Skip to main content
Top
Published in: BMC Gastroenterology 1/2023

Open Access 01-12-2023 | Colorectal Cancer | Research article

Repeatability of radiomics studies in colorectal cancer: a systematic review

Authors: Ying Liu, Xiaoqin Wei, Xu Feng, Yan Liu, Guiling Feng, Yong Du

Published in: BMC Gastroenterology | Issue 1/2023

Login to get access

Abstract

Background

Recently, radiomics has been widely used in colorectal cancer, but many variable factors affect the repeatability of radiomics research. This review aims to analyze the repeatability of radiomics studies in colorectal cancer and to evaluate the current status of radiomics in the field of colorectal cancer.

Methods

The included studies in this review by searching from the PubMed and Embase databases. Then each study in our review was evaluated using the Radiomics Quality Score (RQS). We analyzed the factors that may affect the repeatability in the radiomics workflow and discussed the repeatability of the included studies.

Results

A total of 188 studies was included in this review, of which only two (2/188, 1.06%) studies controlled the influence of individual factors. In addition, the median score of RQS was 11 (out of 36), range-1 to 27.

Conclusions

The RQS score was moderately low, and most studies did not consider the repeatability of radiomics features, especially in terms of Intra-individual, scanners, and scanning parameters. To improve the generalization of the radiomics model, it is necessary to further control the variable factors of repeatability.
Appendix
Available only for authorised users
Literature
1.
go back to reference Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-249 Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-249
2.
go back to reference Staal FCR, van der Reijd DJ, Taghavi M, Lambregts DMJ, Beets-Tan RGH, Maas M. Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review. Clin Colorectal Cancer. 2021;20(1):52–71.PubMedCrossRef Staal FCR, van der Reijd DJ, Taghavi M, Lambregts DMJ, Beets-Tan RGH, Maas M. Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review. Clin Colorectal Cancer. 2021;20(1):52–71.PubMedCrossRef
3.
go back to reference Limkin EJ, Sun R, Dercle L, Zacharaki EI, Robert C, Reuzé S, Schernberg A, Paragios N, Deutsch E, Ferté C. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol. 2017;28(6):1191–206.PubMedCrossRef Limkin EJ, Sun R, Dercle L, Zacharaki EI, Robert C, Reuzé S, Schernberg A, Paragios N, Deutsch E, Ferté C. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol. 2017;28(6):1191–206.PubMedCrossRef
4.
go back to reference Lambin P, Leijenaar RTH, Deist TM, Peerlings J, De Jong EEC, Van Timmeren J, Sanduleanu S, Larue RTHM, Even AJG, Jochems A, et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749–62.PubMedCrossRef Lambin P, Leijenaar RTH, Deist TM, Peerlings J, De Jong EEC, Van Timmeren J, Sanduleanu S, Larue RTHM, Even AJG, Jochems A, et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749–62.PubMedCrossRef
5.
go back to reference Forghani R, Savadjiev P, Chatterjee A, Muthukrishnan N, Reinhold C, Forghani B. Radiomics and Artificial Intelligence for Biomarker and Prediction Model Development in Oncology. Comput Struct Biotechnol J. 2019;17:995–1008.PubMedPubMedCentralCrossRef Forghani R, Savadjiev P, Chatterjee A, Muthukrishnan N, Reinhold C, Forghani B. Radiomics and Artificial Intelligence for Biomarker and Prediction Model Development in Oncology. Comput Struct Biotechnol J. 2019;17:995–1008.PubMedPubMedCentralCrossRef
8.
go back to reference Xue C, Yuan J, Lo GG, Chang ATY, Poon DMC, Wong OL, Zhou Y, Chu WCW. Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review. Quant Imaging Med Surg. 2021;11(10):4431–60.PubMedPubMedCentralCrossRef Xue C, Yuan J, Lo GG, Chang ATY, Poon DMC, Wong OL, Zhou Y, Chu WCW. Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review. Quant Imaging Med Surg. 2021;11(10):4431–60.PubMedPubMedCentralCrossRef
9.
go back to reference Mackin D, Fave X, Zhang L, Fried D, Yang J, Taylor B, Rodriguez-Rivera E, Dodge C, Jones AK, Court L. Measuring Computed Tomography Scanner Variability of Radiomics Features. Invest Radiol. 2015;50(11):757–65.PubMedPubMedCentralCrossRef Mackin D, Fave X, Zhang L, Fried D, Yang J, Taylor B, Rodriguez-Rivera E, Dodge C, Jones AK, Court L. Measuring Computed Tomography Scanner Variability of Radiomics Features. Invest Radiol. 2015;50(11):757–65.PubMedPubMedCentralCrossRef
10.
go back to reference Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, Dekker A, Fenstermacher D, et al. Radiomics: the process and the challenges. Magn Reson Imaging. 2012;30(9):1234–48.PubMedPubMedCentralCrossRef Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, Dekker A, Fenstermacher D, et al. Radiomics: the process and the challenges. Magn Reson Imaging. 2012;30(9):1234–48.PubMedPubMedCentralCrossRef
11.
go back to reference Berenguer R, Pastor-Juan MDR, Canales-Vázquez J, Castro-García M, Villas MV, Mansilla Legorburo F, Sabater S. Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters. Radiology. 2018;288(2):407–15.PubMedCrossRef Berenguer R, Pastor-Juan MDR, Canales-Vázquez J, Castro-García M, Villas MV, Mansilla Legorburo F, Sabater S. Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters. Radiology. 2018;288(2):407–15.PubMedCrossRef
