Yeang Guo
ABSTRACT
Objective: The objective is to develop a predictive model utilizing Support Vector Machines (SVM) for the purpose of classifying the clinical stage of breast cancer.
Materials and Methods: Accurate determination of the clinical stage of breast cancer patients holds significant importance in selecting suitable treatment options and minimizing avoidable complications. In this study, we present the application of radiomics and SVM for breast cancer computed tomography (CT) to anticipate the preoperative clinical stage in breast cancer patients. The training dataset encompasses 166 cases obtained from the Affiliated Hospital of Xiangnan University, while the test dataset comprises 91 cases from Chenzhou Third People's Hospital. The integration of clinical parameters with radiomics exhibits the most superior diagnostic efficacy in forecasting the clinical stage of breast cancer. As part of the evaluation, various metrics were calculated, including the area under the curve (AUC), the accuracy (ACC), sensitivity (Sen), specificity (Spe), positive predictive value (PPV) and negative predictive value (NPV). To differentiate between the radiomics model, clinical data model, and fusion model, the Delong test was utilized. The precision of the prediction model was evaluated by generating a calibration curve using 1,000 bootstrap weight samples. Furthermore, the decision curve analysis (DCA) was conducted to assess the model's practicality.
Results: The fusion model exhibits superior predictive performance compared to both the single radiomics model and clinical model. The fusion model's test sets of AUC, ACC, Sen, Spe, PPV, and NPV are 0.824, 0.780, 0.932, 0.652, 0.707, and 0.909, respectively.
Conclusion: The fusion model exhibits greater efficacy than both the single radiomics model and clinical model, and thus holds significant potential for facilitating the diagnosis of breast cancer stage and the development of individualized treatment plans.CCS
- Zhang T, Tan T, Wang X, Gao Y, Han L, Balkenende L, D'Angelo A, Bao L, Horlings HM, Teuwen J, Beets-Tan RGH, Mann RM. RadioLOGIC, a healthcare model for processing electronic health records and decision-making in breast disease. Cell Rep Med. 2023 Aug 15;4(8):101131. doi: 10.1016/j.xcrm.2023.101131. Epub 2023 Jul 24. PMID: 37490915; PMCID: PMC10439251.Google Scholar
Cross Ref
- Zhang T, Tan T, Samperna R, Li Z, Gao Y, Wang X Radiomics and artificial intelligence in breast imaging: a survey. Artificial Intelligence Review. 2023 Jul 1. Epub 2023 Jul 1. doi: 10.1007/s10462-023-10543-y.Google ScholarDigital Library
- Wang Y, Deng Y, Xie H, Cao S. Hub gene of disulfidptosis-related immune checkpoints in breast cancer. Med Oncol. 2023 Jul 5;40(8):222. doi: 10.1007/s12032-023-02073-y. PMID: 37402987.Google ScholarCross Ref
- Xie H, Gong M, Zhang J, Li Q. Construction of a predictive model for radiation proctitis after radiotherapy for female pelvic tumors based on machine learning. Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2022 Aug 28;47(8):1065-1074. English, Chinese. doi: 10.11817/j.issn.1672-7347.2022.220353. PMID: 36097774.Google ScholarCross Ref
- Horvat N, Carlos Tavares Rocha C, Clemente Oliveira B, Petkovska I, Gollub MJ. MRI of Rectal Cancer: Tumor Staging, Imaging Techniques, and Management. Radiographics. 2019 Mar-Apr;39(2):367-387. doi: 10.1148/rg.2019180114. Epub 2019 Feb 15. PMID: 30768361; PMCID: PMC6438362.Google ScholarCross Ref
- Zhang Y, Chen JH, Chang KT, Park VY, Kim MJ, Chan S, Chang P, Chow D, Luk A, Kwong T, Su MY. Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net. Acad Radiol. 2019 Nov;26(11):1526-1535. doi: 10.1016/j.acra.2019.01.012. Epub 2019 Jan 31. PMID: 30713130; PMCID: PMC6669125.Google ScholarCross Ref
- https://www.nccn.org/login?ReturnURL=https://www.nccn.org/professionals/physician_gls/pdf/breast.pdfGoogle Scholar
