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Concomitant Prediction of the Ki67 and PIT-1 Expression in Pituitary Adenoma Using Different Radiomics Models

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Abstract

Objectives

To preoperatively predict the high expression of Ki67 and positive pituitary transcription factor 1 (PIT-1) simultaneously in pituitary adenoma (PA) using three different radiomics models.

Methods

A total of 247 patients with PA (training set: n = 198; test set: n = 49) were included in this retrospective study. The imaging features were extracted from preoperative contrast-enhanced T1WI (T1CE), T1-weighted imaging (T1WI), and T2-weighted imaging (T2WI). Feature selection was performed using Spearman’s rank correlation coefficient and least absolute shrinkage and selection operator (LASSO). The classic machine learning (CML), deep learning (DL), and deep learning radiomics (DLR) models were constructed using logistic regression (LR), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms. The area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, accuracy, negative predictive value (NPV) and positive predictive value (PPV) were calculated for the training and test sets. In addition, combined with clinical characteristics, the best CML and the best DL models (SVM classifier), the DL radiomics nomogram (DLRN) was constructed to aid clinical decision-making.

Results

Seven CML features, 96 DL features, and 107 DLR features were selected to construct CML, DL and DLR models. Compared to CML and DL model, the DLR model had the best performance. The AUC, sensitivity, specificity, accuracy, NPV and PPV were 0.827, 0.792, 0.800, 0.796, 0.800 and 0.792 in the test set, respectively.

Conclusions

Compared with CML and DL models, the DLR model shows the best performance in predicting the Ki67 and PIT-1 expression in PAs simultaneously.

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Abbreviations

AUC:

Area under the curve

CML:

Classic machine learning

DCA:

Decision curve analysis

DICOM:

Digital imaging and communications in medicine

DL:

Deep learning

DLR:

Deep learning radiomics

DLRN:

DL radiomics nomogram

GH:

Growth hormone

Grad-CAM:

Gradient-weighted class activation mapping

LASSO:

Least absolute shrinkage and selection operator

LR:

Logistic regression

MLP:

Multi-layer perceptron

MSE:

Mean Standard Error

NPV:

Negative predictive value

PA:

Pituitary adenoma

PIT-1:

Positive pituitary transcription factor 1

PPV:

Positive predictive value

ROI:

Region of interest

ROC:

Receiver operating characteristic

SVM:

Support vector machine

T1CE:

Contrast-enhanced T1-weighted imaging

T1WI:

T1-weighted imaging

T2WI:

T2-weighted imaging

5 References

  1. S. Melmed, U.B. Kaiser, M.B. Lopes, J. Bertherat, L.V. Syro, G. Raverot, M. Reincke, G. Johannsson, A. Beckers, M. Fleseriu, A. Giustina, J.A.H. Wass, K.K.Y. Ho, Clinical Biology of the Pituitary Adenoma, Endocr. Rev. 43(6) (2022) 1003–1037, https://doi.org/10.1210/endrev/bnac010

    Article  PubMed  PubMed Central  Google Scholar 

  2. M.W. Møller, M.S. Andersen, D. Glintborg, C.B. Pedersen, B. Halle, B.W. Kristensen, F.R. Poulsen, [Pituitary adenoma], Ugeskr. Laeger 181(20) (2019),

  3. C.S. Graffeo, K.J. Yagnik, L.P. Carlstrom, N. Lakomkin, I. Bancos, C. Davidge-Pitts, D. Erickson, G. Choby, B.E. Pollock, A.M. Chamberlain, J.J. Van Gompel, Pituitary Adenoma Incidence, Management Trends, and Long-term Outcomes: A 30-Year Population-Based Analysis, Mayo Clinic Proceedings 97(10) (2022) 1861–1871, https://doi.org/10.1016/j.mayocp.2022.03.017

  4. A.F. Daly, A. Beckers, The Epidemiology of Pituitary Adenomas, Endocrinol. Metab. Clin. North Am. 49(3) (2020) 347–355, https://doi.org/10.1016/j.ecl.2020.04.002

