Ferlay J , Colombet M , Soerjomataram I , Mathers C , Parkin DM , Piñeros M , et al . Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods . Int J Cancer . 2019 ; 144 ( 8 ): 1941 – 53 .

Yin L , Li Z , Xue G , Lu L . Nasopharyngeal carcinoma misdiagnosed as pituitary tumor with multiple cranial neuropathies . J Cancer Res Ther . 2021 ; 17 ( 7 ): 1748 – 50 .

Hou Z , Li S , Ren W , Liu J , Yan J , Wan S . Radiomic analysis in T 2 W and SPAIR T 2 W MRI: predict treatment response to chemoradiotherapy in esophageal squamous cell carcinoma . J Thorac Dis . 2018 ; 10 ( 4 ): 2256 – 67 .

Zhang L , Li L , Feng G , Fan T , Jiang H , Wang Z . Advances in CT techniques in vascular calcification . Front Cardiovasc Med . 2021 ; 8 : 716822 .

Chabanova E , Larsen L , Løgager VB , Møller JM , Thomsen HS . Use of magnetic resonance imaging . Ugeskr Laeger . 2014 ; 176 ( 1 ): 50 – 4 .

Zhu A , Marcus DM , Shu HK , Shim H . Application of metabolic PET imaging in radiation oncology . Radiat Res . 2012 ; 177 ( 4 ): 436 – 48 .

Liu Z , Wang S , Dong D , Wei J , Fang C , Zhou X , et al . The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges . Theranostics . 2019 ; 9 ( 5 ): 1303 – 22 .

Lambin P , Leijenaar RTH , Deist TM , Peerlings J , De Jong EEC , Van Timmeren J , et al . Radiomics: the bridge between medical imaging and personalized medicine . Nat Rev Clin Oncol . 2017 ; 14 ( 12 ): 749 – 62 .

Scapicchio C , Gabelloni M , Barucci A , Cioni D , Saba L , Neri E . A deep look into radiomics . Radiol Med . 2021 ; 126 ( 10 ): 1296 – 311 .

Lim H , Kim DH , Jung HY , Gong EJ , Na HK , Ahn JY , et al . Clinical significance of early detection of esophageal cancer in patients with head and neck cancer . Gut Liver . 2015 ; 9 ( 2 ): 159 – 65 .

Mydlarz WK , Hennessey PT , Califano JA . Advances and perspectives in the molecular diagnosis of head and neck cancer . Expert Opin Med Diagn . 2010 ; 4 ( 1 ): 53 – 65 .

Yip SS , Aerts HJ . Applications and limitations of radiomics . Phys Med Biol . 2016 ; 61 ( 13 ): R150 – 66 .

Wu W , Ye J , Wang Q , Luo J , Xu S . CT-based radiomics signature for the preoperative discrimination between head and neck squamous cell carcinoma grades . Front Oncol . 2019 ; 9 : 821 .

Mukherjee P , Cintra M , Huang C , Zhou M , Zhu S , Colevas AD , et al . CT-based radiomic signatures for predicting histopathologic features in head and neck squamous cell carcinoma . Radiol Imaging Cancer . 2020 ; 2 ( 3 ): e190039 .

Parmar C , Leijenaar RT , Grossmann P , Rios Velazquez E , Bussink J , Rietveld D , et al . Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer . Sci Rep . 2015 ; 5 : 11044 .

Wang F , Zhang B , Wu X , Liu L , Fang J , Chen Q , et al . Radiomic nomogram improves preoperative T category accuracy in locally advanced laryngeal carcinoma . Front Oncol . 2019 ; 9 : 1064 .

Ren J , Tian J , Yuan Y , Dong D , Li X , Shi Y , et al . Magnetic resonance imaging based radiomics signature for the preoperative discrimination of stage Ⅰ-Ⅱ and Ⅲ-Ⅳ head and neck squamous cell carcinoma . Eur J Radiol . 2018 ; 106 : 1 – 6 .

Wu L , Wang C , Tan X , Cheng Z , Zhao K , Yan L , et al . Radiomics approach for preoperative identification of stages Ⅰ-Ⅱ and Ⅲ-Ⅳ of esophageal cancer . Chin J Cancer Res . 2018 ; 30 ( 4 ): 396 – 405 .

Liang C , Huang Y , He L , Chen X , Ma Z , Dong D , et al . The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage Ⅰ-Ⅱ and stage Ⅲ-Ⅳ colorectal cancer . Oncotarget . 2016 ; 7 ( 21 ): 31401 – 12 .

Gao RZ , Wen R , Wen DY , Huang J , Qin H , Li X , et al . Radiomics analysis based on ultrasound images to distinguish the tumor stage and pathological grade of bladder cancer . J Ultrasound Med . 2021 ; 40 ( 12 ): 2685 – 97 .

Ho TY , Chao CH , Chin SC , Ng SH , Kang CJ , Tsang NM . Classifying neck lymph nodes of head and neck squamous cell carcinoma in MRI images with radiomic features . J Digit Imaging . 2020 ; 33 ( 3 ): 613 – 8 .

Yin J , Qiu JJ , Qian W , Ji L , Yang D , Jiang JW , et al . A radiomics signature to identify malignant and benign liver tumors on plain CT images . J Xray Sci Technol . 2020 ; 28 ( 4 ): 683 – 94 .

Piludu F , Marzi S , Ravanelli M , Pellini R , Covello R , Terrenato I , et al . MRI-based radiomics to differentiate between benign and malignant parotid tumors with external validation . Front Oncol . 2021 ; 11 : 656918 .

Zhang S , Yu M , Chen D , Li P , Tang B , Li J . Role of MRI-based radiomics in locally advanced rectal cancer (review) . Oncol Rep . 2022 ; 47 ( 2 ): 34 .

Nowakowski A , Lahijanian Z , Panet-Raymond V , Siegel PM , Petrecca K , Maleki F , et al . Radiomics as an emerging tool in the management of brain metastases . Neurooncol Adv . 2022 ; 4 ( 1 ): vdac141 .

