hospital, Affiliated Hospital of Nanjing University Medical School
| | - Bing Wang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou
| | - Wen-Ji Yu
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou
| | - Fei-Fei Zhang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou
| | - Rong Niu
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou
| | - Xiao-Liang Shao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou
| | - Yun-Mei Shi
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou
| | - Yan-Song Yang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou
| | - Jian-Feng Wang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou
| | - Xiao-Feng Li
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou
| | - Yue-Tao Wang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou
- Department of Nuclear Medicine, Changzhou Key Laboratory of Molecular Imaging, Changzhou, Jiangsu, China
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Abstract
Gastrointestinal malignancies encompass a variety of primary tumor sites, each with different staging criteria and treatment approaches. In this review we discuss technical aspects of 18F-FDG-PET/CT scanning to optimize information from both the PET and computed tomography components. Specific applications for 18F-FDG-PET/CT are summarized for initial staging and follow-up of the major disease sites, including esophagus, stomach, hepatobiliary system, pancreas, colon, rectum, and anus.
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Affiliation(s)
- Brandon A Howard
- Division of Nuclear Medicine and Radiotheranostics, Department of Radiology, Duke University Medical Center, DUMC Box 3949, 2301 Erwin Road, Durham, NC 27710, USA.
| | - Terence Z Wong
- Division of Nuclear Medicine and Radiotheranostics, Department of Radiology, Duke University Medical Center, DUMC Box 3949, 2301 Erwin Road, Durham, NC 27710, USA
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Song BI. A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer. Breast Cancer 2021; 28:664-671. [PMID: 33454875 DOI: 10.1007/s12282-020-01202-z] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 12/02/2020] [Indexed: 02/08/2023]
Abstract
OBJECTIVE The aim of this study was to develop and validate machine learning-based radiomics model for predicting axillary lymph-node (ALN) metastasis in invasive ductal breast cancer (IDC) using F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT). METHODS A total of 100 consecutive IDC patients who underwent surgical resection of primary tumor with sentinel lymph-node biopsy and/or ALN dissection without any neoadjuvant treatment were analyzed. Volume of interests (VOIs) were drawn more than 2.5 of standardized uptake value in the primary tumor on the PET scan using 3D slicer. Pyradiomics package was used for the extraction of texture features in python. The radiomics prediction model for ALN metastasis was developed in 75 patients of the training cohort and validated in 25 patients of the test cohort. XGBoost algorithm was utilized to select features and build radiomics model. The sensitivity, specificity, and accuracy of the predictive model were calculated. RESULTS ALN metastasis was found in 43 patients (43%). The sensitivity, specificity, and accuracy of F-18 FDG PET/CT for the diagnosis of ALN metastasis in the entire patients were 55.8%, 93%, and 77%, respectively. The radiomics model for the prediction of ALN metastasis was successfully developed. The sensitivity, specificity, and accuracy of the radiomics model for the prediction of ALN metastasis in the test cohorts were 90.9%, 71.4%, and 80%, respectively. CONCLUSION The machine learning-based radiomics model showed good sensitivity for the prediction of ALN metastasis and could assist the preoperative individualized prediction of ALN status in patients with IDC.
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Affiliation(s)
- Bong-Il Song
- Department of Nuclear Medicine, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, 1095 Dalgubeol-daero, Dalseo-gu, Daegu, 42601, Republic of Korea.
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