    Article  PubMed  Google Scholar 

  5. G. Raverot, M.D. Ilie, H. Lasolle, V. Amodru, J. Trouillas, F. Castinetti, T. Brue, Aggressive pituitary tumours and pituitary carcinomas, Nat. Rev. Endocrinol. 17(11) (2021) 671–684, https://doi.org/10.1038/s41574-021-00550-w

    Article  PubMed  Google Scholar 

  6. I.F. Burcea, V.N. Năstase, C. Poiană, Pituitary transcription factors in the immunohistochemical and molecular diagnosis of pituitary tumours - a systematic review, Endokrynol. Pol. 72(1) (2021) 53–63, https://doi.org/10.5603/EP.a2020.0090

    Article  CAS  PubMed  Google Scholar 

  7. K. Asmaro, M. Zhang, A.J. Rodrigues, A. Mohyeldin, V. Vigo, K. Nernekli, H. Vogel, D.E. Born, L. Katznelson, J.C. Fernandez-Miranda, Cytodifferentiation of pituitary tumors influences pathogenesis and cavernous sinus invasion, J. Neurosurg. 139(5) (2023) 1216–1224, https://doi.org/10.3171/2023.3.Jns221949

    Article  CAS  PubMed  Google Scholar 

  8. M. Luo, R. Tang, H. Wang, Tumor immune microenvironment in pituitary neuroendocrine tumors (PitNETs): increased M2 macrophage infiltration and PD-L1 expression in PIT1-lineage subset, J. Neurooncol. 163(3) (2023) 663–674, https://doi.org/10.1007/s11060-023-04382-8

    Article  CAS  PubMed  Google Scholar 

  9. X.Y. Wan, J. Chen, J.W. Wang, Y.C. Liu, K. Shu, T. Lei, Overview of the 2022 WHO Classification of Pituitary Adenomas/Pituitary Neuroendocrine Tumors: Clinical Practices, Controversies, and Perspectives, Curr Med Sci 42(6) (2022) 1111–1118, https://doi.org/10.1007/s11596-022-2673-6

    Article  PubMed  Google Scholar 

  10. X.Y. Liu, M.G. Wei, J. Liang, H.H. Xu, J.J. Wang, J. Wang, X.P. Yang, F.F. Lv, K.Q. Wang, J.H. Duan, Y. Tu, S. Zhang, C. Chen, X.H. Li, Injury-preconditioning secretome of umbilical cord mesenchymal stem cells amplified the neurogenesis and cognitive recovery after severe traumatic brain injury in rats, Journal of Neurochemistry 153(2) (2020) 230–251, https://doi.org/10.1111/jnc.14859

    Article  CAS  PubMed  Google Scholar 

  11. L. Yuhan, W. Zhiqun, T. Jihui, P. Renlong, Ki-67 labeling index and Knosp classification of pituitary adenomas, Br. J. Neurosurg. (2021) 1–5, https://doi.org/10.1080/02688697.2021.1884186

  12. L. Mastronardi, A. Guiducci, C. Spera, F. Puzzilli, F. Liberati, G. Maira, Ki-67 labelling index and invasiveness among anterior pituitary adenomas: analysis of 103 cases using the MIB-1 monoclonal antibody, J. Clin. Pathol. 52(2) (1999) 107–111, https://doi.org/10.1136/jcp.52.2.107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. C. Xue, S. Liu, J. Deng, X. Liu, S. Li, P. Zhang, J. Zhou, Apparent Diffusion Coefficient Histogram Analysis for the Preoperative Evaluation of Ki-67 Expression in Pituitary Macroadenoma, Clin Neuroradiol 32(1) (2022) 269–276, https://doi.org/10.1007/s00062-021-01134-x