Shalata AT , Shehata M , Van Bogaert E , Ali KM , Alksas A , Mahmoud A , et al . Predicting recurrence of non-muscle-invasive bladder cancer: current techniques and future trends . Cancers (Basel) . 2022 ; 14 ( 20 ): 5019 .

Li M , Zhu YZ , Zhang YC , Yue YF , Yu HP , Song B . Radiomics of rectal cancer for predicting distant metastasis and overall survival . World J Gastroenterol . 2020 ; 26 ( 33 ): 5008 – 21 .

Fh T , Cyw C , Eyw C . Radiomics AI prediction for head and neck squamous cell carcinoma (HNSCC) prognosis and recurrence with target volume approach . BJR Open . 2021 ; 3 ( 1 ): 20200073 .

Folkert MR , Setton J , Apte AP , Grkovski M , Young RJ , Schöder H , et al . Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics . Phys Med Biol . 2017 ; 62 ( 13 ): 5327 – 43 .

Haider SP , Zeevi T , Baumeister P , Reichel C , Sharaf K , Forghani R , et al . Potential added value of PET/CT radiomics for survival prognostication beyond AJCC 8th edition staging in oropharyngeal squamous cell carcinoma . Cancers (Basel) . 2020 ; 12 ( 7 ): 1778 .

Woolen S , Virkud A , Hadjiiski L , Cha K , Chan HP , Swiecicki P , et al . Prediction of disease free survival in laryngeal and hypopharyngeal cancers using CT perfusion and radiomic features: a pilot study . Tomography . 2021 ; 7 ( 1 ): 10 – 9 .

Bologna M , Corino V , Calareso G , Tenconi C , Alfieri S , Iacovelli NA , et al . Baseline MRI-radiomics can predict overall survival in non-endemic EBV-related nasopharyngeal carcinoma patients . Cancers (Basel) . 2020 ; 12 ( 10 ): 2958 .

Liu K , Qiu Q , Qin Y , Chen T , Zhang D , Huang L , et al . Radiomics nomogram based on multiple-sequence magnetic resonance imaging predicts long-term survival in patients diagnosed with nasopharyngeal carcinoma . Front Oncol . 2022 ; 12 : 852348 .

Wang F , Tan R , Feng K , Hu J , Zhuang Z , Wang C , et al . Magnetic resonance imaging-based radiomics features associated with depth of invasion predicted lymph node metastasis and prognosis in tongue cancer . J Magn Reson Imaging . 2022 ; 56 ( 1 ): 196 – 209 .

Li H , Chen XL , Liu H , Lu T , Li ZL . MRI-based multiregional radiomics for predicting lymph nodes status and prognosis in patients with resectable rectal cancer . Front Oncol . 2022 ; 12 : 1087882 .

M. D. Anderson Cancer Center Head and Neck Quantitative Imaging Working Group . Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients . Sci Rep . 2018 ; 8 ( 1 ): 1524 .

Kim M , Lee JH , Joo L , Jeong B , Kim S , Ham S , et al . Development and validation of a model using radiomics features from an apparent diffusion coefficient map to diagnose local tumor recurrence in patients treated for head and neck squamous cell carcinoma . Korean J Radiol . 2022 ; 23 ( 11 ): 1078 – 88 .

Dasgupta A , Bhardwaj D , Dicenzo D , Fatima K , Osapoetra LO , Quiaoit K , et al . Radiomics in predicting recurrence for patients with locally advanced breast cancer using quantitative ultrasound . Oncotarget . 2021 ; 12 ( 25 ): 2437 – 48 .

Zhang L , Dong D , Li H , Tian J , Ouyang F , Mo X , et al . Development and validation of a magnetic resonance imaging-based model for the prediction of distant metastasis before initial treatment of nasopharyngeal carcinoma: a retrospective cohort study . EBioMedicine . 2019 ; 40 : 327 – 35 .

Sharma D , Koshy G , Grover S , Sharma B . Sentinel lymph node biopsy: a new approach in the management of head and neck cancers . Sultan Qaboos Univ Med J . 2017 ; 17 ( 1 ): e3 – 10 .

Li F , Pan D , He Y , Wu Y , Peng J , Li J , et al . Using ultrasound features and radiomics analysis to predict lymph node metastasis in patients with thyroid cancer . BMC Surg . 2020 ; 20 ( 1 ): 315 .

Lin M , Tang X , Cao L , Liao Y , Zhang Y , Zhou J . Using ultrasound radiomics analysis to diagnose cervical lymph node metastasis in patients with nasopharyngeal carcinoma . Eur Radiol . 2023 ; 33 ( 2 ): 774 – 83 .

Cysouw MCF , Jansen BHE , Van De Brug T , Oprea-Lager DE , Pfaehler E , De Vries BM , et al . Machine learning-based analysis of [ 18 F] DCFPyL PET radiomics for risk stratification in primary prostate cancer . Eur J Nucl Med Mol Imaging . 2021 ; 48 ( 2 ): 340 – 9 .

Tran WT , Suraweera H , Quiaoit K , Dicenzo D , Fatima K , Jang D , et al . Quantitative ultrasound delta-radiomics during radiotherapy for monitoring treatment responses in head and neck malignancies . Future Sci OA . 2020 ; 6 ( 9 ): FSO624 .

Kaźmierska J , Kaźmierski MR , Bajon T , Winiecki T , Bandurska-Luque A , Ryczkowski A , et al . Prediction of incomplete response of primary tumour based on clinical and radiomics features in inoperable head and neck cancers after definitive treatment . J Pers Med . 2022 ; 12 ( 7 ): 1092 .

Zhang MH , Cao D , Ginat DT . Radiomic model predicts lymph node response to induction chemotherapy in locally advanced head and neck cancer . Diagnostics (Basel) . 2021 ; 11 ( 4 ): 588 .

Xi Y , Ge X , Ji H , Wang L , Duan S , Chen H , et al . Prediction of response to induction chemotherapy plus concurrent chemoradiotherapy for nasopharyngeal carcinoma based on MRI radiomics and delta radiomics: a two-center retrospective study . Front Oncol . 2022 ; 12 : 824509 .

