{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T16:40:19Z","timestamp":1790354419810,"version":"4.1.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Key Research and development program of the Ministry of Science and Technology","award":["2017YFB1002300"],"award-info":[{"award-number":["2017YFB1002300"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Arch Gynecol Obstet"],"published-print":{"date-parts":[[2021,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>Our objective was to establish a random forest model and to evaluate its predictive capability of the treatment effect of neoadjuvant chemotherapy\u2013radiation therapy.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>This retrospective study included 82 patients with locally advanced cervical cancer who underwent scanning from March 2013 to May 2018. The random forest model was established and optimised based on the open source toolkit scikit-learn. Byoptimising of the number of decision trees in the random forest, the criteria for selecting the final partition index and the minimum number of samples partitioned by each node, the performance of random forest in the prediction of the treatment effect of neoadjuvant chemotherapy\u2013radiation therapy on advanced cervical cancer (&gt;\u2009IIb) was evaluated.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The number of decision trees in the random forests influenced the model performance. When the number of decision trees was set to 10, 25, 40, 55, 70, 85 and 100, the performance of random forest model exhibited an increasing trend first and then a decreasing one. The criteria for the selection of final partition index showed significant effects on the generation of decision trees. The Gini index demonstrated a better effect compared with information gain index. The area under the receiver operating curve for Gini index attained a value of 0.917.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The random forest model showed potential in predicting the treatment effect of neoadjuvant chemotherapy\u2013radiation therapy based on high-resolution T2WIs for advanced cervical cancer (&gt;\u2009IIb).<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00404-020-05908-5","type":"journal-article","created":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T06:03:29Z","timestamp":1609740209000},"page":"811-820","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Optimisation and evaluation of the random forest model in the efficacy prediction of chemoradiotherapy for advanced cervical cancer based on radiomics signature from high-resolution T2 weighted\u00a0images"],"prefix":"10.1007","volume":"303","author":[{"given":"Defeng","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinglei","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujie","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongji","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7434-4434","authenticated-orcid":false,"given":"Lanxiang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,4]]},"reference":[{"key":"5908_CR1","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1007\/s10552-019-01223-w","volume":"30","author":"DSM Chan","year":"2019","unstructured":"Chan DSM, Abar L, Cariolou M, Nanu N, Greenwood DC, Bandera EV, McTiernan A, Norat T (2019) World Cancer Research Fund International: continuous update project-systematic literature review and meta-analysis of observational cohort studies on physical activity, sedentary behavior, adiposity, and weight change and breast cancer risk. Cancer Causes Control 30:1183\u20131200","journal-title":"Cancer Causes Control"},{"key":"5908_CR2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.3322\/caac.21254","volume":"65","author":"RL Siegel","year":"2015","unstructured":"Siegel RL, Miller KD, Jemal A (2015) Cancer statistics, 2015. CA cancer J Clin 65:5\u201329","journal-title":"CA cancer J Clin"},{"key":"5908_CR3","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1007\/s00404-018-5006-z","volume":"299","author":"SG Vitale","year":"2019","unstructured":"Vitale SG, Capriglione S, Zito G, Lopez S, Gulino FA, Di