12.
go back to reference Mackin D, Fave X, Zhang L, Yang J, Jones AK, Ng CS, Court L. Harmonizing the pixel size in retrospective computed tomography radiomics studies. PLoS ONE. 2017;12(9):e0178524.PubMedPubMedCentralCrossRef Mackin D, Fave X, Zhang L, Yang J, Jones AK, Ng CS, Court L. Harmonizing the pixel size in retrospective computed tomography radiomics studies. PLoS ONE. 2017;12(9):e0178524.PubMedPubMedCentralCrossRef
13.
go back to reference Shafiq-Ul-Hassan M, Zhang GG, Latifi K, Ullah G, Hunt DC, Balagurunathan Y, Abdalah MA, Schabath MB, Goldgof DG, Mackin D, et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys. 2017;44(3):1050–62.PubMedPubMedCentralCrossRef Shafiq-Ul-Hassan M, Zhang GG, Latifi K, Ullah G, Hunt DC, Balagurunathan Y, Abdalah MA, Schabath MB, Goldgof DG, Mackin D, et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys. 2017;44(3):1050–62.PubMedPubMedCentralCrossRef
14.
go back to reference Lu L, Ehmke RC, Schwartz LH, Zhao B. Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings. PLoS ONE. 2016;11(12):e0166550.PubMedPubMedCentralCrossRef Lu L, Ehmke RC, Schwartz LH, Zhao B. Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings. PLoS ONE. 2016;11(12):e0166550.PubMedPubMedCentralCrossRef
15.
go back to reference He L, Huang Y, Ma Z, Liang C, Liang C, Liu Z. Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule. Sci Rep. 2016;6:34921.PubMedPubMedCentralCrossRef He L, Huang Y, Ma Z, Liang C, Liang C, Liu Z. Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule. Sci Rep. 2016;6:34921.PubMedPubMedCentralCrossRef
16.
go back to reference Kim H, Park CM, Lee M, Park SJ, Song YS, Lee JH, Hwang EJ, Goo JM. Impact of Reconstruction Algorithms on CT Radiomic Features of Pulmonary Tumors: Analysis of Intra- and Inter-Reader Variability and Inter-Reconstruction Algorithm Variability. PLoS ONE. 2016;11(10):e0164924.PubMedPubMedCentralCrossRef Kim H, Park CM, Lee M, Park SJ, Song YS, Lee JH, Hwang EJ, Goo JM. Impact of Reconstruction Algorithms on CT Radiomic Features of Pulmonary Tumors: Analysis of Intra- and Inter-Reader Variability and Inter-Reconstruction Algorithm Variability. PLoS ONE. 2016;11(10):e0164924.PubMedPubMedCentralCrossRef
17.
go back to reference Traverso A, Kazmierski M, Shi Z, Kalendralis P, Welch M, Nissen HD, Jaffray D, Dekker A, Wee L. Stability of radiomic features of apparent diffusion coefficient (ADC) maps for locally advanced rectal cancer in response to image pre-processing. Phys Med. 2019;61:44–51.PubMedCrossRef Traverso A, Kazmierski M, Shi Z, Kalendralis P, Welch M, Nissen HD, Jaffray D, Dekker A, Wee L. Stability of radiomic features of apparent diffusion coefficient (ADC) maps for locally advanced rectal cancer in response to image pre-processing. Phys Med. 2019;61:44–51.PubMedCrossRef
18.
go back to reference Li Y, Han G, Wu X, Li Z, Zhao K, Zhang Z, Liu Z, Liang C: Normalization of multicenter CT radiomics by a generative adversarial network method. Phys Med Biol. 2021;(5):66. Li Y, Han G, Wu X, Li Z, Zhao K, Zhang Z, Liu Z, Liang C: Normalization of multicenter CT radiomics by a generative adversarial network method. Phys Med Biol. 2021;(5):66.
19.
go back to reference van Heeswijk MM, Lambregts DM, van Griethuysen JJ, Oei S, Rao SX, de Graaff CA, Vliegen RF, Beets GL, Papanikolaou N, Beets-Tan RG. Automated and Semiautomated Segmentation of Rectal Tumor Volumes on Diffusion-Weighted MRI: Can It Replace Manual Volumetry? Int J Radiat Oncol Biol Phys. 2016;94(4):824–31.PubMedCrossRef van Heeswijk MM, Lambregts DM, van Griethuysen JJ, Oei S, Rao SX, de Graaff CA, Vliegen RF, Beets GL, Papanikolaou N, Beets-Tan RG. Automated and Semiautomated Segmentation of Rectal Tumor Volumes on Diffusion-Weighted MRI: Can It Replace Manual Volumetry? Int J Radiat Oncol Biol Phys. 2016;94(4):824–31.PubMedCrossRef
20.
go back to reference Parmar C, Rios Velazquez E, Leijenaar R, Jermoumi M, Carvalho S, Mak RH, Mitra S, Shankar BU, Kikinis R, Haibe-Kains B, et al. Robust Radiomics feature quantification using semiautomatic volumetric segmentation. PLoS ONE. 2014;9(7):e102107.PubMedPubMedCentralCrossRef Parmar C, Rios Velazquez E, Leijenaar R, Jermoumi M, Carvalho S, Mak RH, Mitra S, Shankar BU, Kikinis R, Haibe-Kains B, et al. Robust Radiomics feature quantification using semiautomatic volumetric segmentation. PLoS ONE. 2014;9(7):e102107.PubMedPubMedCentralCrossRef
21.
go back to reference Day E, Betler J, Parda D, Reitz B, Kirichenko A, Mohammadi S, Miften M. A region growing method for tumor volume segmentation on PET images for rectal and anal cancer patients. Med Phys. 2009;36(10):4349–58.PubMedCrossRef Day E, Betler J, Parda D, Reitz B, Kirichenko A, Mohammadi S, Miften M. A region growing method for tumor volume segmentation on PET images for rectal and anal cancer patients. Med Phys. 2009;36(10):4349–58.PubMedCrossRef
22.
