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Behmanesh, Baharak, Abdi-Saray, Akbar, Deevband, Mohammad Reza, Amoui, Mahasti, Haghighatkhah, Hamid Reza. (1403). Radiomics Analysis for Clinical Decision Support in 177Lu-DOTATATE Therapy of Metastatic Neuroendocrine Tumors using CT Images. سامانه مدیریت نشریات علمی, 14(5), 423-434. doi: 10.31661/jbpe.v0i0.2112-1444
Baharak Behmanesh; Akbar Abdi-Saray; Mohammad Reza Deevband; Mahasti Amoui; Hamid Reza Haghighatkhah. "Radiomics Analysis for Clinical Decision Support in 177Lu-DOTATATE Therapy of Metastatic Neuroendocrine Tumors using CT Images". سامانه مدیریت نشریات علمی, 14, 5, 1403, 423-434. doi: 10.31661/jbpe.v0i0.2112-1444
Behmanesh, Baharak, Abdi-Saray, Akbar, Deevband, Mohammad Reza, Amoui, Mahasti, Haghighatkhah, Hamid Reza. (1403). 'Radiomics Analysis for Clinical Decision Support in 177Lu-DOTATATE Therapy of Metastatic Neuroendocrine Tumors using CT Images', سامانه مدیریت نشریات علمی, 14(5), pp. 423-434. doi: 10.31661/jbpe.v0i0.2112-1444
Behmanesh, Baharak, Abdi-Saray, Akbar, Deevband, Mohammad Reza, Amoui, Mahasti, Haghighatkhah, Hamid Reza. Radiomics Analysis for Clinical Decision Support in 177Lu-DOTATATE Therapy of Metastatic Neuroendocrine Tumors using CT Images. سامانه مدیریت نشریات علمی, 1403; 14(5): 423-434. doi: 10.31661/jbpe.v0i0.2112-1444

Radiomics Analysis for Clinical Decision Support in 177Lu-DOTATATE Therapy of Metastatic Neuroendocrine Tumors using CT Images

Journal of Biomedical Physics and Engineering
دوره 14، شماره 5، دی 2024، صفحه 423-434    اصل مقاله (939.18 K)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2112-1444
نویسندگان
Baharak Behmanesh1؛ Akbar Abdi-Saray1؛ Mohammad Reza Deevband* 2؛ Mahasti Amoui3؛ Hamid Reza Haghighatkhah4
1Department of Nuclear Physics Faculty of Science, Urmia University, Oroumieh, Iran
2Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
3Department of Nuclear Medicine, Shohada-e Tajrish Hospital, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4Department of Radiology and Medical Imaging Center, Shohada-e Tajrish Hospital, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
چکیده
Background: Radiomics is the computation of quantitative image features extracted from medical imaging modalities to help clinical decision support systems, which could ultimately meliorate personalized management based on individual characteristics.
Objective: This study aimed to create a predictive model of response to peptide receptor radionuclide therapy (PRRT) using radiomics computed tomography (CT) images to decrease the dose for patients if they are not a candidate for treatment.
Material and Methods: In the current retrospective study, 34 patients with neuroendocrine tumors whose disease is clinically confirmed participated. Effective factors in the treatment were selected by eXtreme gradient boosting (XGBoost) and minimum redundancy maximum relevance (mRMR). Classifiers of decision trees (DT), random forest (RF), and K-nearest neighbors (KNN) with selected quantitative and clinical features were used for modeling. A confusion matrix was used to evaluate the performance of the model.
Results: Out of 866 quantitative and clinical features, nine features with the XGBoost method and ten features with the mRMR pattern were selected that had the most relevance in predicting response to treatment. Selected features of the XGBoost method in integration with the RF classifier provided the highest accuracy (accuracy: 89%), and features selected by the mRMR method in combination with the RF classifier showed satisfactory performance (accuracy: 74%). 
Conclusion: This exploratory analysis shows that radiomic features with high accuracy can effectively predict response to personalize treatment.

تازه های تحقیق

Baharak Behmanesh (PubMed)

Mohammad Reza Deevband (Google Scholar)