Guardo F, Vitagliano A, Noventa M, La Rosa VL, Sapia F, Valenti G, Rapisarda AMC, Peterlunger I, Rossetti D, Lagan\u00e0 AS (2019) Management of endometrial, ovarian and cervical cancer in the elderly: current approach to a challenging condition. Arch Gynecol Obstet 299:299\u2013315","journal-title":"Arch Gynecol Obstet"},{"key":"5908_CR4","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1002\/ijgo.12749","volume":"145","author":"N Bhatla","year":"2019","unstructured":"Bhatla N, Berek JS, Cuello Fredes M, Denny LA, Grenman S, Karunaratne K, Kehoe ST, Konishi I, Olawaiye AB, Prat J, Sankaranarayanan R, Brierley J, Mutch D, Querleu D, Cibula D, Quinn M, Botha H, Sigurd L, Rice L, Ryu HS, Ngan H, M\u00e4enp\u00e4\u00e4 J, Andrijono A, Purwoto G, Maheshwari A, Bafna UD, Plante M, Natarajan J (2019) Revised FIGO staging for carcinoma of the cervix uteri. Int J Gynaecol Obstetr Off Organ Int Feder Gynaecol Obstetr 145:129\u2013135","journal-title":"Int J Gynaecol Obstetr Off Organ Int Feder Gynaecol Obstetr"},{"key":"5908_CR5","doi-asserted-by":"publisher","DOI":"10.1111\/1754-9485.13103","author":"K Rongsriyam","year":"2020","unstructured":"Rongsriyam K, Tangjitgamol S, Leelahavarong P, Teerawattananon Y, Tharavichitkul E, Tovanabutra C, Asakij T, Paengchit K, Sukhaboon J, Penpattanagul S, Kridakara LCA, Hanprasertpong J, Khunnarong J, Chottetanaprasith T, Lorvidhaya V (2020) Cost-utility analysis of adjuvant chemotherapy after concurrent chemoradiation in patients with locally advanced cervical cancer. J Med Imag Radiat Oncol. https:\/\/doi.org\/10.1111\/1754-9485.13103","journal-title":"J Med Imag Radiat Oncol"},{"key":"5908_CR6","first-page":"3929435","volume":"2020","author":"Y Wang","year":"2020","unstructured":"Wang Y, Zhang Z, Tao P, Reyila M, Qi X, Yang J (2020) The abnormal expression of miR-205-5p, miR-195-5p, and VEGF-A in human cervical cancer is related to the treatment of venous thromboembolism. Biomed Res Int 2020:3929435","journal-title":"Biomed Res Int"},{"key":"5908_CR7","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/s11912-019-0824-0","volume":"21","author":"IS Haldorsen","year":"2019","unstructured":"Haldorsen IS, Lura N, Blaak\u00e6r J, Fischerova D, Werner HMJ (2019) What is the role of imaging at primary diagnostic work-up in uterine cervical cancer? Curr Oncol Rep 21:77","journal-title":"Curr Oncol Rep"},{"key":"5908_CR8","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1007\/s00404-014-3350-1","volume":"290","author":"R Cooper","year":"2014","unstructured":"Cooper R, Brearley E, Hurmuz P, Musunuru HB, Richardson C, Swift S, Orton J, Bownes P (2014) Assessment of uterus position as a surrogate for high-risk clinical target volume with respect to the applicator position for multiple fractions of brachytherapy in cervical cancer. Arch Gynecol Obstet 290:1201\u20131205","journal-title":"Arch Gynecol Obstet"},{"key":"5908_CR9","doi-asserted-by":"publisher","first-page":"1859","DOI":"10.1002\/jmri.27297","volume":"52","author":"S Li","year":"2020","unstructured":"Li S, Liu J, Zhang F, Yang M, Zhang Z, Liu J, Zhang Y, Hilbert T, Kober T, Cheng J, Zhu J (2020) Novel T2 Mapping for evaluating cervical cancer features by providing quantitative T2 maps and synthetic morphologic images: a Preliminary Study. J Magn Reson Imaging 52:1859","journal-title":"J Magn Reson Imaging"},{"key":"5908_CR10","first-page":"108","volume":"25","author":"A Tomasevic","year":"2020","unstructured":"Tomasevic A, Plesinac Karapandzic V, Stojanovic Rundic S, Vuckovic S, Milinkovic P, Gavrilovic D, Marjanovic D, Stanic D, Mikovic M, Petrasinovic P (2020) 3D MRI-based evaluation of the 2D brachytherapy planning in patients with advanced cervical cancer: An analysis of the delivered dose. J B.U.ON. Off J Balk Union Oncol 25:108\u2013115","journal-title":"Off J Balk Union Oncol"},{"key":"5908_CR11","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/s13244-012-0196-6","volume":"3","author":"F Davnall","year":"2012","unstructured":"Davnall F, Yip CS, Ljungqvist G, Selmi M, Ng F, Sanghera B, Ganeshan B, Miles KA, Cook GJ, Goh V (2012) Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights imaging 3:573\u2013589","journal-title":"Insights imaging"},{"key":"5908_CR12","doi-asserted-by":"publisher","first-page":"3585","DOI":"10.1007\/s00330-019-06655-1","volume":"30","author":"T Wang","year":"2020","unstructured":"Wang T, Gao T, Guo H, Wang Y, Zhou X, Tian J, Huang L, Zhang M (2020) Preoperative prediction of parametrial invasion in early-stage cervical cancer with MRI-based radiomics nomogram. Eur Radiol 30:3585\u20133593","journal-title":"Eur Radiol"},{"key":"5908_CR13","doi-asserted-by":"publisher","first-page":"1393","DOI":"10.3389\/fonc.2020.01393","volume":"10","author":"L Hou","year":"2020","unstructured":"Hou L, Zhou W, Ren J, Du X, Xin L, Zhao X, Cui Y, Zhang R (2020) Radiomics analysis of multiparametric MRI for the preoperative prediction of lymph node metastasis in cervical cancer. Front Oncol 10:1393","journal-title":"Front Oncol"},{"key":"5908_CR14","doi-asserted-by":"publisher","first-page":"563","DOI":"10.3389\/fonc.2020.00563","volume":"10","author":"M Fang","year":"2020","unstructured":"Fang M, Kan Y, Dong D, Yu T, Zhao N, Jiang W, Zhong L, Hu C, Luo Y, Tian J (2020) Multi-habitat based radiomics for the prediction of treatment response to concurrent chemotherapy and radiation therapy in locally advanced cervical cancer. Front Oncol 10:563","journal-title":"Front Oncol"},{"key":"5908_CR15","doi-asserted-by":"publisher","first-page":"102985","DOI":"10.1016\/j.critrevonc.2020.102985","volume":"152","author":"Y Ai","year":"2020","unstructured":"Ai Y, Zhu H, Xie C, Jin X (2020) Radiomics in cervical cancer: Current applications and future potential. Crit Rev Oncol\/hematol 152:102985","journal-title":"Crit Rev Oncol\/hematol"},{"key":"5908_CR16","doi-asserted-by":"publisher","first-page":"464","DOI":"10.3389\/fonc.2020.00464","volume":"10","author":"T Dong","year":"2020","unstructured":"Dong T, Yang C, Cui B, Zhang T, Sun X, Song K, Wang L, Kong B, Yang X (2020) Development and validation of a deep learning radiomics model predicting lymph node status in operable cervical cancer. Front Oncol 10:464","journal-title":"Front Oncol"},{"key":"5908_CR17","doi-asserted-by":"publisher","first-page":"109214","DOI":"10.1016\/j.ejrad.2020.109214","volume":"131","author":"CP Reinert","year":"2020","unstructured":"Reinert CP, Krieg EM, B\u00f6sm\u00fcller H, Horger M (2020) Mid-term response assessment in multiple myeloma using a texture analysis approach on dual energy-CT-derived bone marrow images: a proof of principle study. Eur J Radiol 131:109214","journal-title":"Eur J Radiol"},{"key":"5908_CR18","doi-asserted-by":"publisher","first-page":"1042","DOI":"10.3389\/fonc.2020.01042","volume":"10","author":"B Yang","year":"2020","unstructured":"Yang B, Zhong J, Zhong J, Ma L, Li A, Ji H, Zhou C, Duan S, Wang Q, Zhu C, Tian J, Zhang L, Wang F, Zhu H, Lu G (2020) Development and validation of a radiomics nomogram based on (18)F-fluorodeoxyglucose positron emission tomography\/computed tomography and clinicopathological factors to predict the survival outcomes of patients with non-small cell lung cancer. Front Oncol 10:1042","journal-title":"Front Oncol"},{"key":"5908_CR19","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563\u2013577","journal-title":"Radiology"},{"key":"5908_CR20","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.3109\/0284186X.2015.1061214","volume":"54","author":"RT Leijenaar","year":"2015","unstructured":"Leijenaar RT, Carvalho S, Hoebers FJ, Aerts HJ, van Elmpt WJ, Huang SH, Chan B, Waldron JN, O\u2019Sullivan B, Lambin P (2015) External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma. Acta Oncol 54:1423\u20131429 (Stockholm, Sweden)","journal-title":"Acta Oncol"},{"key":"5908_CR21","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","volume":"48","author":"P Lambin","year":"2012","unstructured":"Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A, Aerts HJ (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441\u2013446 (Oxford, England\u202f: 1990)","journal-title":"Eur J Cancer"},{"key":"5908_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07250-5","author":"H Chu","year":"2020","unstructured":"Chu H, Liu Z, Liang W, Zhou Q, Zhang Y, Lei K, Tang M, Cao Y, Chen S, Peng S, Kuang M (2020) Radiomics using CT images for preoperative prediction of futile resection in intrahepatic cholangiocarcinoma. Eur Radiol. https:\/\/doi.org\/10.1007\/s00330-020-07250-5","journal-title":"Eur Radiol"},{"key":"5908_CR23","unstructured":"P. Fontaine, F.G. Riet, J. Castelli, K. Gnep, A. Depeursinge, R. Crevoisier, O. Acosta (2020) Comparison of feature selection in radiomics for the prediction of overall survival after radiotherapy for hepatocellular carcinoma. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2020 1667\u20131670."},{"key":"5908_CR24","doi-asserted-by":"crossref","unstructured":"Tang Z, Zhao W, Xie X, Zhong Z, Shi F, Liu J, Shen D (2020) Severity assessment of COVID-19 using CT image features and laboratory indices. Phys Med Biol","DOI":"10.1088\/1361-6560\/abbf9e"},{"key":"5908_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/s00261-020-02833-8","author":"M Saleh","year":"2020","unstructured":"Saleh M, Bhosale PR, Yano M, Itani M, Elsayes AK, Halperin D, Bergsland EK, Morani AC (2020) New frontiers in imaging including radiomics updates for pancreatic neuroendocrine neoplasms. Abdom Radiol (NY). https:\/\/doi.org\/10.1007\/s00261-020-02833-8","journal-title":"Abdom Radiol (NY)"},{"key":"5908_CR26","doi-asserted-by":"publisher","first-page":"109219","DOI":"10.1016\/j.ejrad.2020.109219","volume":"131","author":"S Zhang","year":"2020","unstructured":"Zhang S, Song M, Zhao Y, Xu S, Sun Q, Zhai G, Liang D, Wu G, Li ZC (2020) Radiomics nomogram for preoperative prediction of progression-free survival using diffusion-weighted imaging in patients with muscle-invasive bladder cancer. Eur J Radiol 131:109219","journal-title":"Eur J Radiol"},{"key":"5908_CR27","doi-asserted-by":"publisher","first-page":"4731532","DOI":"10.1155\/2019\/4731532","volume":"2019","author":"K Cao","year":"2019","unstructured":"Cao K, Zhao B, Li XT, Li YL, Sun YS (2019) Texture analysis of dynamic contrast-enhanced mri in evaluating pathologic complete response (pCR) of mass-like breast cancer after neoadjuvant therapy. J Oncol 2019:4731532","journal-title":"J Oncol"},{"key":"5908_CR28","doi-asserted-by":"publisher","first-page":"37125","DOI":"10.18632\/oncotarget.26437","volume":"9","author":"NA Parra","year":"2018","unstructured":"Parra NA, Lu H, Li Q, Stoyanova R, Pollack A, Punnen S, Choi J, Abdalah M, Lopez C, Gage K, Park JY, Kosj Y, Pow-Sang JM, Gillies RJ, Balagurunathan Y (2018) Predicting clinically significant prostate cancer using DCE-MRI habitat descriptors. Oncotarget 9:37125\u201337136","journal-title":"Oncotarget"},{"key":"5908_CR29","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.radonc.2019.04.035","volume":"138","author":"Q Wu","year":"2019","unstructured":"Wu Q, Wang S, Chen X, Wang Y, Dong L, Liu Z, Tian J, Wang M (2019) Radiomics analysis of magnetic resonance imaging improves diagnostic performance of lymph node metastasis in patients with cervical cancer. Radiother Oncol 138:141\u2013148","journal-title":"Radiother Oncol"},{"key":"5908_CR30","doi-asserted-by":"publisher","first-page":"41","DOI":"10.5114\/jcb.2019.83285","volume":"11","author":"B Liu","year":"2019","unstructured":"Liu B, Ma WL, Zhang GW, Sun Z, Zhong JM, Wei MQ, Yang H, Wei LC, Huan Y (2019) Changes in magnetic resonance T2-weighted imaging signal intensity correlate with concurrent chemoradiotherapy response in cervical cancer. J Contemp Brachyther 11:41\u201347","journal-title":"J Contemp Brachyther"},{"key":"5908_CR31","first-page":"93","volume":"34","author":"YC Lin","year":"2011","unstructured":"Lin YC, Lin G, Chen YR, Yen TC, Wang CC, Ng KK (2011) Role of magnetic resonance imaging and apparent diffusion coefficient at 3T in distinguishing between adenocarcinoma of the uterine cervix and endometrium. Chang Gung Med J 34:93\u2013100","journal-title":"Chang Gung Med J"},{"key":"5908_CR32","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1016\/j.ygyno.2014.12.038","volume":"136","author":"SS Patankar","year":"2015","unstructured":"Patankar SS, Tergas AI, Deutsch I, Burke WM, Hou JY, Ananth