Chen BY , Xie H , Li Y , Jiang XH , Xiong L , Tang XF , et al . MRI-based radiomics features to predict treatment response to neoadjuvant chemotherapy in locally advanced rectal cancer: a single center, prospective study . Front Oncol . 2022 ; 12 : 801743 .

Guo L , Du S , Gao S , Zhao R , Huang G , Jin F , et al . Delta-radiomics based on dynamic contrast-enhanced MRI predicts pathologic complete response in breast cancer patients treated with neoadjuvant chemotherapy . Cancers (Basel) . 2022 ; 14 ( 14 ): 3515 .

Xiang Y , Li S , Wang H , Song M , Hu K , Wang F , et al . MRI-based radiomics to predict neoadjuvant chemoradiotherapy outcomes in locally advanced rectal cancer: a multicenter study . Clin Transl Radiat Oncol . 2023 ; 38 : 175 – 82 .

King AD , Griffith JF , Abrigo JM , Leung SF , Yau FK , Tse GM , et al . Osteoradionecrosis of the upper cervical spine: MR imaging following radiotherapy for nasopharyngeal carcinoma . Eur J Radiol . 2010 ; 73 ( 3 ): 629 – 35 .

Zhong X , Li L , Jiang H , Yin J , Lu B , Han W , et al . Cervical spine osteoradionecrosis or bone metastasis after radiotherapy for nasopharyngeal carcinoma? The MRI-based radiomics for characterization . BMC Med Imaging . 2020 ; 20 ( 1 ): 104 .

Van Dijk LV , Thor M , Steenbakkers R , Apte A , Zhai TT , Borra R , et al . Parotid gland fat related magnetic resonance image biomarkers improve prediction of late radiation-induced xerostomia . Radiother Oncol . 2018 ; 128 ( 3 ): 459 – 66 .

Dercle L , Zhao B , Gönen M , Moskowitz CS , Connors DE , Yang H , et al . An imaging signature to predict outcome in metastatic colorectal cancer using routine computed tomography scans . Eur J Cancer . 2022 ; 161 : 138 – 47 .

Jiang Y , Wang H , Wu J , Chen C , Yuan Q , Huang W , et al . Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer . Ann Oncol . 2020 ; 31 ( 6 ): 760 – 8 .

Gillies RJ , Kinahan PE , Hricak H . Radiomics: images are more than pictures, they are data . Radiology . 2016 ; 278 ( 2 ): 563 – 77 .

Haralick RM , Shanmugam K , Dinstein I . Textural features for image classification . TSMC . 1973 ; SMC-3 ( 6 ): 610 – 21 .

Galloway MM . Texture analysis using gray level run lengths . Comput Graph Image Proc . 1975 ; 4 ( 2 ): 172 – 9 .

Sun C , Wee WG . Neighboring gray level dependence matrix for texture classification . Comput Vis Graph Image Proc . 1983 ; 23 ( 3 ): 341 – 52 .

Shen C , Liu Z , Guan M , Song J , Lian Y , Wang S , et al . 2D and 3D CT radiomics features prognostic performance comparison in non-small cell lung cancer . Transl Oncol . 2017 ; 10 ( 6 ): 886 – 94 .

Wan Q , Zhou J , Xia X , Hu J , Wang P , Peng Y , et al . Diagnostic performance of 2D and 3D T2WI-based radiomics features with machine learning algorithms to distinguish solid solitary pulmonary lesion . Front Oncol . 2021 ; 11 : 683587 .

Xu L , Yang P , Yen EA , Wan Y , Jiang Y , Cao Z , et al . A multi-organ cancer study of the classification performance using 2D and 3D image features in radiomics analysis . Phys Med Biol . 2019 ; 64 ( 21 ): 215009 .

Houseni M , Mahmoud MA , Saad S , Elhussiny F , Shihab M . Advanced intra-tumoural structural characterisation of hepatocellular carcinoma utilising FDG-PET/CT: a comparative study of radiomics and metabolic features in 3D and 2D . Pol J Radiol . 2021 ; 86 : e64 – 73 .

Zhu Y , Yao W , Xu BC , Lei YY , Guo QK , Liu LZ , et al . Predicting response to immunotherapy plus chemotherapy in patients with esophageal squamous cell carcinoma using non-invasive radiomic biomarkers . BMC Cancer . 2021 ; 21 ( 1 ): 1167 .

Beichel RR , Ulrich EJ , Smith BJ , Bauer C , Brown B , Casavant T , et al . FDG PET based prediction of response in head and neck cancer treatment: assessment of new quantitative imaging features . PLoS ONE . 2019 ; 14 ( 4 ): e0215465 .

Buizza G , Toma-Dasu I , Lazzeroni M , Paganelli C , Riboldi M , Chang Y , et al . Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans . Phys Med . 2018 ; 54 : 21 – 9 .

Van Griethuysen JJM , Fedorov A , Parmar C , Hosny A , Aucoin N , Narayan V , et al . Computational radiomics system to decode the radiographic phenotype . Cancer Res . 2017 ; 77 ( 21 ): e104 – 7 .

Fedorov A , Beichel R , Kalpathy-Cramer J , Finet J , Fillion-Robin JC , Pujol S , et al . 3D Slicer as an image computing platform for the quantitative imaging network . Magn Reson Imaging . 2012 ; 30 ( 9 ): 1323 – 41 .

Zhang L , Fried DV , Fave XJ , Hunter LA , Yang J , Court LE . IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics . Med Phys . 2015 ; 42 ( 3 ): 1341 – 53 .

Zwanenburg A , Vallières M , Abdalah MA , Aerts H , Andrearczyk V , Apte A , et al . The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping . Radiology . 2020 ; 295 ( 2 ): 328 – 38 .

Avanzo M , Wei L , Stancanello J , Vallières M , Rao A , Morin O , et al . Machine and deep learning methods for radiomics . Med Phys . 2020 ; 47 ( 5 ): e185 – 202 .