go back to reference Trebeschi S, van Griethuysen JJM, Lambregts DMJ, Lahaye MJ, Parmar C, Bakers FCH, Peters N, Beets-Tan RGH, Aerts H. Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR. Sci Rep. 2017;7(1):5301.PubMedPubMedCentralCrossRef Trebeschi S, van Griethuysen JJM, Lambregts DMJ, Lahaye MJ, Parmar C, Bakers FCH, Peters N, Beets-Tan RGH, Aerts H. Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR. Sci Rep. 2017;7(1):5301.PubMedPubMedCentralCrossRef
24.
go back to reference McInnes MDF, Moher D, Thombs BD, McGrath TA, Bossuyt PM. Group atP-D: Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement. JAMA. 2018;319(4):388–96.PubMedCrossRef McInnes MDF, Moher D, Thombs BD, McGrath TA, Bossuyt PM. Group atP-D: Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement. JAMA. 2018;319(4):388–96.PubMedCrossRef
25.
go back to reference Chetan MR, Gleeson FV. Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives. Eur Radiol. 2021;31(2):1049–58.PubMedCrossRef Chetan MR, Gleeson FV. Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives. Eur Radiol. 2021;31(2):1049–58.PubMedCrossRef
26.
go back to reference Babyak MA. What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosom Med. 2004;66(3):411–21.PubMed Babyak MA. What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosom Med. 2004;66(3):411–21.PubMed
27.
go back to reference Lovinfosse P, Polus M, Van Daele D, Martinive P, Daenen F, Hatt M, Visvikis D, Koopmansch B, Lambert F, Coimbra C, et al. FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer. Eur J Nucl Med Mol Imaging. 2018;45(3):365–75.PubMedCrossRef Lovinfosse P, Polus M, Van Daele D, Martinive P, Daenen F, Hatt M, Visvikis D, Koopmansch B, Lambert F, Coimbra C, et al. FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer. Eur J Nucl Med Mol Imaging. 2018;45(3):365–75.PubMedCrossRef
28.
go back to reference Ma X, Shen F, Jia Y, Xia Y, Li Q, Lu J. MRI-based radiomics of rectal cancer: preoperative assessment of the pathological features. BMC Med Imaging. 2019;19(1):86.PubMedPubMedCentralCrossRef Ma X, Shen F, Jia Y, Xia Y, Li Q, Lu J. MRI-based radiomics of rectal cancer: preoperative assessment of the pathological features. BMC Med Imaging. 2019;19(1):86.PubMedPubMedCentralCrossRef
29.
go back to reference Wang J, Shen L, Zhong H, Zhou Z, Hu P, Gan J, Luo R, Hu W, Zhang Z. Radiomics features on radiotherapy treatment planning CT can predict patient survival in locally advanced rectal cancer patients. Sci Rep. 2019;9(1):15346.PubMedPubMedCentralCrossRef Wang J, Shen L, Zhong H, Zhou Z, Hu P, Gan J, Luo R, Hu W, Zhang Z. Radiomics features on radiotherapy treatment planning CT can predict patient survival in locally advanced rectal cancer patients. Sci Rep. 2019;9(1):15346.PubMedPubMedCentralCrossRef
30.
go back to reference Nie K, Shi L, Chen Q, Hu X, Jabbour SK, Yue N, Niu T, Sun X. Rectal Cancer: Assessment of Neoadjuvant Chemoradiation Outcome based on Radiomics of Multiparametric MRI. Clin Cancer Res. 2016;22(21):5256–64.PubMedCrossRef Nie K, Shi L, Chen Q, Hu X, Jabbour SK, Yue N, Niu T, Sun X. Rectal Cancer: Assessment of Neoadjuvant Chemoradiation Outcome based on Radiomics of Multiparametric MRI. Clin Cancer Res. 2016;22(21):5256–64.PubMedCrossRef
31.
go back to reference Kang J, Lee JH, Lee HS, Cho ES, Park EJ, Baik SH, Lee KY, Park C, Yeu Y, Clemenceau JR, et al. Radiomics features of18f-fluorodeoxyglucose positron-emission tomography as a novel prognostic signature in colorectal cancer. Cancers. 2021;13(3):1–17.CrossRef Kang J, Lee JH, Lee HS, Cho ES, Park EJ, Baik SH, Lee KY, Park C, Yeu Y, Clemenceau JR, et al. Radiomics features of18f-fluorodeoxyglucose positron-emission tomography as a novel prognostic signature in colorectal cancer. Cancers. 2021;13(3):1–17.CrossRef
32.
go back to reference Rios Velazquez E, Aerts HJ, Gu Y, Goldgof DB, De Ruysscher D, Dekker A, Korn R, Gillies RJ, Lambin P. A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists’ delineations and with the surgical specimen. Radiother Oncol. 2012;105(2):167–73.PubMedCrossRef Rios Velazquez E, Aerts HJ, Gu Y, Goldgof DB, De Ruysscher D, Dekker A, Korn R, Gillies RJ, Lambin P. A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists’ delineations and with the surgical specimen. Radiother Oncol. 2012;105(2):167–73.PubMedCrossRef
33.
go back to reference van Dam IE. van Sörnsen de Koste JR, Hanna GG, Muirhead R, Slotman BJ, Senan S: Improving target delineation on 4-dimensional CT scans in stage I NSCLC using a deformable registration tool. Radiother Oncol. 2010;96(1):67–72.PubMedCrossRef van Dam IE. van Sörnsen de Koste JR, Hanna GG, Muirhead R, Slotman BJ, Senan S: Improving target delineation on 4-dimensional CT scans in stage I NSCLC using a deformable registration tool. Radiother Oncol. 2010;96(1):67–72.PubMedCrossRef
34.
