{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T05:05:41Z","timestamp":1774328741644,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T00:00:00Z","timestamp":1643414400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T00:00:00Z","timestamp":1643414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s10489-021-03053-3","type":"journal-article","created":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T00:02:43Z","timestamp":1643414563000},"page":"11827-11845","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Dynamic radiomics: A new methodology to extract quantitative time-related features from tomographic images"],"prefix":"10.1007","volume":"52","author":[{"given":"Hui","family":"Qu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruichuan","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuqin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengying","family":"Che","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoran","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weixing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0585-9813","authenticated-orcid":false,"given":"Xiaoyu","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,29]]},"reference":[{"key":"3053_CR1","doi-asserted-by":"publisher","first-page":"10S","DOI":"10.2967\/jnumed.110.085639","volume":"2","author":"HR Schelbert","year":"2011","unstructured":"Schelbert HR (2011) Nuclear Medicine at a Crossroads. Journal of Nuclear Medicine, Suppl 2:10S\u20135S","journal-title":"Journal of Nuclear Medicine, Suppl"},{"issue":"9181","key":"3053_CR2","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1016\/S0140-6736(99)80041-5","volume":"354","author":"JF Eary","year":"1999","unstructured":"Eary JF (1999) Nuclear medicine in cancer diagnosis. Lancet 354(9181):853\u2013857","journal-title":"Lancet"},{"key":"3053_CR3","doi-asserted-by":"publisher","first-page":"4006","DOI":"10.1038\/ncomms5006","volume":"5","author":"H Aerts","year":"2014","unstructured":"Aerts H, Velazquez ER, Leijenaar RTH, Parmar C, Grossmann P, Cavalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications 5:4006","journal-title":"Nature Communications"},{"issue":"9","key":"3053_CR4","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1016\/j.mri.2012.06.010","volume":"30","author":"V Kumar","year":"2012","unstructured":"Kumar V, Gu YH, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts H, Dekker A, Fenstermacher D, Goldgof DB, Hall LO, Lambin P, Balagurunathan Y, Gatenby RA, Gillies RJ (2012) Radiomics: the process and the challenges. Magnetic Resonance Imaging 30(9):1234\u20131248","journal-title":"Magnetic Resonance Imaging"},{"issue":"12","key":"3053_CR5","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","volume":"14","author":"P Lambin","year":"2017","unstructured":"Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nature Reviews Clinical Oncology 14(12):749\u2013762","journal-title":"Nature Reviews Clinical Oncology"},{"issue":"4","key":"3053_CR6","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 R, Granton P, Zegers CML, Gillies R, Boellard R, Dekker A, Aerts H (2012) Radiomics: Extracting more information from medical images using advanced feature analysis. European Journal of Cancer 48(4):441\u2013446","journal-title":"European Journal of Cancer"},{"key":"3053_CR7","doi-asserted-by":"crossref","unstructured":"Hu C, He S, Wang Y (2020) A classification method to detect faults in a rotating machinery based on kernelled support tensor machine and multilinear principal component analysis, Applied Intelligence","DOI":"10.1007\/s10489-020-02011-9"},{"key":"3053_CR8","doi-asserted-by":"crossref","unstructured":"Hu C, Wang Y, Gu J (2020) Cross-domain intelligent fault classification of bearings based on tensor-aligned invariant subspace learning and two-dimensional convolutional neural networks, Knowledge-Based Systems, Volume 209","DOI":"10.1016\/j.knosys.2020.106214"},{"issue":"1070","key":"3053_CR9","doi-asserted-by":"publisher","first-page":"20160665","DOI":"10.1259\/bjr.20160665","volume":"90","author":"R