Xie C , Yang P , Zhang X , Xu L , Wang X , Li X , et al . Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy . EBioMedicine . 2019 ; 44 : 289 – 97 .

Peng H , Long F , Ding C . Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy . IEEE Trans Pattern Anal Mach Intell . 2005 ; 27 ( 8 ): 1226 – 38 .

Hu C , Zheng D , Cao X , Pang P , Fang Y , Lu T , et al . Application value of magnetic resonance radiomics and clinical nomograms in evaluating the sensitivity of neoadjuvant chemotherapy for nasopharyngeal carcinoma . Front Oncol . 2021 ; 11 : 740776 .

Kira K , Rendell LA . A practical approach to feature selection . In: Sleeman D , Edwards P , editors . Proceedings of the ninth international workshop on machine learning . Aberdeen : Morgan Kaufmann Publishers Inc ; 1992 . p. 249 – 56 .

Student . The probable error of a mean . Biometrika . 1908 ; 6 : 1 – 25 .

Jin X , Xu A , Bie R , Guo P . Machine learning techniques and Chi-square feature selection for cancer classification using SAGE gene expression profiles . In: Li J , Yang Q , Tan AH , editors . Data mining for biomedical applications . Berlin : Springer ; 2006 . p. 106 – 15 .

Parmar C , Grossmann P , Bussink J , Lambin P , Aerts H . Machine learning methods for quantitative radiomic biomarkers . Sci Rep . 2015 ; 5 : 13087 .

Koul N , Manvi SS . Ensemble feature selection from cancer gene expression data using mutual information and recursive feature elimination . In: 2020 third international conference on advances in electronics, computers and communications (ICAECC). Bengaluru: IEEE ; 2020 . p. 1 – 6 .

Yu Q , Wang A , Gu J , Li Q , Ning Y , Peng J , et al . Multiphasic CT-based radiomics analysis for the differentiation of benign and malignant parotid tumors . Front Oncol . 2022 ; 12 : 913898 .

Tibshirani R . Regression shrinkage and selection via the LASSO: a retrospective . J R Stat Soc B . 2011 ; 73 ( 3 ): 273 – 82 .

Duan W , Xiong B , Tian T , Zou X , He Z , Zhang L . Radiomics in nasopharyngeal carcinoma . Clin Med Insights Oncol . 2022 ; 16 : 11795549221079186 .

Li Y , Yu M , Wang G , Yang L , Ma C , Wang M , et al . Contrast-enhanced CT-based radiomics analysis in predicting lymphovascular invasion in esophageal squamous cell carcinoma . Front Oncol . 2021 ; 11 : 644165 .

Song F , Guo Z , Mei D . Feature selection using principal component analysis . In: 2010 International conference on system science, engineering design and manufacturing informatization. Yichang: IEEE ; 2010 . p. 27 – 30 .

Van Der Maaten L , Hinton G . Viualizing data using t-SNE . J Mach Learn Res . 2008 ; 9 : 2579 – 605 .

Teng X , Zhang J , Ma Z , Zhang Y , Lam S , Li W , et al . Improving radiomic model reliability using robust features from perturbations for head-andneck carcinoma . Front Oncol . 2022 ; 12 : 974467 .

Teng X , Zhang J , Zwanenburg A , Sun J , Huang Y , Lam S , et al . Building reliable radiomic models using image perturbation . Sci Rep . 2022 ; 12 ( 1 ): 10035 .

Jensen LJ , Kim D , Elgeti T , Steffen IG , Hamm B , Nagel SN . Stability of radiomic features across different region of interest sizes-a CT and MR phantom study . Tomography . 2021 ; 7 ( 2 ): 238 – 52 .

Flouris K , Jimenez-Del-Toro O , Aberle C , Bach M , Schaer R , Obmann MM , et al . Assessing radiomics feature stability with simulated CT acquisitions . Sci Rep . 2022 ; 12 ( 1 ): 4732 .

Balagurunathan Y , Kumar V , Gu Y , Kim J , Wang H , Liu Y , et al . Test-retest reproducibility analysis of lung CT image features . J Digit Imaging . 2014 ; 27 ( 6 ): 805 – 23 .

Midya A , Chakraborty J , Gönen M , Do RKG , Simpson AL . Influence of CT acquisition and reconstruction parameters on radiomic feature reproducibility . J Med Imaging (Bellingham) . 2018 ; 5 ( 1 ): 011020 .

Tu SJ , Chen WY , Wu CT . Uncertainty measurement of radiomics features against inherent quantum noise in computed tomography imaging . Eur Radiol . 2021 ; 31 ( 10 ): 7865 – 75 .

Michallek F , Genske U , Niehues SM , Hamm B , Jahnke P . Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging: a phantom study . Eur Radiol . 2022 ; 32 ( 7 ): 4587 – 95 .

Yan J , Chu-Shern JL , Loi HY , Khor LK , Sinha AK , Quek ST , et al . Impact of image reconstruction settings on texture features in 18 F-FDG PET . J Nucl Med . 2015 ; 56 ( 11 ): 1667 – 73 .

Galavis PE , Hollensen C , Jallow N , Paliwal B , Jeraj R . Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters . Acta Oncol . 2010 ; 49 ( 7 ): 1012 – 6 .

Prayer F , Hofmanninger J , Weber M , Kifjak D , Willenpart A , Pan J , et al . Variability of computed tomography radiomics features of fibrosing interstitial lung disease: a test-retest study . Methods . 2021 ; 188 : 98 – 104 .

Zhao B , Tan Y , Tsai WY , Qi J , Xie C , Lu L , et al . Reproducibility of radiomics for deciphering tumor phenotype with imaging . Sci Rep . 2016 ; 6 : 23428 .

Haarburger C , Müller-Franzes G , Weninger L , Kuhl C , Truhn D , Merhof D . Radiomics feature reproducibility under inter-rater variability in segmentations of CT images . Sci Rep . 2020 ; 10 ( 1 ): 12688 .