go back to reference Heye T, Merkle EM, Reiner CS, Davenport MS, Horvath JJ, Feuerlein S, Breault SR, Gall P, Bashir MR, Dale BM, et al. Reproducibility of dynamic contrast-enhanced MR imaging. Part II. Comparison of intra- and interobserver variability with manual region of interest placement versus semiautomatic lesion segmentation and histogram analysis. Radiology. 2013;266(3):812–21.PubMedCrossRef Heye T, Merkle EM, Reiner CS, Davenport MS, Horvath JJ, Feuerlein S, Breault SR, Gall P, Bashir MR, Dale BM, et al. Reproducibility of dynamic contrast-enhanced MR imaging. Part II. Comparison of intra- and interobserver variability with manual region of interest placement versus semiautomatic lesion segmentation and histogram analysis. Radiology. 2013;266(3):812–21.PubMedCrossRef
35.
go back to reference Egger J, Kapur T, Fedorov A, Pieper S, Miller JV, Veeraraghavan H, Freisleben B, Golby AJ, Nimsky C, Kikinis R. GBM volumetry using the 3D Slicer medical image computing platform. Sci Rep. 2013;3:1364.PubMedPubMedCentralCrossRef Egger J, Kapur T, Fedorov A, Pieper S, Miller JV, Veeraraghavan H, Freisleben B, Golby AJ, Nimsky C, Kikinis R. GBM volumetry using the 3D Slicer medical image computing platform. Sci Rep. 2013;3:1364.PubMedPubMedCentralCrossRef
36.
go back to reference Tibermacine H, Rouanet P, Sbarra M, Forghani R, Reinhold C, Nougaret S. Radiomics modelling in rectal cancer to predict disease-free survival: evaluation of different approaches. Br J Surg. 2021;108(10):1243–50.PubMedCrossRef Tibermacine H, Rouanet P, Sbarra M, Forghani R, Reinhold C, Nougaret S. Radiomics modelling in rectal cancer to predict disease-free survival: evaluation of different approaches. Br J Surg. 2021;108(10):1243–50.PubMedCrossRef
37.
go back to reference Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures. They Are Data Radiology. 2016;278(2):563–77.PubMed Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures. They Are Data Radiology. 2016;278(2):563–77.PubMed
38.
go back to reference Clarke LP, Nordstrom RJ, Zhang H, Tandon P, Zhang Y, Redmond G, Farahani K, Kelloff G, Henderson L, Shankar L, et al. The Quantitative Imaging Network: NCI’s Historical Perspective and Planned Goals. Transl Oncol. 2014;7(1):1–4.PubMedPubMedCentralCrossRef Clarke LP, Nordstrom RJ, Zhang H, Tandon P, Zhang Y, Redmond G, Farahani K, Kelloff G, Henderson L, Shankar L, et al. The Quantitative Imaging Network: NCI’s Historical Perspective and Planned Goals. Transl Oncol. 2014;7(1):1–4.PubMedPubMedCentralCrossRef
39.
go back to reference Fotina I, Lütgendorf-Caucig C, Stock M, Pötter R, Georg D. Critical discussion of evaluation parameters for inter-observer variability in target definition for radiation therapy. Strahlenther Onkol. 2012;188(2):160–7.PubMedCrossRef Fotina I, Lütgendorf-Caucig C, Stock M, Pötter R, Georg D. Critical discussion of evaluation parameters for inter-observer variability in target definition for radiation therapy. Strahlenther Onkol. 2012;188(2):160–7.PubMedCrossRef
40.
go back to reference Nakanishi R, Akiyoshi T, Toda S, Murakami Y, Taguchi S, Oba K, Hanaoka Y, Nagasaki T, Yamaguchi T, Konishi T, et al. Radiomics Approach Outperforms Diameter Criteria for Predicting Pathological Lateral Lymph Node Metastasis After Neoadjuvant (Chemo)Radiotherapy in Advanced Low Rectal Cancer. Ann Surg Oncol. 2020;27(11):4273–83.PubMedCrossRef Nakanishi R, Akiyoshi T, Toda S, Murakami Y, Taguchi S, Oba K, Hanaoka Y, Nagasaki T, Yamaguchi T, Konishi T, et al. Radiomics Approach Outperforms Diameter Criteria for Predicting Pathological Lateral Lymph Node Metastasis After Neoadjuvant (Chemo)Radiotherapy in Advanced Low Rectal Cancer. Ann Surg Oncol. 2020;27(11):4273–83.PubMedCrossRef
41.
go back to reference Avanzo M, Stancanello J, El Naqa I. Beyond imaging: The promise of radiomics. Phys Med. 2017;38:122–39.PubMedCrossRef Avanzo M, Stancanello J, El Naqa I. Beyond imaging: The promise of radiomics. Phys Med. 2017;38:122–39.PubMedCrossRef
42.
go back to reference Chalkidou A, O’Doherty MJ, Marsden PK. False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review. PLoS ONE. 2015;10(5):e0124165.PubMedPubMedCentralCrossRef Chalkidou A, O’Doherty MJ, Marsden PK. False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review. PLoS ONE. 2015;10(5):e0124165.PubMedPubMedCentralCrossRef
43.
go back to reference Guyon IM, Andr, Elisseeff. An introduction to variable and feature selection [J]. J Mach Learn Res. 2003. Guyon IM, Andr, Elisseeff. An introduction to variable and feature selection [J]. J Mach Learn Res. 2003.
44.
go back to reference Boldrini L, Cusumano D, Chiloiro G, Casà C, Masciocchi C, Lenkowicz J, Cellini F, Dinapoli N, Azario L, Teodoli S, et al. Delta radiomics for rectal cancer response prediction with hybrid 0.35 T magnetic resonance-guided radiotherapy (MRgRT): a hypothesis-generating study for an innovative personalized medicine approach. Radiol Med. 2019;124(2):145–53.PubMedCrossRef Boldrini L, Cusumano D, Chiloiro G, Casà C, Masciocchi C, Lenkowicz J, Cellini F, Dinapoli N, Azario L, Teodoli S, et al. Delta radiomics for rectal cancer response prediction with hybrid 0.35 T magnetic resonance-guided radiotherapy (MRgRT): a hypothesis-generating study for an innovative personalized medicine approach. Radiol Med. 2019;124(2):145–53.PubMedCrossRef
45.
