{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T08:15:48Z","timestamp":1767168948143,"version":"build-2238731810"},"reference-count":22,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T00:00:00Z","timestamp":1656892800000},"content-version":"vor","delay-in-days":184,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BioMed Research International"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:p>Microsatellites are small, repetitive sequences found all across the human genome. Microsatellite instability is the phenomenon of variations in the length of microsatellites induced by the insertion or deletion of repeat units in tumor tissue (MSI). MSI\u2010type stomach malignancy has distinct genetic phenotypes and clinic pathological characteristics, and the stability of microsatellites influences whether or not patients with gastric mesothelioma react to immunotherapy. As a result, determining MSI status prior to surgery is critical for developing treatment options for individuals with gastric cancer. Traditional MSI detection approaches need immunological histochemistry and genetic analysis, which adds to the expense and makes it difficult to apply to every patient in clinical practice. In this study, to predict the MSI status of gastric cancer patients, researchers used image feature extraction technology and a machine learning algorithm to evaluate high\u2010resolution histopathology pictures of patients. 279 cases of raw data were obtained from the TCGA database, 442 samples were obtained after preprocessing and upsampling, and 445 quantitative image features, including first\u2010order statistics of impressions, texture features, and wavelet features, were extracted from the histopathological images of each sample. To filter the characteristics and provide a prediction label (risk score) for MSI status of gastric cancer, Lasso regression was utilized. The predictive label\u2019s classification performance was evaluated using a logistic classification model, which was then coupled with the clinical data of each patient to create a customized nomogram for MSI status prediction using multivariate analysis.<\/jats:p>","DOI":"10.1155\/2022\/1012684","type":"journal-article","created":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T19:50:09Z","timestamp":1656964209000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["[Retracted] ML\u2010Based Texture and Wavelet Features Extraction Technique to Predict Gastric Mesothelioma Cancer"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3144-3114","authenticated-orcid":false,"given":"Neeraj","family":"Garg","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6277-5432","authenticated-orcid":false,"given":"Divyanshu","family":"Sinha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4524-2452","authenticated-orcid":false,"given":"Babita","family":"Yadav","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0568-2867","authenticated-orcid":false,"given":"Bhoomi","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5093-6507","authenticated-orcid":false,"given":"Sachin","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4928-3449","authenticated-orcid":false,"given":"Shahajan","family":"Miah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,7,4]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"crossref","unstructured":"DengY. LanJ. HuangY. GaoQ. ZhangH. TongT. andChenG. Predicting differentiation degree of gastric cancer pathology images based on mask attention R-CNN 2nd International Conference on Computer Engineering and Intelligent Control (ICCEIC) 2021 Chongqing China https:\/\/doi.org\/10.1109\/icceic54227.2021.00027.","DOI":"10.1109\/ICCEIC54227.2021.00027"},{"key":"e_1_2_8_2_2","doi-asserted-by":"crossref","unstructured":"LiY. LiX. XieX. andShenL. Deep learning based gastric cancer identification 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) 2018 Washington DC USA https:\/\/doi.org\/10.1109\/isbi.2018.8363550 2-s2.0-85048131929.","DOI":"10.1109\/ISBI.2018.8363550"},{"key":"e_1_2_8_3_2","doi-asserted-by":"crossref","unstructured":"LuoS. LiuH. ZhaoH. AnX. XieH. andHuoL. Gastric and colon cancer imaging with swept source optical coherence tomography Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR) 2017 Singapore https:\/\/doi.org\/10.1109\/cleopr.2017.8118726 2-s2.0-85044340496.","DOI":"10.1109\/CLEOPR.2017.8118726"},{"key":"e_1_2_8_4_2","doi-asserted-by":"crossref","unstructured":"LiL. ChenM. ZhouY. WangJ. andWangD. Research of deep learning on gastric cancer diagnosis Cross Strait Radio Science &amp; Wireless Technology Conference (CSRSWTC) 2020 Fuzhou China https:\/\/doi.org\/10.1109\/csrswtc50769.2020.9372583.","DOI":"10.1109\/CSRSWTC50769.2020.9372583"},{"key":"e_1_2_8_5_2","doi-asserted-by":"crossref","unstructured":"YahataA. TakemuraH. TakamatsuT. YokotaH. IwanamiR. UmezawaM. OkuboK. SogaK. MitsuiT. KadotaT. KuwataT. andIkematsuH. Wavelength selection of near-infrared hyperspectral imaging for gastric cancer detection 6th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) 2021 Oita Japan https:\/\/doi.org\/10.1109\/iciibms52876.2021.9651625.","DOI":"10.1109\/ICIIBMS52876.2021.9651625"},{"key":"e_1_2_8_6_2","doi-asserted-by":"crossref","unstructured":"XiongN.