Lu L , Sun SH , Afran A , Yang H , Lu ZF , So J , et al . Identifying robust radiomics features for lung cancer by using in-vivo and phantom lung lesions . Tomography . 2021 ; 7 ( 1 ): 55 – 64 .

Meyer M , Ronald J , Vernuccio F , Nelson RC , Ramirez-Giraldo JC , Solomon J , et al . Reproducibility of CT radiomic features within the same patient: influence of radiation dose and CT reconstruction settings . Radiology . 2019 ; 293 ( 3 ): 583 – 91 .

Fiset S , Welch ML , Weiss J , Pintilie M , Conway JL , Milosevic M , et al . Repeatability and reproducibility of MRI-based radiomic features in cervical cancer . Radiother Oncol . 2019 ; 135 : 107 – 14 .

Rai R , Holloway LC , Brink C , Field M , Christiansen RL , Sun Y , et al . Multicenter evaluation of MRI-based radiomic features: a phantom study . Med Phys . 2020 ; 47 ( 7 ): 3054 – 63 .

Shafiq-Ul-Hassan M , Zhang GG , Latifi K , Ullah G , Hunt DC , Balagurunathan Y , et al . Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels . Med Phys . 2017 ; 44 ( 3 ): 1050 – 62 .

Linsalata S , Borgheresi R , Marfisi D , Barca P , Sainato A , Paiar F , et al . Radiomics of patients with locally advanced rectal cancer: effect of preprocessing on features estimation from computed tomography imaging . Biomed Res Int . 2022 ; 2022 : 2003286 .

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 .

Jensen LJ , Kim D , Elgeti T , Steffen IG , Schaafs LA , Hamm B , et al . Enhancing the stability of CT radiomics across different volume of interest sizes using parametric feature maps: a phantom study . Eur Radiol Exp . 2022 ; 6 ( 1 ): 43 .

Bologna M , Corino V , Mainardi L . Technical Note: virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI-radiomics of the brain . Med Phys . 2019 ; 46 ( 11 ): 5116 – 23 .

Li Y , Ammari S , Balleyguier C , Lassau N , Chouzenoux E . Impact of preprocessing and harmonization methods on the removal of scanner effects in brain MRI radiomic features . Cancers (Basel) . 2021 ; 13 ( 12 ): 3000 .

Mali SA , Ibrahim A , Woodruff HC , Andrearczyk V , Müller H , Primakov S , et al . Making radiomics more reproducible across scanner and imaging protocol variations: a review of harmonization methods . J Pers Med . 2021 ; 11 ( 9 ): 842 .

Orlhac F , Lecler A , Savatovski J , Goya-Outi J , Nioche C , Charbonneau F , et al . How can we combat multicenter variability in MR radiomics? Validation of a correction procedure . Eur Radiol . 2021 ; 31 ( 4 ): 2272 – 80 .

Orlhac F , Eertink JJ , Cottereau AS , Zijlstra JM , Thieblemont C , Meignan M , et al . A guide to ComBat harmonization of imaging biomarkers in multicenter studies . J Nucl Med . 2022 ; 63 ( 2 ): 172 – 9 .

Orlhac F , Boughdad S , Philippe C , Stalla-Bourdillon H , Nioche C , Champion L , et al . A postreconstruction harmonization method for multicenter radiomic studies in PET . J Nucl Med . 2018 ; 59 ( 8 ): 1321 – 8 .

Wang L , Tan J , Ge Y , Tao X , Cui Z , Fei Z , et al . Assessment of liver metastases radiomic feature reproducibility with deep-learning-based semiautomatic segmentation software . Acta Radiol . 2021 ; 62 ( 3 ): 291 – 301 .

Parmar C , Rios Velazquez E , Leijenaar R , Jermoumi M , Carvalho S , Mak RH , et al . Robust radiomics feature quantification using semiautomatic volumetric segmentation . PLoS One . 2014 ; 9 ( 7 ): e102107 .

Pavic M , Bogowicz M , Würms X , Glatz S , Finazzi T , Riesterer O , et al . Influence of inter-observer delineation variability on radiomics stability in different tumor sites . Acta Oncol . 2018 ; 57 ( 8 ): 1070 – 4 .

Gitto S , Bologna M , Corino VDA , Emili I , Albano D , Messina C , et al . Diffusion-weighted MRI radiomics of spine bone tumors: feature stability and machine learning-based classification performance . Radiol Med . 2022 ; 127 ( 5 ): 518 – 25 .

Denzler S , Vuong D , Bogowicz M , Pavic M , Frauenfelder T , Thierstein S , et al . Impact of CT convolution kernel on robustness of radiomic features for different lung diseases and tissue types . Br J Radiol . 2021 ; 94 ( 1120 ): 20200947 .

Bologna M , Corino VDA , Montin E , Messina A , Calareso G , Greco FG , et al . Assessment of stability and discrimination capacity of radiomic features on apparent diffusion coefficient images . J Digit Imaging . 2018 ; 31 ( 6 ): 879 – 94 .

Yan KK , Wang X , Lam WWT , Vardhanabhuti V , Lee AWM , Pang HH . Radiomics analysis using stability selection supervised component analysis for right-censored survival data . Comput Biol Med . 2020 ; 124 : 103959 .

Lam SK , Zhang Y , Zhang J , Li B , Sun JC , Liu CY , et al . Multi-organ omicsbased prediction for adaptive radiation therapy eligibility in nasopharyngeal carcinoma patients undergoing concurrent chemoradiotherapy . Front Oncol . 2021 ; 11 : 792024 .

Leijenaar RT , Carvalho S , Velazquez ER , Van Elmpt WJ , Parmar C , Hoekstra OS , et al . Stability of FDG-PET radiomics features: an integrated analysis of test–retest and inter-observer variability . Acta Oncol . 2013 ; 52 ( 7 ): 1391 – 7 .

Hu Y , Xie C , Yang H , Ho JWK , Wen J , Han L , et al . Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma . Radiother Oncol . 2021 ; 154 : 6 – 13 .

Wang J , Zeng J , Li H , Yu X . A deep learning radiomics analysis for survival prediction in esophageal cancer . J Healthc Eng . 2022 ; 2022 : 4034404 .