go back to reference Cusumano D, Boldrini L, Yadav P, Yu G, Musurunu B, Chiloiro G, Piras A, Lenkowicz J, Placidi L, Romano A, et al. Delta radiomics for rectal cancer response prediction using low field magnetic resonance guided radiotherapy: an external validation. Physica Med. 2021;84:186–91.CrossRef Cusumano D, Boldrini L, Yadav P, Yu G, Musurunu B, Chiloiro G, Piras A, Lenkowicz J, Placidi L, Romano A, et al. Delta radiomics for rectal cancer response prediction using low field magnetic resonance guided radiotherapy: an external validation. Physica Med. 2021;84:186–91.CrossRef
46.
go back to reference Hotta M, Minamimoto R, Gohda Y, Miwa K, Otani K, Kiyomatsu T, Yano H. Prognostic value of (18)F-FDG PET/CT with texture analysis in patients with rectal cancer treated by surgery. Ann Nucl Med. 2021;35(7):843–52.PubMedCrossRef Hotta M, Minamimoto R, Gohda Y, Miwa K, Otani K, Kiyomatsu T, Yano H. Prognostic value of (18)F-FDG PET/CT with texture analysis in patients with rectal cancer treated by surgery. Ann Nucl Med. 2021;35(7):843–52.PubMedCrossRef
47.
go back to reference Negreros-Osuna AA, Parakh A, Corcoran RB, Pourvaziri A, Kambadakone A, Ryan DP, Sahani DV. Radiomics Texture Features in Advanced Colorectal Cancer: Correlation with BRAF Mutation and 5-year Overall Survival. Radiol Imaging Cancer. 2020;2(5):e190084.PubMedPubMedCentralCrossRef Negreros-Osuna AA, Parakh A, Corcoran RB, Pourvaziri A, Kambadakone A, Ryan DP, Sahani DV. Radiomics Texture Features in Advanced Colorectal Cancer: Correlation with BRAF Mutation and 5-year Overall Survival. Radiol Imaging Cancer. 2020;2(5):e190084.PubMedPubMedCentralCrossRef
48.
go back to reference Liu Z, Meng X, Zhang H, Li Z, Liu J, Sun K, Meng Y, Dai W, Xie P, Ding Y, et al. Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer. Nat Commun. 2020;11(1):4308.PubMedPubMedCentralCrossRef Liu Z, Meng X, Zhang H, Li Z, Liu J, Sun K, Meng Y, Dai W, Xie P, Ding Y, et al. Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer. Nat Commun. 2020;11(1):4308.PubMedPubMedCentralCrossRef
49.
go back to reference Shayesteh SP, Alikhassi A, Fard Esfahani A, Miraie M, Geramifar P, Bitarafan-Rajabi A, Haddad P. Neo-adjuvant chemoradiotherapy response prediction using MRI based ensemble learning method in rectal cancer patients. Phys Med. 2019;62:111–9.PubMedCrossRef Shayesteh SP, Alikhassi A, Fard Esfahani A, Miraie M, Geramifar P, Bitarafan-Rajabi A, Haddad P. Neo-adjuvant chemoradiotherapy response prediction using MRI based ensemble learning method in rectal cancer patients. Phys Med. 2019;62:111–9.PubMedCrossRef
50.
go back to reference Zhang Z, Jiang X, Zhang R, Yu T, Liu S, Luo Y. Radiomics signature as a new biomarker for preoperative prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer. Diagn Interv Radiol (Ankara, Turkey). 2021;27(3):308–14.CrossRef Zhang Z, Jiang X, Zhang R, Yu T, Liu S, Luo Y. Radiomics signature as a new biomarker for preoperative prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer. Diagn Interv Radiol (Ankara, Turkey). 2021;27(3):308–14.CrossRef
52.
go back to reference Zwanenburg A, Vallières M, Abdalah MA, Aerts H, Andrearczyk V, Apte A, Ashrafinia S, Bakas S, Beukinga RJ, Boellaard R, et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiol. 2020;295(2):328–38.CrossRef Zwanenburg A, Vallières M, Abdalah MA, Aerts H, Andrearczyk V, Apte A, Ashrafinia S, Bakas S, Beukinga RJ, Boellaard R, et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiol. 2020;295(2):328–38.CrossRef
53.
go back to reference Buckler AJ, Bresolin L, Dunnick NR, Sullivan DC. A collaborative enterprise for multi-stakeholder participation in the advancement of quantitative imaging. Radiol. 2011;258(3):906–14.CrossRef Buckler AJ, Bresolin L, Dunnick NR, Sullivan DC. A collaborative enterprise for multi-stakeholder participation in the advancement of quantitative imaging. Radiol. 2011;258(3):906–14.CrossRef
54.
go back to reference van Timmeren JE, Leijenaar RTH, van Elmpt W, Wang J, Zhang Z, Dekker A, Lambin P. Test-Retest Data for Radiomics Feature Stability Analysis: Generalizable or Study-Specific? Tomogr. 2016;2(4):361–5.CrossRef van Timmeren JE, Leijenaar RTH, van Elmpt W, Wang J, Zhang Z, Dekker A, Lambin P. Test-Retest Data for Radiomics Feature Stability Analysis: Generalizable or Study-Specific? Tomogr. 2016;2(4):361–5.CrossRef
55.
go back to reference Balagurunathan Y, Kumar V, Gu Y, Kim J, Wang H, Liu Y, Goldgof DB, Hall LO, Korn R, Zhao B, et al. Test-retest reproducibility analysis of lung CT image features. J Digit Imaging. 2014;27(6):805–23.PubMedPubMedCentralCrossRef Balagurunathan Y, Kumar V, Gu Y, Kim J, Wang H, Liu Y, Goldgof DB, Hall LO, Korn R, Zhao B, et al. Test-retest reproducibility analysis of lung CT image features. J Digit Imaging. 2014;27(6):805–23.PubMedPubMedCentralCrossRef
56.
go back to reference Zhao B, Tan Y, Tsai WY, Qi J, Xie C, Lu L, Schwartz LH. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep. 2016;6:23428.PubMedPubMedCentralCrossRef Zhao B, Tan Y, Tsai WY, Qi J, Xie C, Lu L, Schwartz LH. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep. 2016;6:23428.PubMedPubMedCentralCrossRef
57.