    Article  PubMed  Google Scholar 

  14. A. Conficoni, P. Feraco, D. Mazzatenta, M. Zoli, S. Asioli, C. Zenesini, V.P. Fabbri, M. Cellerini, A. Bacci, Biomarkers of pituitary macroadenomas aggressive behaviour: a conventional MRI and DWI 3T study, Br. J. Radiol. 93(1113) (2020) 20200321, https://doi.org/10.1259/bjr.20200321

    Article  PubMed  PubMed Central  Google Scholar 

  15. X.-j. Shu, H. Chang, Q. Wang, W.-g. Chen, K. Zhao, B.-y. Li, G.-c. Sun, S.-b. Chen, B.-n. Xu, Deep Learning model-based approach for preoperative prediction of Ki67 labeling index status in a noninvasive way using magnetic resonance images: A single-center study, Clinical Neurology and Neurosurgery 219 (2022), https://doi.org/10.1016/j.clineuro.2022.107301

  16. L. Ugga, R. Cuocolo, D. Solari, E. Guadagno, A. D’Amico, T. Somma, P. Cappabianca, M.L. Del Basso de Caro, L.M. Cavallo, A. Brunetti, Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning, Neuroradiology 61(12) (2019) 1365–1373, https://doi.org/10.1007/s00234-019-02266-1

  17. A. Peng, H. Dai, H. Duan, Y. Chen, J. Huang, L. Zhou, L. Chen, A machine learning model to precisely immunohistochemically classify pituitary adenoma subtypes with radiomics based on preoperative magnetic resonance imaging, European Journal of Radiology 125 (2020), https://doi.org/10.1016/j.ejrad.2020.108892

  18. Y. Chen, F. Cai, J. Cao, F. Gao, Y. Lv, Y. Tang, A. Zhang, W. Yan, Y. Wang, X. Hu, S. Chen, X. Dong, J. Zhang, Q. Wu, Analysis of Related Factors of Tumor Recurrence or Progression After Transnasal Sphenoidal Surgical Treatment of Large and Giant Pituitary Adenomas and Establish a Nomogram to Predict Tumor Prognosis, Front Endocrinol (Lausanne) 12 (2021) 793337, https://doi.org/10.3389/fendo.2021.793337

    Article  PubMed  Google Scholar 

  19. X.J. Shu, H. Chang, Q. Wang, W.G. Chen, K. Zhao, B.Y. Li, G.C. Sun, S.B. Chen, B.N. Xu, Deep Learning model-based approach for preoperative prediction of Ki67 labeling index status in a noninvasive way using magnetic resonance images: A single-center study, Clin. Neurol. Neurosurg. 219 (2022) 107301, https://doi.org/10.1016/j.clineuro.2022.107301

    Article  PubMed  Google Scholar 

  20. S. Aydin, N. Comunoglu, M.L. Ahmedov, O.P. Korkmaz, B. Oz, P. Kadioglu, N. Gazioglu, N. Tanriover, Clinicopathologic Characteristics and Surgical Treatment of Plurihormonal Pituitary Adenomas, World Neurosurg 130 (2019) e765-e774, https://doi.org/10.1016/j.wneu.2019.06.217

    Article  PubMed  Google Scholar 

  21. X. Li, P.S. Morgan, J. Ashburner, J. Smith, C. Rorden, The first step for neuroimaging data analysis: DICOM to NIfTI conversion, J Neurosci Methods 264 (2016) 47–56, https://doi.org/10.1016/j.jneumeth.2016.03.001

  22. A. Myronenko, M.M.R. Siddiquee, D. Yang, Y. He, D. Xu, Automated head and neck tumor segmentation from 3D PET/CT, HECKTOR@MICCAI, 2022.