CV, Huang Y, Neugut AI, Hershman DL, Wright JD (2015) High versus low-dose rate brachytherapy for cervical cancer. Gynecol Oncol 136:534\u2013541","journal-title":"Gynecol Oncol"},{"key":"5908_CR33","first-page":"122","volume":"38","author":"M Avanzo","year":"2017","unstructured":"Avanzo M, Stancanello J, El Naqa I (2017) Beyond imaging: The promise of radiomics. Physica medica PM Int J Devot Appl Phys Med Biol Off J Ital Assoc Biomed Phys (AIFB) 38:122\u2013139","journal-title":"Physica medica PM Int J Devot Appl Phys Med Biol Off J Ital Assoc Biomed Phys (AIFB)"},{"key":"5908_CR34","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.27390","author":"L Yang","year":"2020","unstructured":"Yang L, Zheng T, Dong Y, Wang Z, Liu D, Du J, Wu S, Shi Q, Liu L (2020) MRI texture-based models for predicting mitotic index and risk classification of gastrointestinal stromal tumors. J Magn Reson Imaging. https:\/\/doi.org\/10.1002\/jmri.27390","journal-title":"J Magn Reson Imaging"},{"key":"5908_CR35","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45:5\u201332","journal-title":"Mach Learn"},{"key":"5908_CR36","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1023\/A:1007607513941","volume":"40","author":"TG Dietterich","year":"2000","unstructured":"Dietterich TG (2000) An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn 40:139\u2013157","journal-title":"Mach Learn"},{"key":"5908_CR37","first-page":"1","volume":"7","author":"H Li","year":"2018","unstructured":"Li H, Panwar B, Omenn GS, Guan Y (2018) Accurate prediction of personalized olfactory perception from large-scale chemoinformatic features. GigaScience 7:1\u201311","journal-title":"GigaScience"},{"key":"5908_CR38","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.1073\/pnas.1711236115","volume":"115","author":"S Basu","year":"2018","unstructured":"Basu S, Kumbier K, Brown JB, Yu B (2018) Iterative random forests to discover predictive and stable high-order interactions. Proc Natl Acad Sci USA 115:1943\u20131948","journal-title":"Proc Natl Acad Sci USA"},{"key":"5908_CR39","doi-asserted-by":"publisher","first-page":"1770","DOI":"10.1080\/01621459.2015.1036994","volume":"110","author":"R Zhu","year":"2015","unstructured":"Zhu R, Zeng D, Kosorok MR (2015) Reinforcement learning trees. J Am Stat Assoc 110:1770\u20131784","journal-title":"J Am Stat Assoc"},{"key":"5908_CR40","doi-asserted-by":"publisher","first-page":"5979","DOI":"10.3390\/s20215979","volume":"20","author":"P Lipinski","year":"2020","unstructured":"Lipinski P, Brzychczy E, Zimroz R (2020) Decision tree-based classification for Planetary Gearboxes\u2019 condition monitoring with the use of vibration data in multidimensional symptom space. Sensors 20:5979 (Basel, Switzerland)","journal-title":"Sensors"}],"container-title":["Archives of Gynecology and Obstetrics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00404-020-05908-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00404-020-05908-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00404-020-05908-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T15:23:59Z","timestamp":1615821839000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00404-020-05908-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,4]]},"references-count":40,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["5908"],"URL":"https:\/\/doi.org\/10.1007\/s00404-020-05908-5","relation":{},"ISSN":["0932-0067","1432-0711"],"issn-type":[{"value":"0932-0067","type":"print"},{"value":"1432-0711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,4]]},"assertion":[{"value":"20 August 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Author Qinglei Shi was employed by the company Siemens Ltd.. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Data collection protocols were approved by the Ethics Committee of Qinhuangdao Municipal No.1 hospital, and the need for informed consent was waived.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"All participants in this retrospective study had anonymous information and the need for informed consent was waived.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}}]}}