go back to reference Shafiq-Ul-Hassan M, Latifi K, Zhang G, Ullah G, Gillies R, Moros E. Voxel size and gray level normalization of CT radiomic features in lung cancer. Sci Rep. 2018;8(1):10545.PubMedPubMedCentralCrossRef Shafiq-Ul-Hassan M, Latifi K, Zhang G, Ullah G, Gillies R, Moros E. Voxel size and gray level normalization of CT radiomic features in lung cancer. Sci Rep. 2018;8(1):10545.PubMedPubMedCentralCrossRef
58.
go back to reference Zwanenburg A, Leger S, Agolli L, Pilz K, Troost EGC, Richter C, Löck S. Assessing robustness of radiomic features by image perturbation. Sci Rep. 2019;9(1):614.PubMedPubMedCentralCrossRef Zwanenburg A, Leger S, Agolli L, Pilz K, Troost EGC, Richter C, Löck S. Assessing robustness of radiomic features by image perturbation. Sci Rep. 2019;9(1):614.PubMedPubMedCentralCrossRef
59.
go back to reference Lazar C, Meganck S, Taminau J, Steenhoff D, Coletta A, Molter C, Weiss-Solís DY, Duque R, Bersini H, Nowé A. Batch effect removal methods for microarray gene expression data integration: a survey. Brief Bioinform. 2013;14(4):469–90.PubMedCrossRef Lazar C, Meganck S, Taminau J, Steenhoff D, Coletta A, Molter C, Weiss-Solís DY, Duque R, Bersini H, Nowé A. Batch effect removal methods for microarray gene expression data integration: a survey. Brief Bioinform. 2013;14(4):469–90.PubMedCrossRef
60.
go back to reference Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostat. 2007;8(1):118–27.CrossRef Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostat. 2007;8(1):118–27.CrossRef
61.
go back to reference Orlhac F, Boughdad S, Philippe C, Stalla-Bourdillon H, Nioche C, Champion L, Soussan M, Frouin F, Frouin V, Buvat I. A Postreconstruction Harmonization Method for Multicenter Radiomic Studies in PET. J Nucl Med. 2018;59(8):1321–8.PubMedCrossRef Orlhac F, Boughdad S, Philippe C, Stalla-Bourdillon H, Nioche C, Champion L, Soussan M, Frouin F, Frouin V, Buvat I. A Postreconstruction Harmonization Method for Multicenter Radiomic Studies in PET. J Nucl Med. 2018;59(8):1321–8.PubMedCrossRef
62.
go back to reference Orlhac F, Frouin F, Nioche C, Ayache N, Buvat I. Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics. Radiol. 2019;291(1):53–9.CrossRef Orlhac F, Frouin F, Nioche C, Ayache N, Buvat I. Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics. Radiol. 2019;291(1):53–9.CrossRef
63.
go back to reference Fortin JP, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, Adams P, Cooper C, Fava M, McGrath PJ, et al. Harmonization of cortical thickness measurements across scanners and sites. Neuroimage. 2018;167:104–20.PubMedCrossRef Fortin JP, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, Adams P, Cooper C, Fava M, McGrath PJ, et al. Harmonization of cortical thickness measurements across scanners and sites. Neuroimage. 2018;167:104–20.PubMedCrossRef
64.
go back to reference Ibrahim A, Primakov S, Beuque M, Woodruff HC, Halilaj I, Wu G, Refaee T, Granzier R, Widaatalla Y, Hustinx R, et al. Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework. Methods. 2021;188:20–9.PubMedCrossRef Ibrahim A, Primakov S, Beuque M, Woodruff HC, Halilaj I, Wu G, Refaee T, Granzier R, Widaatalla Y, Hustinx R, et al. Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework. Methods. 2021;188:20–9.PubMedCrossRef
65.
go back to reference Wesdorp NJ, Hellingman T, Jansma EP, van Waesberghe JTM, Boellaard R, Punt CJA, Huiskens J, Kazemier G. Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment. Eur J Nucl Med Mol Imaging. 2021;48(6):1785–94.PubMedCrossRef Wesdorp NJ, Hellingman T, Jansma EP, van Waesberghe JTM, Boellaard R, Punt CJA, Huiskens J, Kazemier G. Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment. Eur J Nucl Med Mol Imaging. 2021;48(6):1785–94.PubMedCrossRef
66.
go back to reference Ligero M, Jordi-Ollero O, Bernatowicz K, Garcia-Ruiz A, Delgado-Muñoz E, Leiva D, Mast R, Suarez C, Sala-Llonch R, Calvo N, et al. Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis. Eur Radiol. 2021;31(3):1460–70.PubMedCrossRef Ligero M, Jordi-Ollero O, Bernatowicz K, Garcia-Ruiz A, Delgado-Muñoz E, Leiva D, Mast R, Suarez C, Sala-Llonch R, Calvo N, et al. Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis. Eur Radiol. 2021;31(3):1460–70.PubMedCrossRef
67.
go back to reference Peerlings J, Woodruff HC, Winfield JM, Ibrahim A, Van Beers BE, Heerschap A, Jackson A, Wildberger JE, Mottaghy FM, DeSouza NM, et al. Stability of radiomics features in apparent diffusion coefficient maps from a multi-centre test-retest trial. Sci Rep. 2019;9(1):4800.PubMedPubMedCentralCrossRef Peerlings J, Woodruff HC, Winfield JM, Ibrahim A, Van Beers BE, Heerschap A, Jackson A, Wildberger JE, Mottaghy FM, DeSouza NM, et al. Stability of radiomics features in apparent diffusion coefficient maps from a multi-centre test-retest trial. Sci Rep. 2019;9(1):4800.PubMedPubMedCentralCrossRef
68.