- Davey M.G., Davey M.S., Boland M.R., Ryan É.J., Lowery A.J., Kerin M.J. Radiomic differentiation of breast cancer molecular subtypes using pre-operative breast imaging—A systematic review and meta-analysis. Eur. J. Radiol. 2021;144:109996.238-250 doi: 10.1016/j.ejrad.2021.109996.Google ScholarCross Ref
- Kan A. Machine learning applications in cell image analysis. Immunol Cell Biol. 2017 Jul;95(6):525-530. doi: 10.1038/icb.2017.16. Epub 2017 Mar 15. PMID: 28294138.Google ScholarCross Ref
- Bryan S, Masoud H, Weir HK, Woods R, Lockwood G, Smith L, Brierley J, Gospodarowicz M, Badets N. Cancer in Canada: Stage at diagnosis. Health Rep. 2018 Dec 19;29(12):21-25. PMID: 30566206.Google Scholar
- Lu J, Jin R, Wang M, Song E, Ma G. A bidirectional registration neural network for cardiac motion tracking using cine MRI images. Comput Biol Med. 2023 Jun;160:107001. doi: 10.1016/j.compbiomed.2023.107001. Epub 2023 May 9. PMID: 37187138.Google ScholarDigital Library
- Liu M, Sui LY, Yin XP, Wang JN, Li G, Song J, Ji Q. Deep learning for predicting the human epidermal growth factor receptor 2 status of breast cancer liver metastases based on contrast-enhanced computed tomography: a development and validation study. Quant Imaging Med Surg. 2023 May 1;13(5):2837-2845. doi: 10.21037/qims-22-967. Epub 2023 Mar 30. PMID: 37179945; PMCID: PMC10167468.Google ScholarCross Ref
- Agner S.C., Rosen M.A., Englander S., Tomaszewski J.E., Feldman M.D., Zhang P., Mies C., Schnall M.D., Madabhushi A. Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: A feasibility study. Radiology. 2014;272(1):91–99. doi: 10.1148/radiol.14121031.Google ScholarCross Ref
- Yan J, Lai Y, Xu Y, Zheng Y, Niu Z, Tan T. Artificial intelligence-based medical image automatic diagnosis and prognosis prediction. Frontiers in Physics. 2023 May 25;11:403.Google Scholar
- Henkin S, McBane RD. SVM Communications: Membership Spotlight. Vasc Med. 2022 Aug;27(4):418-420. doi: 10.1177/1358863X221112726. PMID: 36522856.Google ScholarCross Ref
- Tagliafico AS, Valdora F, Mariscotti G, Durando M, Nori J, La Forgia D, Rosenberg I, Caumo F, Gandolfo N, Houssami N, Calabrese M. An exploratory radiomics analysis on digital breast tomosynthesis in women with mammographically negative dense breasts. Breast. 2018 Aug;40:92-96. doi: 10.1016/j.breast.2018.04.016. Epub 2018 Apr 30. PMID: 29723697.Google ScholarCross Ref
- Granata V, Fusco R, De Muzio F, Cutolo C, Setola SV, Grassi R, Grassi F, Ottaiano A, Nasti G, Tatangelo F, Pilone V, Miele V, Brunese MC, Izzo F, Petrillo A. Radiomics textural features by MR imaging to assess clinical outcomes following liver resection in colorectal liver metastases. Radiol Med. 2022 May;127(5):461-470. doi: 10.1007/s11547-022-01477-6. Epub 2022 Mar 26. PMID: 35347583.Google ScholarCross Ref
- Tagliafico AS, Piana M, Schenone D, Lai R, Massone AM, Houssami N. Overview of radiomics in breast cancer diagnosis and prognostication. Breast. 2020 Feb;49:74-80. doi: 10.1016/j.breast.2019.10.018. Epub 2019 Nov 6. PMID: 31739125; PMCID: PMC7375670.Google ScholarCross Ref
- Yang X, Tridandapani S, Beitler JJ, Yu DS, Yoshida EJ, Curran WJ, Liu T. Ultrasound GLCM texture analysis of radiation-induced parotid-gland injury in head-and-neck cancer radiotherapy: an in vivo study of late toxicity. Med Phys. 2012 Sep;39(9):5732-9. doi: 10.1118/1.4747526. PMID: 22957638; PMCID: PMC3443195.Google ScholarCross Ref
- Gu J, Tong T, Xu D, Cheng F, Fang C, He C, Wang J, Wang B, Yang X, Wang K, Tian J, Jiang T. Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: A multicenter study. Cancer. 2023 Feb 1;129(3):356-366. doi: 10.1002/cncr.34540. Epub 2022 Nov 19. PMID: 36401611.Google ScholarCross Ref
- Lee SC, Tchelepi H, Khadem N, Desai B, Yamashita M, Hovanessian-Larsen L. Imaging of Benign and Malignant Breast Lesions Using Contrast-Enhanced Ultrasound: A Pictorial Essay. Ultrasound Q. 2022 Mar 1;38(1):2-12. doi: 10.1097/RUQ.0000000000000574. PMID: 35239626.Google ScholarCross Ref
