{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T16:21:16Z","timestamp":1759335676873,"version":"3.41.2"},"reference-count":28,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,6,15]],"date-time":"2022-06-15T00:00:00Z","timestamp":1655251200000},"content-version":"vor","delay-in-days":165,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Contrast Media &amp; Molecular Imaging"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:p><jats:italic>Objective<\/jats:italic>. A case\u2010control study was conducted to explore the application and clinical value of machine learning\u2010based cervical cancer (CC) diagnosis and prediction model in adjuvant chemotherapy of CC. <jats:italic>Methods<\/jats:italic>. From August 2019 to August 2021, 46 patients with stage IA CC (study group) and 55 patients with high\u2010grade squamous intraepithelial lesions (HSIL) (control group) were retrospectively analyzed. All patients completed routine MRI examinations, the ADC values of diseased CC and normal cervix and cervical tissues in different stages were compared, and the changes of ADC values in CC tissues before and after chemotherapy were analyzed. The training set (IA\u2009=\u200937, HSIL\u2009=\u200944) and test set (IA\u2009=\u20099, HSIL\u2009=\u200911) are set in a ratio of 4\u2009:\u20091. The preoperative MRI images were collected and uploaded to the radiomics cloud platform after preprocessing, and the cervix was manually delineated layer by layer on OSag\u2010T2WI, OAx\u2010T1WI, and OAx\u2010T2FS, respectively, to obtain a three\u2010dimensional volume of interest (VOI) of the cervix to extract omics features. Variance Threshold analysis, univariate feature selection (SelectKBest), and least absolute shrinkage and selection operator (LASSO) are adopted to reduce the dimension of data and enroll features. The arbitrary forest model was adopted for machine learning, the ROC curve was drawn, and the diagnostic performance of different sequence omics models was analyzed. <jats:italic>Results<\/jats:italic>. Compared with ADC of stage A CC and HSIL, the ADC value of CC was remarkably lower than that of normal CC (<jats:italic>P<\/jats:italic> &lt; 0.05). The ROC curve analysis of ADC value to differentiate CC and normal cervix indicated that the AUC was 0.838 and the 95% confidence interval was 0.721\u20130.955. According to the maximum Youden index of 0.848, the optimal critical value of ADC was 1.267\u2009\u00d7\u200910<jats:sup>\u22123<\/jats:sup>\u2009mm<jats:sup>2<\/jats:sup>\/s and the sensitivity and specificity were 92.21% and 9.48%, respectively. All results are indicated in Table 2. After CC treatment, 12 patients were effective (CR\u2009+\u2009PR) and 4 patients were ineffective (PD\u2009+\u2009SD). When the <jats:italic>b<\/jats:italic> value was 1000\u2009s\/mm<jats:sup>2<\/jats:sup>, the ADC value of the effective patients after the second chemotherapy was significantly higher than that of the first chemotherapy and before treatment (<jats:italic>P<\/jats:italic> &lt; 0.05). There was no significant difference between the ADC value after the first chemotherapy and before treatment, compared with before treatment (<jats:italic>P<\/jats:italic> &gt; 0.05). There was no significant difference in ADC value between the ineffective patients before treatment and after the first and second chemotherapy (<jats:italic>P<\/jats:italic> &gt; 0.05). A total of 8 omics features were extracted based on OSag\u2010T2WI, all of which were wavelet features, including 7 texture features and 1 first\u2010order feature. A total of 10 omics features were extracted based on OAx\u2010T1WI, including 6 wavelet first\u2010order features, 2 gradient first\u2010order features, and 2 wavelet texture features. Based on OAx\u2010T2FS, 6 omics features were extracted, including 3 wavelet texture features, 2 original shape features, and 1 logarithmic first\u2010order feature. Based on OSag\u2010T2WI&amp;OAx\u2010T2FS, 9 histological features were extracted, 4 from OSag\u2010T2WI and 5 from OAx\u2010T2FS. The diagnostic performance of the four arbitrary forest models is indicated in Table 1, and the ROC curve is indicated in Figure 6. The diagnostic performance of the omics model based on OSag\u2010T2WI&amp;OAx\u2010T2FS was the best in both the training set and the test set. The AUC of the training set was 0.991 (95% CI (0.94, 1.00)), and the accuracy rate was 0.925. The AUC of the test set was 0.894 (95% CI (0.75, 1.00)), and the accuracy rate was 0.835. On the other hand, the diagnostic efficiency of the group model based on OAx\u2010T1WI was the worst in both the training set and the test set. The AUC of the training set was 0.713 (95% CI (0.52, 0.92)), and the accuracy rate was 0.71. The AUC of test set is 0.513 (95% CI (0.24, 0.77)), and the accuracy rate was 0.56, which has no practical clinical significance. <jats:italic>Conclusion<\/jats:italic>. A CC diagnosis and prediction model based on machine learning can better distinguish stage IA CC from HSIL in the absence of clear lesions, which is of great significance for reducing invasive examination before surgery, guiding surgical procedures and adjuvant chemotherapy for CC.<\/jats:p>","DOI":"10.1155\/2022\/2432291","type":"journal-article","created":{"date-parts":[[2022,6,15]],"date-time":"2022-06-15T18:35:09Z","timestamp":1655318109000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Application and Clinical Value of Machine Learning\u2010Based Cervical Cancer Diagnosis and Prediction Model in Adjuvant Chemotherapy for Cervical Cancer: A Single\u2010Center, Controlled, Non\u2010Arbitrary Size Case\u2010Control Study"],"prefix":"10.1155","volume":"2022","author":[{"given":"Yang","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lidan","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6356-7715","authenticated-orcid":false,"given":"Guohua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,6,15]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21492"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2017.09.013"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2014.11.005"},{"key":"e_1_2_9_4_2","article-title":"Breast cancer survivability prediction using labeled, unlabeled, and pseudo-labeled patient data","author":"Juhyeon K.","year":"2013","journal-title":"Journal of the American Medical Informatics Association Jamia"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.currproblcancer.2018.06.003"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.3892\/ol.2020.11754"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pvr.2019.100170"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21350"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1097\/lgt.0000000000000525"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/s0140-6736(18)32470-x"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2011.11.036"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015151169"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.21037\/tcr.2016.06.17"},{"volume-title":"Research on Machine Learning Method and its Application in Medical Image Processing","year":"2013","author":"Shi Y.","key":"e_1_2_9_14_2"},{"volume-title":"Research on Convolution Neural Network and Transfer Learning in Cancer Image Analysis","year":"2018","author":"Yu S.","key":"e_1_2_9_15_2"},{"volume-title":"Prediction of Pancreatic Cancer Markers Based on Support Vector Machine","year":"2018","author":"Lu Y.","key":"e_1_2_9_16_2"},{"key":"e_1_2_9_17_2","first-page":"44","article-title":"Semi-supervised learning method for cancer clinical outcome prediction based on graph convolution network","volume":"8","author":"Ning S.","year":"2018","journal-title":"Intelligent computer and its Application"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2017.2763984"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.25081"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2019.4595"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1200\/jco.2005.02.0354"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1503\/cmaj.071124"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2020.102963"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/60\/13\/5123"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-017-3697-1"},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-017-3898-7"},{"key":"e_1_2_9_27_2","first-page":"1133","article-title":"A preliminary study on the prediction of myometrial invasion depth of endometrial carcinoma by arbitrary forest model based on MRI imageology","volume":"11","author":"Guo ran","year":"2020","journal-title":"Magnetic Resonance Imaging"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.00412"}],"container-title":["Contrast Media &amp; 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