go back to reference Taghavi M, Trebeschi S, Simões R, Meek DB, Beckers RCJ, Lambregts DMJ, Verhoef C, Houwers JB, van der Heide UA, Beets-Tan RGH, et al. Machine learning-based analysis of CT radiomics model for prediction of colorectal metachronous liver metastases. Abdom Radiol (NY). 2021;46(1):249–56.PubMedCrossRef Taghavi M, Trebeschi S, Simões R, Meek DB, Beckers RCJ, Lambregts DMJ, Verhoef C, Houwers JB, van der Heide UA, Beets-Tan RGH, et al. Machine learning-based analysis of CT radiomics model for prediction of colorectal metachronous liver metastases. Abdom Radiol (NY). 2021;46(1):249–56.PubMedCrossRef
69.
go back to reference Harrell F E . Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis[M]. Springer, 2010. Harrell F E . Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis[M]. Springer, 2010.
70.
go back to reference Yang C, Jiang ZK, Liu LH, Zeng MS. Pre-treatment ADC image-based random forest classifier for identifying resistant rectal adenocarcinoma to neoadjuvant chemoradiotherapy. Int J Colorectal Dis. 2020;35(1):101–7.PubMedCrossRef Yang C, Jiang ZK, Liu LH, Zeng MS. Pre-treatment ADC image-based random forest classifier for identifying resistant rectal adenocarcinoma to neoadjuvant chemoradiotherapy. Int J Colorectal Dis. 2020;35(1):101–7.PubMedCrossRef
71.
go back to reference Sollini M, Antunovic L, Chiti A, Kirienko M. Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics. Eur J Nucl Med Mol Imaging. 2019;46(13):2656–72.PubMedPubMedCentralCrossRef Sollini M, Antunovic L, Chiti A, Kirienko M. Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics. Eur J Nucl Med Mol Imaging. 2019;46(13):2656–72.PubMedPubMedCentralCrossRef
72.
go back to reference Moreira JM, Santiago I, Santinha J, Figueiredo N, Marias K, Figueiredo M, Vanneschi L, Papanikolaou N. Challenges and Promises of Radiomics for Rectal Cancer. Curr Colorectal Cancer Rep. 2019;15(6):175–80.CrossRef Moreira JM, Santiago I, Santinha J, Figueiredo N, Marias K, Figueiredo M, Vanneschi L, Papanikolaou N. Challenges and Promises of Radiomics for Rectal Cancer. Curr Colorectal Cancer Rep. 2019;15(6):175–80.CrossRef
73.
go back to reference Hennessy M, Milner R. Algebraic laws for nondeterminism and concurrency. J ACM. 1985;32(1):137–61.CrossRef Hennessy M, Milner R. Algebraic laws for nondeterminism and concurrency. J ACM. 1985;32(1):137–61.CrossRef
74.
go back to reference Brunese L, Mercaldo F, Reginelli A, Santone A. Prostate Gleason Score Detection and Cancer Treatment Through Real-Time Formal Verification. IEEE Access. 2019;7:186236–46.CrossRef Brunese L, Mercaldo F, Reginelli A, Santone A. Prostate Gleason Score Detection and Cancer Treatment Through Real-Time Formal Verification. IEEE Access. 2019;7:186236–46.CrossRef
75.
go back to reference Rocca A, Brunese MC, Santone A, Avella P, Bianco P, Scacchi A, Scaglione M, Bellifemine F, Danzi R, Varriano G et al: Early Diagnosis of Liver Metastases from Colorectal Cancer through CT Radiomics and Formal Methods: A Pilot Study. J Clin Med. 2021;11(1):31. Rocca A, Brunese MC, Santone A, Avella P, Bianco P, Scacchi A, Scaglione M, Bellifemine F, Danzi R, Varriano G et al: Early Diagnosis of Liver Metastases from Colorectal Cancer through CT Radiomics and Formal Methods: A Pilot Study. J Clin Med. 2021;11(1):31.
76.
go back to reference Santone A, Brunese MC, Donnarumma F, Guerriero P, Mercaldo F, Reginelli A, Miele V, Giovagnoni A, Brunese L. Radiomic features for prostate cancer grade detection through formal verification. Radiol Med. 2021;126(5):688–97.PubMedCrossRef Santone A, Brunese MC, Donnarumma F, Guerriero P, Mercaldo F, Reginelli A, Miele V, Giovagnoni A, Brunese L. Radiomic features for prostate cancer grade detection through formal verification. Radiol Med. 2021;126(5):688–97.PubMedCrossRef
77.
go back to reference Santone A, Belfiore MP, Mercaldo F, Varriano G, Brunese L: On the Adoption of Radiomics and Formal Methods for COVID-19 Coronavirus Diagnosis. Diagnostics (Basel). 2021;11(2):293. Santone A, Belfiore MP, Mercaldo F, Varriano G, Brunese L: On the Adoption of Radiomics and Formal Methods for COVID-19 Coronavirus Diagnosis. Diagnostics (Basel). 2021;11(2):293.
78.
go back to reference Brunese L, Mercaldo F, Reginelli A, Santone A. Formal methods for prostate cancer Gleason score and treatment prediction using radiomic biomarkers. Magn Reson Imaging. 2020;66:165–75.PubMedCrossRef Brunese L, Mercaldo F, Reginelli A, Santone A. Formal methods for prostate cancer Gleason score and treatment prediction using radiomic biomarkers. Magn Reson Imaging. 2020;66:165–75.PubMedCrossRef
79.
go back to reference Brunese L, Mercaldo F, Reginelli A, Santone A: Radiomics for Gleason Score Detection through Deep Learning. Sensors (Basel). 2020;20(18):5411. Brunese L, Mercaldo F, Reginelli A, Santone A: Radiomics for Gleason Score Detection through Deep Learning. Sensors (Basel). 2020;20(18):5411.
80.
go back to reference Wu X, Li Y, Chen X, Huang Y, He L, Zhao K, Huang X, Zhang W, Huang Y, Li Y, et al. Deep Learning Features Improve the Performance of a Radiomics Signature for Predicting KRAS Status in Patients with Colorectal Cancer. Acad Radiol. 2020;27(11):e254–62.PubMedCrossRef Wu X, Li Y, Chen X, Huang Y, He L, Zhao K, Huang X, Zhang W, Huang Y, Li Y, et al. Deep Learning Features Improve the Performance of a Radiomics Signature for Predicting KRAS Status in Patients with Colorectal Cancer. Acad Radiol. 2020;27(11):e254–62.PubMedCrossRef