- Eiriz IF, Vaz Batista M, Cruz Tomás T, Neves MT, Guerra-Pereira N, Braga S. Breast cancer in very young women-a multicenter 10-year experience. ESMO Open. 2021 Feb;6(1):100029. doi: 10.1016/j.esmoop.2020.100029. Epub 2021 Jan 4. PMID: 33399090; PMCID: PMC7807935.Google ScholarCross Ref
- Tekcan Sanli DE, Yildirim D, Kandemirli SG, Sanli AN, Aribal E. Evaluation of Multiparametric Shear Wave Elastography Indices in Malignant and Benign Breast Lesions. Acad Radiol. 2022 Jan;29 Suppl 1:S50-S61. doi: 10.1016/j.acra.2021.09.015. Epub 2021 Oct 19. PMID: 34674923.Google ScholarCross Ref
- Najahi-Missaoui W, Quach ND, Somanath PR, Cummings BS. Liposomes Targeting P21 Activated Kinase-1 (PAK-1) and Selective for Secretory Phospholipase A2 (sPLA2) Decrease Cell Viability and Induce Apoptosis in Metastatic Triple-Negative Breast Cancer Cells. Int J Mol Sci. 2020 Dec 10;21(24):9396. doi: 10.3390/ijms21249396. PMID: 33321758; PMCID: PMC7764208.Google ScholarCross Ref
- Liu X, Qiu H, Huang Y, Xu D, Li W, Li Y, Chen Y, Zhou Z, Sun X. Impact of preoperative anemia on outcomes in patients undergoing curative resection for gastric cancer: a single-institution retrospective analysis of 2163 Chinese patients. Cancer Med. 2018 Feb;7(2):360-369. doi: 10.1002/cam4.1309. Epub 2018 Jan 17. PMID: 29341506; PMCID: PMC5806112.Google ScholarCross Ref
- Alexander DC, Corman T, Mendoza M, Glass A, Belity T, Wu R, Campbell RR, Han J, Keiser AA, Winkler J, Wood MA, Kim T, Garcia BA, Cohen H, Mews P, Egervari G, Berger SL. Targeting acetyl-CoA metabolism attenuates the formation of fear memories through reduced activity-dependent histone acetylation. Proc Natl Acad Sci U S A. 2022 Aug 9;119(32):e2114758119. doi: 10.1073/pnas.2114758119. Epub 2022 Aug 3. PMID: 35921439; PMCID: PMC9371679.Google ScholarCross Ref
- Sáyago-Ayerdi SG, Zamora-Gasga VM, Venema K. Changes in gut microbiota in predigested Hibiscus sabdariffa L calyces and Agave (Agave tequilana weber) fructans assessed in a dynamic in vitro model (TIM-2) of the human colon. Food Res Int. 2020 Jun;132:109036. doi: 10.1016/j.foodres.2020.109036. Epub 2020 Jan 30. PMID: 32331660.Google ScholarCross Ref
- Alshareeda AT, Nur Khatijah MZ, Al-Sowayan BS. Nanotechnology: A revolutionary approach to prevent breast cancer recurrence. Asian J Surg. 2023 Jan;46(1):13-17. doi: 10.1016/j.asjsur.2022.03.002. Epub 2022 Mar 29. PMID: 35361551.Google ScholarCross Ref
- Ambrosone CB, Zirpoli GR, Hutson AD, McCann WE, McCann SE, Barlow WE, Kelly KM, Cannioto R, Sucheston-Campbell LE, Hershman DL, Unger JM, Moore HCF, Stewart JA, Isaacs C, Hobday TJ, Salim M, Hortobagyi GN, Gralow JR, Budd GT, Albain KS. Dietary Supplement Use During Chemotherapy and Survival Outcomes of Patients With Breast Cancer Enrolled in a Cooperative Group Clinical Trial (SWOG S0221). J Clin Oncol. 2020 Mar 10;38(8):804-814. doi: 10.1200/JCO.19.01203. Epub 2019 Dec 19. PMID: 31855498; PMCID: PMC7062457.Google ScholarCross Ref
- Kirshner, J., Hatch, M., Hennessy, D. D., Fridman, M. & Tannous, R. E. Anemia in stage II and III breast cancer patients treated with adjuvant doxorubicin and cyclophosphamide chemotherapy[J]. Oncologist. 2004:9, 25-32.Google Scholar
- Hakozaki Y, Yamada Y, Takeshima Y, Taguchi S, Kawai T, Nakamura M, Iwaki T, Teshima T, Kinoshita Y, Akiyama Y, Sato Y, Yamada D, Suzuki M, Kume H. Low hemoglobin and PSA kinetics are prognostic factors of overall survival in metastatic castration-resistant prostate cancer patients. Sci Rep. 2023 Feb 15;13(1):2672. doi: 10.1038/s41598-023-29634-5. PMID: 36792713; PMCID: PMC9931698.Google ScholarCross Ref
Index Terms
- SVM in Classification of stage 0~II and III~IV with Breast Cancer : A Retrospective Cohort Study on a bicentric cohort
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