Larue","year":"2017","unstructured":"Larue R, Defraene G, De Ruysscher D, Lambin P, Van Elmpt W (2017) Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. British Journal of Radiology 90(1070):20160665","journal-title":"British Journal of Radiology"},{"issue":"6","key":"3053_CR10","doi-asserted-by":"publisher","first-page":"1162","DOI":"10.1016\/j.patcog.2008.08.011","volume":"42","author":"I El Naqa","year":"2009","unstructured":"El Naqa I, Grigsby PW, Apte A, Kidd E, Donnelly E, Khullar D, Chaudhari S, Yang D, Schmitt M, Laforest R, Thorstad WL, Deasy JO (2009) Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognition 42(6):1162\u20131171","journal-title":"Pattern Recognition"},{"issue":"3","key":"3053_CR11","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1109\/TBME.2013.2284600","volume":"61","author":"G Thibault","year":"2014","unstructured":"Thibault G, Angulo J, Meyer F (2014) Advanced Statistical Matrices for Texture Characterization: Application to Cell Classification. IEEE Transactions on Biomedical Engineering 61(3):630\u2013637","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"6","key":"3053_CR12","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","volume":"3","author":"RM Haralick","year":"1973","unstructured":"Haralick RM, Shanmugan K, Dinstein I (1973) Textural features for image classification. IEEE Transactions on System Man Cybern 3(6):610\u2013621","journal-title":"IEEE Transactions on System Man Cybern"},{"key":"3053_CR13","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/S0146-664X(75)80008-6","volume":"4","author":"MM Galloway","year":"1974","unstructured":"Galloway MM (1974) Texture classification using gray-level run lengths. Computer Graphics and Image Processing 4:172\u2013179","journal-title":"Computer Graphics and Image Processing"},{"issue":"3","key":"3053_CR14","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/j.radonc.2015.02.015","volume":"114","author":"TP Coroller","year":"2015","unstructured":"Coroller TP, Grossmann P, Hou Y, Velazquez ER, Leijenaar RTH, Hermann G, Lambin P, Haibe-Kains B, Mak RH, Aerts H (2015) CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiotherapy and Oncology 114(3):345\u201350","journal-title":"Radiotherapy and Oncology"},{"key":"3053_CR15","first-page":"203960","volume":"5","author":"E Verburg","year":"2021","unstructured":"Verburg E, van Gils CH, van der Velden BHM, Bakker MF, Pijnappel RM, Veldhuis WB, Gilhuijs KGA (2021) Deep Learning for Automated Triaging of 4581 Breast MRI Examinations from the DENSE Trial. Radiology 5:203960","journal-title":"Radiology"},{"key":"3053_CR16","doi-asserted-by":"crossref","unstructured":"Gao R, Zhao S, Aishanjiang K, Cai H, Wei T, Zhang Y, Liu Z, Zhou J, Han B, Wang J, Ding H, Liu Y, Xu X, Yu Z, Gu J (2021) Deep learning for differential diagnosis of malignant hepatic tumors based on multi-phase contrast-enhanced CT and clinical data, J Hematol Oncol, 26;14(1):154","DOI":"10.1186\/s13045-021-01167-2"},{"key":"3053_CR17","doi-asserted-by":"publisher","first-page":"101772","DOI":"10.1016\/j.media.2020.101772","volume":"65","author":"X Xu","year":"2020","unstructured":"Xu X, Wang C, Guo J, Gan Y, Wang J, Bai H, Zhang L, Li W, Yi Z (2020) MSCS-DeepLN: Evaluating lung nodule malignancy using multi-scale cost-sensitive neural networks. Med Image Anal 65:101772","journal-title":"Med Image Anal"},{"issue":"3","key":"3053_CR18","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1109\/TMI.2020.3043641","volume":"40","author":"A Rossi","year":"2021","unstructured":"Rossi A, Hosseinzadeh M, Bianchini M, Scarselli F, Huisman H (2021) Multi-Modal Siamese Network for Diagnostically Similar Lesion Retrieval in Prostate MRI. IEEE Trans Med Imaging 40(3):986\u2013995","journal-title":"IEEE Trans Med Imaging"},{"issue":"4","key":"3053_CR19","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1109\/TMI.2017.2776967","volume":"37","author":"G