Paul R , Schabath M , Balagurunathan Y , Liu Y , Li Q , Gillies R , et al . Explaining deep features using radiologist-defined semantic features and traditional quantitative features . Tomography . 2019 ; 5 ( 1 ): 192 – 200 .

Aerts HJ , Velazquez ER , Leijenaar RT , Parmar C , Grossmann P , Carvalho S , et al . Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach . Nat Commun . 2014 ; 5 : 4006 .

Rifi AL , Dufait I , Aisati CE , Ridder MD , Barbé K . Unraveling the biological meaning of radiomic features . In: 2022 IEEE international symposium on medical measurements and applications (MeMeA). Messina: IEEE ; 2022 . p. 1 – 6 .

Just N . Improving tumour heterogeneity MRI assessment with histograms . Br J Cancer . 2014 ; 111 ( 12 ): 2205 – 13 .

Eloyan A , Yue MS , Khachatryan D . Tumor heterogeneity estimation for radiomics in cancer . Stat Med . 2020 ; 39 ( 30 ): 4704 – 23 .

Wang X , Xie T , Luo J , Zhou Z , Yu X , Guo X . Radiomics predicts the prognosis of patients with locally advanced breast cancer by reflecting the heterogeneity of tumor cells and the tumor microenvironment . Breast Cancer Res . 2022 ; 24 ( 1 ): 20 .

Ferreira Junior JR , Koenigkam-Santos M , Machado CVB , Faleiros MC , Correia NSC , Cipriano FEG , et al . Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity . Radiol Bras . 2021 ; 54 ( 2 ): 87 – 93 .

Lambin P , Rios-Velazquez E , Leijenaar R , Carvalho S , Van Stiphout RG , Granton P , et al . Radiomics: extracting more information from medical images using advanced feature analysis . Eur J Cancer . 2012 ; 48 ( 4 ): 441 – 6 .

Vuong D , Tanadini-Lang S , Wu Z , Marks R , Unkelbach J , Hillinger S , et al . Radiomics feature activation maps as a new tool for signature interpretability . Front Oncol . 2020 ; 10 : 578895 .

Kuthuru S , Deaderick W , Bai H , Su C , Vu T , Monga V , et al . A visually interpretable, dictionary-based approach to imaging-genomic modeling, with low-grade glioma as a case study . Cancer Inform . 2018 ; 17 : 1176935118802796 .

Luo Y , Mcshan D , Ray D , Matuszak M , Jolly S , Lawrence T , et al . Development of a fully cross-validated Bayesian network approach for local control prediction in lung cancer . IEEE Trans Radiat Plasma Med Sci . 2019 ; 3 ( 2 ): 232 – 41 .

Choi W , Nadeem S , Alam SR , Deasy JO , Tannenbaum A , Lu W . Reproducible and interpretable spiculation quantification for lung cancer screening . Comput Methods Programs Biomed . 2021 ; 200 : 105839 .

Choi W , Dahiya N , Nadeem S . CIRDataset: a large-scale dataset for clinically-interpretable lung nodule radiomics and malignancy prediction . Med Image Comput Comput Assist Interv . 2022 ; 2022 : 13 – 22 .

Chen J , Jiao L . Classification mechanism of support vector machines. WCC 2000—ICSP 2000 . In: 2000 5th international conference on signal processing proceedings. 16th world computer congress 2000; 2000 21–25 Aug; Beijing: IEEE ; 2000 . p. 1556 – 9 .

Cramer JS . The origins of logistic regression . SSRN Electron J . 2002 . https://doi.org/10.2139/ssrn.360300 https://doi.org/10.2139/ssrn.360300 .

Saini I , Singh D , Khosla A . QRS detection using k-nearest neighbor algorithm (KNN) and evaluation on standard ECG databases . J Adv Res . 2013 ; 4 ( 4 ): 331 – 44 .

Quinlan JR . Induction of decision trees . Mach Learn . 1986 ; 1 ( 1 ): 81 – 106 .

Breiman L . Random forests . Mach Learn . 2001 ; 45 ( 1 ): 5 – 32 .

Chen T , Guestrin C . XGBoost: a scalable tree boosting system . In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. San Francisco: Association for Computing Machinery ; 2016 . p. 785 – 94 .

Castillo TJ , Starmans MPA , Arif M , Niessen WJ , Klein S , Bangma CH , et al . A multi-center, multi-vendor study to evaluate the generalizability of a radiomics model for classifying prostate cancer: high grade vs . low grade . Diagnostics (Basel) . 2021 ; 11 ( 2 ): 369 .

Bogowicz M , Jochems A , Deist TM , Tanadini-Lang S , Huang SH , Chan B , et al . Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer . Sci Rep . 2020 ; 10 ( 1 ): 4542 .

Wang Y , Yao Q , Kwok J , Ni L . Generalizing from a few examples: a survey on few-shot learning . ACM Comput Surv . 2020 ; 53 : 1 – 34 .

Nalepa J , Marcinkiewicz M , Kawulok M . Data augmentation for braintumor segmentation: a review . Front Comput Neurosci . 2019 ; 13 : 83 .

Tanaka S , Kadoya N , Sugai Y , Umeda M , Ishizawa M , Katsuta Y , et al . A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy . Sci Rep . 2022 ; 12 ( 1 ): 8899 .

Guan H , Liu M . Domain adaptation for medical image analysis: a survey . IEEE Trans Biomed Eng . 2022 ; 69 ( 3 ): 1173 – 85 .

Wang S , Dong D , Li H , Feng C , Wang Y , Tian J . Cross-phase adversarial domain adaptation for deep disease-free survival prediction with gastric cancer CT images . Annu Int Conf IEEE Eng Med Biol Soc . 2021 ; 2021 : 3501 – 4 .

Zhang Y , Lam S , Yu T , Teng X , Zhang J , Lee FKH , et al . Integration of an imbalance framework with novel high-generalizable classifiers for radiomics-based distant metastases prediction of advanced nasopharyngeal carcinoma . Knowl-Based Syst . 2022 ; 235 : 107649 .