-y. ZhouJ.-h. ZhouX.-b. ZhangJ. andZhangC.-y. Meta-analysis of association between dietary factors and gastric cancer in China International Conference on Future BioMedical Information Engineering (FBIE) 2009 Sanya China https:\/\/doi.org\/10.1109\/fbie.2009.5405871 2-s2.0-77749333020.","DOI":"10.1109\/FBIE.2009.5405871"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/tbme.2014.2386309"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.47164\/ijngc.v12i3.830"},{"key":"e_1_2_8_9_2","doi-asserted-by":"publisher","DOI":"10.1515\/jisys-2020-0123"},{"key":"e_1_2_8_10_2","doi-asserted-by":"publisher","DOI":"10.1002\/bjs.11928"},{"key":"e_1_2_8_11_2","doi-asserted-by":"publisher","DOI":"10.1117\/12.2611519"},{"key":"e_1_2_8_12_2","doi-asserted-by":"crossref","unstructured":"ZhangL. DongD. LiuZ. ZhouJ. andTianJ. Joint multi-task learning for survival prediction of gastric cancer patients using CT images IEEE 18th International Symposium on Biomedical Imaging (ISBI) 2021 Nice France https:\/\/doi.org\/10.1109\/ISBI48211.2021.9433820.","DOI":"10.1109\/ISBI48211.2021.9433820"},{"key":"e_1_2_8_13_2","doi-asserted-by":"crossref","unstructured":"ZhuZ. DingX. ZhangD. andWangL. Weakly-supervised balanced attention network for gastric pathology image localization and classification 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) 2020 Iowa City IA USA https:\/\/doi.org\/10.1109\/isbi45749.2020.9098567.","DOI":"10.1109\/ISBI45749.2020.9098567"},{"key":"e_1_2_8_14_2","doi-asserted-by":"publisher","DOI":"10.1504\/ijcnds.2011.038525"},{"key":"e_1_2_8_15_2","doi-asserted-by":"crossref","unstructured":"JiaY.-F. SunT. LiuH.-Y. MaX.-l. WangY.-S. andXiaoD.-J. Construction of expression vector of siRNA specific for STAT5a and identification of its efficiency in MGC803 gastric cancer cell line 2014 IEEE Workshop on Electronics Computer and Applications. 2014 IEEE Workshop on Electronics Computer and Applications (IWECA) 2014 Ottawa ON https:\/\/doi.org\/10.1109\/iweca.2014.6845777 2-s2.0-84904575016.","DOI":"10.1109\/IWECA.2014.6845777"},{"key":"e_1_2_8_16_2","doi-asserted-by":"crossref","unstructured":"ChenJ. ChenL. andShabazM. Image fusion algorithm at pixel level based on edge detection 2021 2021 1\u201310 https:\/\/doi.org\/10.1155\/2021\/5760660 34422244.","DOI":"10.1155\/2021\/5760660"},{"key":"e_1_2_8_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10509-021-04036-z"},{"key":"e_1_2_8_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2011.2141674"},{"key":"e_1_2_8_19_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/4242646"},{"key":"e_1_2_8_20_2","unstructured":"HaqM. A. JainK. andMenonK. P. R. Volumetric glacier changes in the Garhwal Himalayas using multitemporal digital elevation model from 2001 to 2010 35th International Symposium on Remote Sensing of Environment (ISRSE) 2013 Beijing."},{"key":"e_1_2_8_21_2","doi-asserted-by":"crossref","unstructured":"HatamiS. ShamsaeeR. andOlyaeiM. H. Detection and classification of gastric precancerous diseases using deep learning 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) 2020 Mashhad Iran https:\/\/doi.org\/10.1109\/icspis51611.2020.9349603.","DOI":"10.1109\/ICSPIS51611.2020.9349603"},{"key":"e_1_2_8_22_2","doi-asserted-by":"crossref","unstructured":"HeQ. Comparison of cancer morbidity and mortality between developed countries developing countries and China International Conference on Public Health and Data Science (ICPHDS). 2020 2020 Guangzhou China https:\/\/doi.org\/10.1109\/icphds51617.2020.00079.","DOI":"10.1109\/ICPHDS51617.2020.00079"}],"updated-by":[{"DOI":"10.1155\/2024\/9842416","type":"retraction","label":"Retraction","source":"retraction-watch","updated":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"record-id":"57625"},{"DOI":"10.1155\/2024\/9842416","type":"retraction","label":"Retraction","source":"publisher","updated":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T00:00:00Z","timestamp":1704412800000}}],"container-title":["BioMed Research International"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/bmri\/2022\/1012684.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/bmri\/2022\/1012684.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2022\/1012684","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T03:34:52Z","timestamp":1735616092000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2022\/1012684"}},"subtitle":[],"editor":[{"given":"Gaganpreet","family":"Kaur","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2022,1]]},"references-count":22,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["10.1155\/2022\/1012684"],"URL":"https:\/\/doi.org\/10.1155\/2022\/1012684","archive":["Portico"],"relation":{},"ISSN":["2314-6133","2314-6141"],"issn-type":[{"value":"2314-6133","type":"print"},{"value":"2314-6141","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1]]},"assertion":[{"value":"2022-05-18","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-06-23","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-07-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"1012684"}}