  23. L.R. Dice, MEASURES OF THE AMOUNT OF ECOLOGIC ASSOCIATION BETWEEN SPECIES, Ecology 26(3) (1945) 297–302, https://doi.org/10.2307/1932409

    Article  Google Scholar 

  24. J. Fan, M. Chen, J. Luo, S. Yang, J. Shi, Q. Yao, X. Zhang, S. Du, H. Qu, Y. Cheng, S. Ma, M. Zhang, X. Xu, Q. Wang, S. Zhan, The prediction of asymptomatic carotid atherosclerosis with electronic health records: a comparative study of six machine learning models, BMC Med. Inform. Decis. Mak. 21(1) (2021) 115, https://doi.org/10.1186/s12911-021-01480-3

    Article  PubMed  PubMed Central  Google Scholar 

  25. P. Schober, T.R. Vetter, Logistic Regression in Medical Research, Anesth Analg 132(2) (2021) 365–366, https://doi.org/10.1213/ane.0000000000005247

    Article  PubMed  PubMed Central  Google Scholar 

  26. J.J. Raposo-Neto, E. Kowalski-Neto, W.B. Luiz, E.A. Fonseca, A. Cedro, M.N. Singh, F.L. Martin, P.F. Vassallo, L.C.G. Campos, V.G. Barauna, Near-Infrared Spectroscopy with Supervised Machine Learning as a Screening Tool for Neutropenia, J Pers Med 14(1) (2023), https://doi.org/10.3390/jpm14010009

  27. H.Y. Wu, C.A. Gong, S.P. Lin, K.Y. Chang, M.Y. Tsou, C.K. Ting, Predicting postoperative vomiting among orthopedic patients receiving patient-controlled epidural analgesia using SVM and LR, Sci Rep 6 (2016) 27041, https://doi.org/10.1038/srep27041

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. N.A. Almansour, H.F. Syed, N.R. Khayat, R.K. Altheeb, R.E. Juri, J. Alhiyafi, S. Alrashed, S.O. Olatunji, Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study, Comput. Biol. Med. 109 (2019) 101–111, https://doi.org/10.1016/j.compbiomed.2019.04.017

    Article  PubMed  Google Scholar 

  29. K. Nagawa, M. Suzuki, Y. Yamamoto, K. Inoue, E. Kozawa, T. Mimura, K. Nakamura, M. Nagata, M. Niitsu, Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies, Sci Rep 11(1) (2021) 9821, https://doi.org/10.1038/s41598-021-89311-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. C.Y. Hsu, P.Y. Liu, S.H. Liu, Y. Kwon, C.J. Lavie, G.M. Lin, Machine Learning for Electrocardiographic Features to Identify Left Atrial Enlargement in Young Adults: CHIEF Heart Study, Front Cardiovasc Med 9 (2022) 840585, https://doi.org/10.3389/fcvm.2022.840585

    Article  PubMed  PubMed Central  Google Scholar 

  31. R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization, International Journal of Computer Vision 128(2) (2020) 336–359, https://doi.org/10.1007/s11263-019-01228-7

    Article  Google Scholar 

  32. A.J. Vickers, E.B. Elkin, Decision curve analysis: a novel method for evaluating prediction models, Med Decis Making 26(6) (2006) 565–574, https://doi.org/10.1177/0272989x06295361

    Article  PubMed  PubMed Central  Google Scholar 

  33. D.M. Wei, W.J. Chen, R.M. Meng, N. Zhao, X.Y. Zhang, D.Y. Liao, G. Chen, Augmented expression of Ki-67 is correlated with clinicopathological characteristics and prognosis for lung cancer patients: an up-dated systematic review and meta-analysis with 108 studies and 14,732 patients, Respir. Res. 19(1) (2018) 150, https://doi.org/10.1186/s12931-018-0843-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. G. Zada, N. Lin, E.R. Laws, Jr., Patterns of extrasellar extension in growth hormone-secreting and nonfunctional pituitary macroadenomas, Neurosurg. Focus 29(4) (2010) E4, https://doi.org/10.3171/2010.7.Focus10155