Shayesteh SP , Shiri I , Karami AH , Hashemian R , Kooranifar S , Ghaznavi H , et al . Predicting lung cancer patients’ survival time via logistic regression-based models in a quantitative radiomic framework . J Biomed Phys Eng . 2020 ; 10 ( 4 ): 479 – 92 .

Keller H , Shek T , Driscoll B , Xu Y , Nghiem B , Nehmeh S , et al . Noisebased image harmonization significantly increases repeatability and reproducibility of radiomics features in PET images: a phantom study . Tomography . 2022 ; 8 ( 2 ): 1113 – 28 .

Yoon JH , Sun SH , Xiao M , Yang H , Lu L , Li Y , et al . Convolutional neural network addresses the confounding impact of CT reconstruction kernels on radiomics studies . Tomography . 2021 ; 7 ( 4 ): 877 – 92 .

Ibrahim A , Refaee T , Leijenaar RTH , Primakov S , Hustinx R , Mottaghy FM , et al . The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset . PLoS One . 2021 ; 16 ( 5 ): e0251147 .

Robinson K , Li H , Lan L , Schacht D , Giger M . Radiomics robustness assessment and classification evaluation: a two-stage method demonstrated on multivendor FFDM . Med Phys . 2019 ; 46 ( 5 ): 2145 – 56 .

Santinha J , Matos C , Figueiredo M , Papanikolaou N . Improving performance and generalizability in radiogenomics: a pilot study for prediction of IDH1/2 mutation status in gliomas with multicentric data . J Med Imaging (Bellingham) . 2021 ; 8 ( 3 ): 031905 .

Ramyachitra D , Manikandan P . Imbalanced dataset classification and solutions: a review . IJCBR . 2014 ; 5 ( 4 ): 1 – 29 .

Haibo H , Yang B , Garcia EA , Shutao L . ADASYN: adaptive synthetic sampling approach for imbalanced learning . In: 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence); 2008 1–8 Jun. Hong Kong: IEEE ; 2008 . p. 1322 – 8 .

Sutton EJ , Onishi N , Fehr DA , Dashevsky BZ , Sadinski M , Pinker K , et al . A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy . Breast Cancer Res . 2020 ; 22 ( 1 ): 57 .

Zhang H , Nguyen TD , Zhang J , Marcille M , Spincemaille P , Wang Y , et al . QSMRim-Net: imbalance-aware learning for identification of chronic active multiple sclerosis lesions on quantitative susceptibility maps . Neuroimage Clin . 2022 ; 34 : 102979 .

Li K , Zhou G , Zhai J , Li F , Shao M . Improved PSO_AdaBoost ensemble algorithm for imbalanced data . Sensors (Basel) . 2019 ; 19 ( 6 ): 1476 .

Khalilia M , Chakraborty S , Popescu M . Predicting disease risks from highly imbalanced data using random forest . BMC Med Inform Decis Mak . 2011 ; 11 : 51 .

Tang FH , Cheung EY , Wong HL , Yuen CM , Yu MH , Ho PC . Radiomics from various tumour volume sizes for prognosis prediction of head and neck squamous cell carcinoma: a voted ensemble machine learning approach . Life (Basel) . 2022 ; 12 ( 9 ): 1380 .

Vamvakas A , Tsivaka D , Logothetis A , Vassiou K , Tsougos I . Breast cancer classification on multiparametric MRI—increased performance of boosting ensemble methods . Technol Cancer Res Treat . 2022 ; 21 : 15330338221087828 .

Pes B , Lai G . Cost-sensitive learning strategies for high-dimensional and imbalanced data: a comparative study . PeerJ Comput Sci . 2021 ; 7 : e832 .

Sun Y , Li M , Li L , Shao H , Sun Y . Cost-sensitive classification for evolving data streams with concept drift and class imbalance . Comput Intell Neurosci . 2021 ; 2021 : 8813806 .

Welch ML , Mcintosh C , Mcniven A , Huang SH , Zhang BB , Wee L , et al . User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions . Phys Med . 2020 ; 70 : 145 – 52 .

Jiao J , Du Y , Li X , Guo Y , Ren Y , Wang Y . Prenatal prediction of neonatal respiratory morbidity: a radiomics method based on imbalanced few-shot fetal lung ultrasound images . BMC Med Imaging . 2022 ; 22 ( 1 ): 2 .

Dolmans TC , Poel M , Van’T Klooster JJR , Veldkamp BP . Perceived mental workload classification using intermediate fusion multimodal deep learning . Front Hum Neurosci . 2020 ; 14 : 609096 .

Ramachandram D , Taylor GW . Deep multimodal learning: a survey on recent advances and trends . IEEE Signal Proc Mag . 2017 ; 34 ( 6 ): 96 – 108 .

Zhang Y , Xia K , Jiang Y , Qian P , Cai W , Qiu C , et al . Multi-modality fusion & inductive knowledge transfer underlying non-sparse multi-kernel learning and distribution adaption . IEEE/ACM Trans Comput Biol Bioinform . 2022 . https://doi.org/10.1109/tcbb.2022.3142748 https://doi.org/10.1109/tcbb.2022.3142748 .

Li W , Shen H , Han L , Liu J , Xiao B , Li X , et al . A multiparametric fusion radiomics signature based on contrast-enhanced MRI for predicting early recurrence of hepatocellular carcinoma . J Oncol . 2022 ; 2022 : 3704987 .

Keek SA , Wesseling FWR , Woodruff HC , Van Timmeren JE , Nauta IH , Hoffmann TK , et al . A prospectively validated prognostic model for patients with locally advanced squamous cell carcinoma of the head and neck based on radiomics of computed tomography images . Cancers (Basel) . 2021 ; 13 ( 13 ): 3271 .

Sheikh K , Lee SH , Cheng Z , Lakshminarayanan P , Peng L , Han P , et al . Predicting acute radiation induced xerostomia in head and neck cancer using MR and CT radiomics of parotid and submandibular glands . Radiat Oncol . 2019 ; 14 ( 1 ): 131 .