Wu","year":"2018","unstructured":"Wu G, Chen Y, Wang Y et al (2018) Sparse Representation-Based Radiomics for the Diagnosis of Brain Tumors. IEEE Transactions on Medical Imaging 37(4):893\u2013905","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"7","key":"3053_CR20","doi-asserted-by":"publisher","first-page":"1678","DOI":"10.1109\/TMI.2018.2794918","volume":"37","author":"A Arnaud","year":"2017","unstructured":"Arnaud A, Forbes F, Coquery N et al (2017) Fully Automatic Lesion Localization and Characterization: Application to Brain Tumors Using Multiparametric MRI Data. IEEE Transactions on Medical Imaging 37(7):1678\u20131689","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"5","key":"3053_CR21","doi-asserted-by":"publisher","first-page":"2196","DOI":"10.1007\/s00330-018-5770-y","volume":"29","author":"Yang Lifeng","year":"2019","unstructured":"Lifeng Yang (2019) Jingbo, et al, Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients. European Radiology 29(5):2196\u20132206","journal-title":"European Radiology"},{"issue":"12","key":"3053_CR22","doi-asserted-by":"publisher","first-page":"2620","DOI":"10.1109\/TMI.2016.2591921","volume":"35","author":"P Cirujeda","year":"2016","unstructured":"Cirujeda P, Cid YD, Mller H et al (2016) A 3-D Riesz-Covariance Texture Model for Prediction of Nodule Recurrence in Lung CT. IEEE Trans Med Imaging 35(12):2620\u20132630","journal-title":"IEEE Trans Med Imaging"},{"issue":"11","key":"3053_CR23","doi-asserted-by":"publisher","first-page":"4514","DOI":"10.1007\/s00330-018-5463-6","volume":"28","author":"R Ortiz-Ramon","year":"2018","unstructured":"Ortiz-Ramon R, Larroza A, Ruiz-Espana S, Arana E, Moratal D (2018) Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. European Radiology 28(11):4514\u20134523","journal-title":"European Radiology"},{"issue":"5","key":"3053_CR24","doi-asserted-by":"publisher","first-page":"2196","DOI":"10.1007\/s00330-018-5770-y","volume":"29","author":"L Yang","year":"2019","unstructured":"Yang L, Yang J, Zhou X (2019) Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients. European Radiology 29(5):2196\u20132206","journal-title":"European Radiology"},{"issue":"8","key":"3053_CR25","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.3390\/diagnostics11081384","volume":"11","author":"Y Dai","year":"2021","unstructured":"Dai Y, Gao Y, Liu F (2021) TransMed: Transformers Advance Multi-Modal Medical Image Classification. Diagnostics (Basel) 11(8):1384","journal-title":"Diagnostics (Basel)"},{"issue":"2","key":"3053_CR26","first-page":"563","volume":"78","author":"RJ Gillies","year":"2016","unstructured":"Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: Images Are More than Pictures. They Are Data, Radiology 78(2):563\u201377","journal-title":"They Are Data, Radiology"},{"issue":"2","key":"3053_CR27","doi-asserted-by":"publisher","first-page":"23","DOI":"10.5430\/jbgc.v2n2p23","volume":"2","author":"R Fusco","year":"2012","unstructured":"Fusco R, Sansone M, Maffei S, Raiano N, Petrillo A (2012) Dynamic contrast-enhanced mri in breast cancer: A comparison between distributed and compartmental tracer kinetic models. J. Biomedical Graphics and Computing 2(2):23\u201336","journal-title":"J. Biomedical Graphics and Computing"},{"issue":"8","key":"3053_CR28","first-page":"1064","volume":"53","author":"J Kallehauge","year":"2014","unstructured":"Kallehauge J, Tanderup K, Duan C et al (2014) Tracer kinetic model se-lection for dynamic contrast-enhanced magnetic resonance imaging of locally advanced cervical cancer 53(8):1064\u201372","journal-title":"Tracer kinetic model se-lection for dynamic contrast-enhanced magnetic resonance imaging of locally advanced cervical cancer"},{"issue":"3","key":"3053_CR29","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1016\/j.ijrobp.2011.08.011","volume":"83","author":"N Mayr","year":"2012","unstructured":"Mayr N, Huang Z, Jian Z et al (2012) Characterizing tumor heterogeneity with