    Article  PubMed  Google Scholar 

  35. X. Cai, J. Zhu, J. Yang, C. Tang, F. Yuan, Z. Cong, C. Ma, A Nomogram for Preoperatively Predicting the Ki-67 Index of a Pituitary Tumor: A Retrospective Cohort Study, Frontiers in Oncology 11 (2021), https://doi.org/10.3389/fonc.2021.687333

  36. H. Li, Z. Liu, F. Li, F. Shi, Y. Xia, Q. Zhou, Q. Zeng, Preoperatively Predicting Ki67 Expression in Pituitary Adenomas Using Deep Segmentation Network and Radiomics Analysis Based on Multiparameter MRI, Academic Radiology (2023), https://doi.org/10.1016/j.acra.2023.05.023

  37. Z. Li, Y. Wang, J. Yu, Y. Guo, W. Cao, Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma, Scientific Reports 7(1) (2017), https://doi.org/10.1038/s41598-017-05848-2

  38. X. Dong, J. Yang, B. Zhang, Y. Li, G. Wang, J. Chen, Y. Wei, H. Zhang, Q. Chen, S. Jin, L. Wang, H. He, M. Gan, W. Ji, Deep Learning Radiomics Model of Dynamic Contrast-Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma, Journal of Magnetic Resonance Imaging 59(1) (2023) 108–119, https://doi.org/10.1002/jmri.28745

    Article  PubMed  Google Scholar 

  39. M.K.H. Khan, W. Guo, J. Liu, F. Dong, Z. Li, T.A. Patterson, H. Hong, Machine learning and deep learning for brain tumor MRI image segmentation, Experimental Biology and Medicine (2023), https://doi.org/10.1177/15353702231214259

    Article  PubMed  PubMed Central  Google Scholar 

  40. X. Kong, Y. Mao, F. Xi, Y. Li, Y. Luo, J. Ma, Development of a nomogram based on radiomics and semantic features for predicting chromosome 7 gain/chromosome 10 loss in IDH wild-type histologically low-grade gliomas, Frontiers in Oncology 13 (2023), https://doi.org/10.3389/fonc.2023.1196614

  41. J. Li, T. Zhang, J. Ma, N. Zhang, Z. Zhang, Z. Ye, Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors, Front Oncol 12 (2022) 934735, https://doi.org/10.3389/fonc.2022.934735

    Article  PubMed  PubMed Central  Google Scholar 

  42. K. Yun, T. He, S. Zhen, M. Quan, X. Yang, D. Man, S. Zhang, W. Wang, X. Han, Development and validation of explainable machine-learning models for carotid atherosclerosis early screening, J. Transl. Med. 21(1) (2023) 353, https://doi.org/10.1186/s12967-023-04093-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. J. Wang, H. Chen, H. Wang, W. Liu, D. Peng, Q. Zhao, M. Xiao, A Risk Prediction Model for Physical Restraints Among Older Chinese Adults in Long-term Care Facilities: Machine Learning Study, J. Med. Internet Res. 25 (2023) e43815, https://doi.org/10.2196/43815

    Article  PubMed  PubMed Central  Google Scholar 

  44. A. Peng, H. Dai, H. Duan, Y. Chen, J. Huang, L. Zhou, L. Chen, A machine learning model to precisely immunohistochemically classify pituitary adenoma subtypes with radiomics based on preoperative magnetic resonance imaging, Eur. J. Radiol. 125 (2020) 108892, https://doi.org/10.1016/j.ejrad.2020.108892

    Article  PubMed  Google Scholar 

  45. Y. Zheng, D. Zhou, H. Liu, M. Wen, CT-based radiomics analysis of different machine learning models for differentiating benign and malignant parotid tumors, Eur. Radiol. 32(10) (2022) 6953–6964, https://doi.org/10.1007/s00330-022-08830-3

    Article  PubMed  Google Scholar 

  46. Y. Fan, S. Jiang, M. Hua, S. Feng, M. Feng, R. Wang, Machine Learning-Based Radiomics Predicts Radiotherapeutic Response in Patients With Acromegaly, Frontiers in Endocrinology 10 (2019), https://doi.org/10.3389/fendo.2019.00588