81.
go back to reference Yang L, Dong D, Fang M, Zhu Y, Zang Y, Liu Z, Zhang H, Ying J, Zhao X, Tian J. Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer? Eur Radiol. 2018;28(5):2058–67.PubMedCrossRef Yang L, Dong D, Fang M, Zhu Y, Zang Y, Liu Z, Zhang H, Ying J, Zhao X, Tian J. Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer? Eur Radiol. 2018;28(5):2058–67.PubMedCrossRef
82.
go back to reference Li Y, Eresen A, Shangguan J, Yang J, Benson AB 3rd, Yaghmai V, Zhang Z. Preoperative prediction of perineural invasion and KRAS mutation in colon cancer using machine learning. J Cancer Res Clin Oncol. 2020;146(12):3165–74.PubMedCrossRef Li Y, Eresen A, Shangguan J, Yang J, Benson AB 3rd, Yaghmai V, Zhang Z. Preoperative prediction of perineural invasion and KRAS mutation in colon cancer using machine learning. J Cancer Res Clin Oncol. 2020;146(12):3165–74.PubMedCrossRef
83.
go back to reference Oh JE, Kim MJ, Lee J, Hur BY, Kim B, Kim DY, Baek JY, Chang HJ, Park SC, Oh JH, et al. Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer. Cancer Res Treat. 2020;52(1):51–9.PubMedCrossRef Oh JE, Kim MJ, Lee J, Hur BY, Kim B, Kim DY, Baek JY, Chang HJ, Park SC, Oh JH, et al. Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer. Cancer Res Treat. 2020;52(1):51–9.PubMedCrossRef
84.
go back to reference Cui Y, Liu H, Ren J, Du X, Xin L, Li D, Yang X, Wang D. Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer. Eur Radiol. 2020;30(4):1948–58.PubMedCrossRef Cui Y, Liu H, Ren J, Du X, Xin L, Li D, Yang X, Wang D. Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer. Eur Radiol. 2020;30(4):1948–58.PubMedCrossRef
85.
go back to reference Horvat N, Veeraraghavan H, Pelossof RA, Fernandes MC, Arora A, Khan M, Marco M, Cheng CT, Gonen M, Golia Pernicka JS, et al. Radiogenomics of rectal adenocarcinoma in the era of precision medicine: A pilot study of associations between qualitative and quantitative MRI imaging features and genetic mutations. Eur J Radiol. 2019;113:174–81.PubMedPubMedCentralCrossRef Horvat N, Veeraraghavan H, Pelossof RA, Fernandes MC, Arora A, Khan M, Marco M, Cheng CT, Gonen M, Golia Pernicka JS, et al. Radiogenomics of rectal adenocarcinoma in the era of precision medicine: A pilot study of associations between qualitative and quantitative MRI imaging features and genetic mutations. Eur J Radiol. 2019;113:174–81.PubMedPubMedCentralCrossRef
86.
go back to reference Shi R, Chen W, Yang B, Qu J, Cheng Y, Zhu Z, Gao Y, Wang Q, Liu Y, Li Z, et al. Prediction of KRAS, NRAS and BRAF status in colorectal cancer patients with liver metastasis using a deep artificial neural network based on radiomics and semantic features. Am J Cancer Res. 2020;10(12):4513–26.PubMedPubMedCentral Shi R, Chen W, Yang B, Qu J, Cheng Y, Zhu Z, Gao Y, Wang Q, Liu Y, Li Z, et al. Prediction of KRAS, NRAS and BRAF status in colorectal cancer patients with liver metastasis using a deep artificial neural network based on radiomics and semantic features. Am J Cancer Res. 2020;10(12):4513–26.PubMedPubMedCentral
87.
go back to reference Neri E, Del Re M, Paiar F, Erba P, Cocuzza P, Regge D, Danesi R. Radiomics and liquid biopsy in oncology: the holons of systems medicine. Insights Imaging. 2018;9(6):915–24.PubMedPubMedCentralCrossRef Neri E, Del Re M, Paiar F, Erba P, Cocuzza P, Regge D, Danesi R. Radiomics and liquid biopsy in oncology: the holons of systems medicine. Insights Imaging. 2018;9(6):915–24.PubMedPubMedCentralCrossRef
Metadata
Title
Repeatability of radiomics studies in colorectal cancer: a systematic review
Authors
Ying Liu
Xiaoqin Wei
Xu Feng
Yan Liu
Guiling Feng
Yong Du
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Gastroenterology / Issue 1/2023
Electronic ISSN: 1471-230X
DOI
https://doi.org/10.1186/s12876-023-02743-1

Other articles of this Issue 1/2023

BMC Gastroenterology 1/2023 Go to the issue
Live Webinar | 27-06-2024 | 18:00 (CEST)

Keynote webinar | Spotlight on medication adherence

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

Prof. Kevin Dolgin
Prof. Florian Limbourg
Prof. Anoop Chauhan
Developed by: Springer Medicine
Obesity Clinical Trial Summary

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine

Highlights from the ACC 2024 Congress

Year in Review: Pediatric cardiology

Watch Dr. Anne Marie Valente present the last year's highlights in pediatric and congenital heart disease in the official ACC.24 Year in Review session.

Year in Review: Pulmonary vascular disease

The last year's highlights in pulmonary vascular disease are presented by Dr. Jane Leopold in this official video from ACC.24.

Year in Review: Valvular heart disease

Watch Prof. William Zoghbi present the last year's highlights in valvular heart disease from the official ACC.24 Year in Review session.

Year in Review: Heart failure and cardiomyopathies

Watch this official video from ACC.24. Dr. Biykem Bozkurt discusses last year's major advances in heart failure and cardiomyopathies.