functional imaging and quantifying high-risk tumor volume for early prediction of treatment outcome: Cervical cancer as a model. Int J Radiat OncolBiol Phys 83(3):972\u20139","journal-title":"Int J Radiat OncolBiol Phys"},{"issue":"1","key":"3053_CR30","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1186\/s13058-017-0846-1","volume":"19","author":"NM Braman","year":"2017","unstructured":"Braman NM, Etesami M, Prasanna P, Dubchuk C, Gilmore H, Tiwari P, Pletcha D, Madabhushi A (2017) Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Research 19(1):57","journal-title":"Breast Cancer Research"},{"issue":"2","key":"3053_CR31","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1148\/radiol.2016152110","volume":"281","author":"H Li","year":"2016","unstructured":"Li H, Zhu YT, Burnside ES, Drukker K, Hoadley KA, Fan C, Conzen SD, Whitman GJ, Sutton EJ, Net JM, Ganott M, Huang E, Morris EA, Perou CM, Ji Y, Giger ML (2016) MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays. Radiology 281(2):382\u2013391","journal-title":"Radiology"},{"key":"3053_CR32","doi-asserted-by":"crossref","unstructured":"Boldrini L, Cusumano D, Chiloiro G, et al (2019) Delta radiomics for rectal cancer response prediction with hybrid 0.35T magnetic resonance-guided radiotherapy (MRgRT): a hypothesis-generating study for an innovative personalized medicine approach, La Radiologia Medica, 124(2):145-153","DOI":"10.1007\/s11547-018-0951-y"},{"key":"3053_CR33","doi-asserted-by":"publisher","first-page":"S20","DOI":"10.1016\/S0167-8140(16)30042-1","volume":"118","author":"S Carvalho","year":"2016","unstructured":"Carvalho S et al (2016) Early variation of FDG-PET radiomics features in NSCLC is related to overall survival - the \u2018delta radiomics concept\u2019. Radiotherapy and Oncology 118:S20\u2013S21","journal-title":"Radiotherapy and Oncology"},{"issue":"2","key":"3053_CR34","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1177\/2050640615601603","volume":"4","author":"SX Rao","year":"2015","unstructured":"Rao SX, Lambregts DM, Schnerr RS et al (2015) CT texture analysis in colorectal liver metastases: A better way than size and volume measurements to assess response to chemotherapy? United European Gastroenterology Journal 4(2):257\u201363","journal-title":"United European Gastroenterology Journal"},{"key":"3053_CR35","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/s41698-019-0096-z","volume":"3","author":"H Nasief","year":"2019","unstructured":"Nasief H, Zheng C, Schott D et al (2019) A machine learning based delta-radiomics process for early prediction of treatment response of pancreatic cancer. NPJ Precision Oncology 3:25","journal-title":"NPJ Precision Oncology"},{"key":"3053_CR36","doi-asserted-by":"crossref","unstructured":"Cunliffe, Alexandra, Armato, Samuel G, Castillo, Richard, et al (2015) Lung Texture in Serial Thoracic Computed Tomography Scans: Correlation of Radiomics-based Features With Radiation Therapy Dose and Radiation Pneumonitis Development, International Journal of Radiation Oncology, Biology, Physics, 91(5):1048-1056","DOI":"10.1016\/j.ijrobp.2014.11.030"},{"issue":"8","key":"3053_CR37","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","volume":"18","author":"A Hosny","year":"2018","unstructured":"Hosny A, Parmar C, Quackenbush J et al (2018) Artificial intelligence in radiology. Nature Reviews Cancer 18(8):500\u2013510","journal-title":"Nature Reviews Cancer"},{"key":"3053_CR38","doi-asserted-by":"crossref","unstructured":"Weigel MT, Weigel DMMT, Dowsett M (2010) Current and emerging biomarkers in breast cancer: prognosis and prediction. Endocr Relat Cancer 17: R245-R262, Endocrine Related Cancer, 17(4):R245-62","DOI":"10.1677\/ERC-10-0136"},{"issue":"1","key":"3053_CR39","doi-asserted-by":"publisher","first-page":"2240","DOI":"10.1038\/s41598-019-38502-0","volume":"9","author":"X Cui","year":"2019","unstructured":"Cui X, Wang N, Zhao Y et al (2019) Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Radiomics