  47. R. Cuocolo, L. Ugga, D. Solari, S. Corvino, A. D’Amico, D. Russo, P. Cappabianca, L.M. Cavallo, A. Elefante, Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI, Neuroradiology 62(12) (2020) 1649–1656, https://doi.org/10.1007/s00234-020-02502-z

    Article  PubMed  PubMed Central  Google Scholar 

  48. H. Wang, W. Zhang, S. Li, Y. Fan, M. Feng, R. Wang, Development and Evaluation of Deep Learning-based Automated Segmentation of Pituitary Adenoma in Clinical Task, The Journal of Clinical Endocrinology & Metabolism 106(9) (2021) 2535–2546, https://doi.org/10.1210/clinem/dgab371

    Article  Google Scholar 

  49. G. Raverot, P. Burman, A. McCormack, A. Heaney, S. Petersenn, V. Popovic, J. Trouillas, O.M. Dekkers, European Society of Endocrinology Clinical Practice Guidelines for the management of aggressive pituitary tumours and carcinomas, European Journal of Endocrinology 178(1) (2018) G1-G24, https://doi.org/10.1530/eje-17-0796

    Article  CAS  PubMed  Google Scholar 

  50. S.K. Cheok, J.D. Carmichael, G. Zada, Management of growth hormone–secreting pituitary adenomas causing acromegaly: a practical review of surgical and multimodal management strategies for neurosurgeons, Journal of Neurosurgery (2023) 1–10, https://doi.org/10.3171/2023.8.Jns221975

  51. A. Ishida, H. Shiramizu, H. Yoshimoto, M. Kato, N. Inoshita, N. Miki, M. Ono, S. Yamada, Resection of the Cavernous Sinus Medial Wall Improves Remission Rate in Functioning Pituitary Tumors: Retrospective Analysis of 248 Consecutive Cases, Neurosurgery 91(5) (2022) 775–781, https://doi.org/10.1227/neu.0000000000002109

    Article  PubMed  Google Scholar 

  52. F. Salehi, A. Agur, B.W. Scheithauer, K. Kovacs, R.V. Lloyd, M. Cusimano, Ki-67 in Pituitary Neoplasms, Neurosurgery 65(3) (2009) 429–437, https://doi.org/10.1227/01.Neu.0000349930.66434.82

    Article  PubMed  Google Scholar 

  53. J.P. Andrews, R.S. Joshi, M.P. Pereira, T. Oh, A.F. Haddad, K.M. Pereira, R.C. Osorio, K.C. Donohue, Z. Peeran, S. Sudhir, S. Jain, A. Beniwal, A.S. Chopra, N.S. Sandhu, T. Tihan, L. Blevins, M.K. Aghi, Plurihormonal PIT-1–Positive Pituitary Adenomas: A Systematic Review and Single-Center Series, World Neurosurgery 151 (2021) e185-e191, https://doi.org/10.1016/j.wneu.2021.04.003

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank Fei Zheng, M.M., Department of Radiology, Peking University People’s Hospital, for her assistance in methods.

Funding

This study has received funding by the Beijing Hospitals Authority Clinical Medicine Development of Special (XMLX202108) and the collaborative innovative major special project supported by Beijing Municipal Science & Technology Commission (No. Z191100006619088).

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  1. Fangzheng Liu and Yuying Zang are co-first authors.

Authors and Affiliations

  1. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, 100070, Beijing, China

    Fangzheng Liu, Wentao Wu, Xin Liu, Yifan Song, Jintian Xu & Songbai Gui

  2. Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, 100070, Beijing, China

    Yuying Zang, Limei Feng, Xinyao Shi & Xuzhu Chen

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Liu, F., Zang, Y., Feng, L. et al. Concomitant Prediction of the Ki67 and PIT-1 Expression in Pituitary Adenoma Using Different Radiomics Models. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01121-x

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