Pan C , Zhou P , Tan J , Sun B , Guan R , Wang Z , et al . Liver tumor detection via a multi-scale intermediate multi-modal fusion network on MRI images . In: 2021 IEEE international conference on image processing (ICIP); 2021 19–22 Sep. Anchorage: IEEE ; 2021 . p. 299 – 303 .

Chen L , Zhou Z , Sher D , Zhang Q , Shah J , Pham NL , et al . Combining many-objective radiomics and 3D convolutional neural network through evidential reasoning to predict lymph node metastasis in head and neck cancer . Phys Med Biol . 2019 ; 64 ( 7 ): 075011 .

Tiwari P , Verma R . The pursuit of generalizability to enable clinical translation of radiomics . Radiol Artif Intell . 2021 ; 3 ( 1 ): e200227 .

Petch J , Di S , Nelson W . Opening the black box: the promise and limitations of explainable machine learning in cardiology . Can J Cardiol . 2022 ; 38 ( 2 ): 204 – 13 .

Zafar MR , Khan NMJA . DLIME: a deterministic local interpretable modelagnostic explanations approach for computer-aided diagnosis systems . ArXiv . 2019 . https://doi.org/10.48550/arXiv.1906.10263 https://doi.org/10.48550/arXiv.1906.10263 .

Giraud P , Giraud P , Nicolas E , Boisselier P , Alfonsi M , Rives M , et al . Interpretable machine learning model for locoregional relapse prediction in oropharyngeal cancers . Cancers (Basel) . 2020 ; 13 ( 1 ): 57 .

Greenwell BM . pdp: an R package for constructing partial dependence plots . R J . 2017 ; 9 : 421 .

Tan E , Merchant K , Kn BP , Cs A , Zhao JJ , Saffari SE , et al . CT-based morphologic and radiomics features for the classification of MYCN gene amplification status in pediatric neuroblastoma . Childs Nerv Syst . 2022 ; 38 ( 8 ): 1487 – 95 .

Altmann A , Toloşi L , Sander O , Lengauer T . Permutation importance: a corrected feature importance measure . Bioinformatics . 2010 ; 26 ( 10 ): 1340 – 7 .

Enke JS , Moltz JH , D’anastasi M , Kunz WG , Schmidt C , Maurus S , et al . Radiomics features of the spleen as surrogates for CT-based lymphoma diagnosis and subtype differentiation . Cancers (Basel) . 2022 ; 14 ( 3 ): 713 .

Rundo L , Beer L , Escudero Sanchez L , Crispin-Ortuzar M , Reinius M , Mccague C , et al . Clinically interpretable radiomics-based prediction of histopathologic response to neoadjuvant chemotherapy in high-grade serous ovarian carcinoma . Front Oncol . 2022 ; 12 : 868265 .

Smedley NF , Aberle DR , Hsu W . Using deep neural networks and interpretability methods to identify gene expression patterns that predict radiomic features and histology in non-small cell lung cancer . J Med Imaging (Bellingham) . 2021 ; 8 ( 3 ): 031906 .

Hua J , Xiong Z , Lowey J , Suh E , Dougherty ER . Optimal number of features as a function of sample size for various classification rules . Bioinformatics . 2005 ; 21 ( 8 ): 1509 – 15 .

Kumar V , Gu Y , Basu S , Berglund A , Eschrich SA , Schabath MB , et al . Radiomics: the process and the challenges . Magn Reson Imaging . 2012 ; 30 ( 9 ): 1234 – 48 .

Van Timmeren JE , Leijenaar RTH , Van Elmpt W , Wang J , Zhang Z , Dekker A , et al . Test-retest data for radiomics feature stability analysis: generalizable or study-specific? . Tomography . 2016 ; 2 ( 4 ): 361 – 5 .

Li Y , Reyhan M , Zhang Y , Wang X , Zhou J , Zhang Y , et al . The impact of phantom design and material-dependence on repeatability and reproducibility of CT-based radiomics features . Med Phys . 2022 ; 49 ( 3 ): 1648 – 59 .

Mackin D , Fave X , Zhang L , Fried D , Yang J , Taylor B , et al . Measuring computed tomography scanner variability of radiomics features . Investig Radiol . 2015 ; 50 ( 11 ): 757 – 65 .

Oliveira C , Amstutz F , Vuong D , Bogowicz M , Hüllner M , Foerster R , et al . Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDGPET imaging . EJNMMI Res . 2021 ; 11 ( 1 ): 79 .

Schurink NW , Van Kranen SR , Roberti S , Van Griethuysen JJM , Bogveradze N , Castagnoli F , et al . Sources of variation in multicenter rectal MRI data and their effect on radiomics feature reproducibility . Eur Radiol . 2022 ; 32 ( 3 ): 1506 – 16 .

Gabryś HS , Buettner F , Sterzing F , Hauswald H , Bangert M . Design and selection of machine learning methods using radiomics and dosiomics for normal tissue complication probability modeling of xerostomia . Front Oncol . 2018 ; 8 : 35 .

Xie C , Du R , Ho JW , Pang HH , Chiu KW , Lee EY , et al . Effect of machine learning re-sampling techniques for imbalanced datasets in 18 F-FDG PET-based radiomics model on prognostication performance in cohorts of head and neck cancer patients . Eur J Nucl Med Mol Imaging . 2020 ; 47 ( 12 ): 2826 – 35 .

Wang B , Yang J , Peng H , Ai J , An L , Yang B , et al . Brain tumor segmentation via multi-modalities interactive feature learning . Front Med (Lausanne) . 2021 ; 8 : 653925 .

Daisne JF , Grégoire V . Multimodalities imaging for target volume definition in radiotherapy . Bull Cancer . 2006 ; 93 ( 12 ): 1175 – 82 .

Wang J , Yuan C , Han C , Wen Y , Lu H , Liu C , et al . IMAL-Net: interpretable multi-task attention learning network for invasive lung adenocarcinoma screening in CT images . Med Phys . 2021 ; 48 ( 12 ): 7913 .