Features of DCE-MRI. Scientific Reports 9(1):2240","journal-title":"Scientific Reports"},{"issue":"7","key":"3053_CR40","doi-asserted-by":"publisher","first-page":"3820","DOI":"10.1007\/s00330-018-5981-2","volume":"29","author":"L Han","year":"2019","unstructured":"Han L, Zhu Y, Liu Z et al (2019) Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer. European Radiology 29(7):3820\u20133829","journal-title":"European Radiology"},{"issue":"9","key":"3053_CR41","doi-asserted-by":"publisher","first-page":"2157","DOI":"10.1158\/1078-0432.CCR-14-2821","volume":"21","author":"S Arena","year":"2015","unstructured":"Arena S, Bellosillo B, Siravegna G et al (2015) Emergence of Multiple EGFR Extracellular Mutations during Cetuximab Treatment in Colorectal Cancer. Clinical Cancer Research 21(9):2157\u20132166","journal-title":"Clinical Cancer Research"},{"issue":"20","key":"3053_CR42","doi-asserted-by":"publisher","first-page":"6580","DOI":"10.1158\/1078-0432.CCR-07-4310","volume":"14","author":"ML Ahsee","year":"2008","unstructured":"Ahsee ML, Makris A, Taylor NJ et al (2008) Early changes in functional dynamic magnetic resonance imaging predict for pathologic response to neoadjuvant chemotherapy in primary breast cancer. Clinical Cancer Research An Official Journal of the American Association for Cancer Research 14(20):6580","journal-title":"Clinical Cancer Research An Official Journal of the American Association for Cancer Research"},{"issue":"1","key":"3053_CR43","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1186\/s13058-017-0846-1","volume":"19","author":"NM Braman","year":"2017","unstructured":"Braman NM, Etesami M, Prasanna P et al (2017) Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Research 19(1):57","journal-title":"Breast Cancer Research"},{"issue":"1","key":"3053_CR44","first-page":"56","volume":"2","author":"W Huang","year":"2016","unstructured":"Huang W, Chen Y, Fedorov A et al (2016) The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge. Tomography A Journal for Imaging Research 2(1):56","journal-title":"Tomography A Journal for Imaging Research"},{"issue":"2","key":"3053_CR45","doi-asserted-by":"publisher","first-page":"191145","DOI":"10.1148\/radiol.2020191145","volume":"295","author":"A Zwanenburg","year":"2020","unstructured":"Zwanenburg A, Vallires M, Abdalah MA et al (2020) The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology 295(2):191145","journal-title":"Radiology"},{"issue":"8","key":"3053_CR46","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Computation 9(8):1735","journal-title":"Neural Computation"},{"issue":"9","key":"3053_CR47","doi-asserted-by":"publisher","first-page":"2201","DOI":"10.3390\/cancers13092201","volume":"13","author":"H Bellio","year":"2021","unstructured":"Bellio H, Fumet JD, Ghiringhelli F (2021) Targeting BRAF and RAS in Colorectal Cancer. Cancers (Basel) 13(9):2201","journal-title":"Cancers (Basel)"},{"key":"3053_CR48","doi-asserted-by":"crossref","unstructured":"F A R (1997) Fundamental Concepts in Pharmacokinetics, Pharmacological Research, 35(5):363-390","DOI":"10.1006\/phrs.1997.0175"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-03053-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-03053-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-03053-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T18:22:14Z","timestamp":1667067734000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-03053-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,29]]},"references-count":48,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["3053"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-03053-3","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,29]]},"assertion":[{"value":"27 November 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2022","order":2,"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":"The authors have declared that no competing interest exists.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}