کلیدواژه‌ها
Classifier؛ CT؛ 177Lu-DOTATATE؛ Neuroendocrine Tumors؛ PRRT؛ Radiomics؛ Radioisotopes؛ Tomography
سایر فایل های مرتبط با مقاله
مراجع
  1. Mirvis E, Toumpanakis C, Mandair D, Gnanasegaran G, Caplin M, Navalkissoor S. Efficacy and tolerability of peptide receptor radionuclide therapy (PRRT) in advanced metastatic bronchial neuroendocrine tumours (NETs). Lung Cancer. 2020;150:70-5. doi: 10.1016/j.lungcan.2020.10.005. PubMed PMID: 33075738.
  2. Mittra ES. Neuroendocrine Tumor Therapy: 177Lu-DOTATATE. AJR Am J Roentgenol. 2018;211(2):278-85. doi: 10.2214/AJR.18.19953. PubMed PMID: 29949416.
  3. Weilin M, Xu H, Yang L, Wenqi C, Huanyu W, et al. Propensity score-matched analysis of clinical outcome after enucleation versus regular pancreatectomy in patients with small non-functional pancreatic neuroendocrine tumors. Pancreatology. 2020;20(2):169-76. doi: 10.1016/j.pan.2019.12.007. PubMed PMID: 31941586.
  4. Chan DL, Singh S. Current Chemotherapy Use in Neuroendocrine Tumors. Endocrinol Metab Clin North Am. 2018;47(3):603-14. doi: 10.1016/j.ecl.2018.04.006. PubMed PMID: 30098718.
  5. Chicheportiche A, Grozinsky-Glasberg S, Gross DJ, Krausz Y, et al. Predictive power of the post-treatment scans after the initial or first two courses of [177Lu]-DOTA-TATE. EJNMMI Phys. 2018;5(1):36. doi: 10.1186/s40658-018-0234-7. PubMed PMID: 30535780. PubMed PMCID: PMC6286905.
  6. Dash A, Pillai MR, Knapp FF Jr. Production of (177)Lu for Targeted Radionuclide Therapy: Available Options. Nucl Med Mol Imaging. 2015;49(2):85-107. doi: 10.1007/s13139-014-0315-z. PubMed PMID: 26085854. PubMed PMCID: PMC4463871.
  7. Smith-Palmer J, Leeuwenkamp OR, Virk J, Reed N. Lutetium oxodotreotide (177Lu-Dotatate) for the treatment of unresectable or metastatic progressive gastroenteropancreatic neuroendocrine tumors: a cost-effectiveness analysis for Scotland. BMC Cancer. 2021;21(1):10. doi: 10.1186/s12885-020-07710-7. PubMed PMID: 33402120. PubMed PMCID: PMC7786468.
  8. Abou Jokh Casas E, Pubul Núñez V, Anido-Herranz U, et al. Evaluation of 177Lu-Dotatate treatment in patients with metastatic neuroendocrine tumors and prognostic factors. World J Gastroenterol. 2020;26(13):1513-24. doi: 10.3748/wjg.v26.i13.1513. PubMed PMID: 32308351. PubMed PMCID: PMC7152518.
  9. Levart D, Kalogianni E, Corcoran B, Mulholland N, Vivian G. Radiation precautions for inpatient and outpatient 177Lu-DOTATATE peptide receptor radionuclide therapy of neuroendocrine tumours. EJNMMI Phys. 2019;6(1):7. doi: 10.1186/s40658-019-0243-1. PubMed PMID: 31025215. PubMed PMCID: PMC6484059.
  10. Dromain C, Sundin A, Najran P, Vidal Trueba H, Dioguardi Burgio M, et al. Tumor Growth Rate to Predict the Outcome of Patients with Neuroendocrine Tumors: Performance and Sources of Variability. 2021;111(9):831-9. doi: 10.1159/000510445. PubMed PMID: 32717738.
  11. Liang W, Yang P, Huang R, Xu L, Wang J, Liu W, et al. A Combined Nomogram Model to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors. Clin Cancer Res. 2019;25(2):584-94. doi: 10.1158/1078-0432.CCR-18-1305.
  12. Zhao Z, Bian Y, Jiang H, Fang X, Li J, et al. CT-Radiomic Approach to Predict G1/2 Nonfunctional Pancreatic Neuroendocrine Tumor. Acad Radiol. 2020;27(12):e272-81. doi: 10.1016/j.acra.2020.01.002. PubMed PMID: 32037260.
  13. Horvat N, Bates DDB, Petkovska I. Novel imaging techniques of rectal cancer: what do radiomics and radiogenomics have to offer? A literature review. Abdom Radiol (NY). 2019;44(11):3764-74. doi: 10.1007/s00261-019-02042-y. PubMed PMID: 31055615. PubMed PMCID: PMC6824982.
  14. Fave X, Zhang L, Yang J, Mackin D, Balter P, et al. Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer. Sci Rep. 2017;7(1):588. doi: 10.1038/s41598-017-00665-z. PubMed PMID: 28373718. PubMed PMCID: PMC5428827.
  15. Meng Y, Sun J, Qu N, Zhang G, Yu T, Piao H. Application of Radiomics for Personalized Treatment of Cancer Patients. Cancer Manag Res. 2019;11:10851-8. doi: 10.2147/CMAR.S232473. PubMed PMID: 31920394. PubMed PMCID: PMC6941598.
  16. Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001;69(3):89-95. doi: 10.1067/mcp.2001.113989. PubMed PMID: 11240971.
  17. Strosberg J, El-Haddad G, Wolin E, Hendifar A, Yao J, Chasen B, et al. Phase 3 Trial of 177Lu-Dotatate for Midgut Neuroendocrine Tumors. N Engl J Med. 2017;376(2):125-35. doi: 10.1056/NEJMoa1607427. PubMed PMID: 28076709. PubMed PMCID: PMC5895095.
  18. Van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, et al. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res. 2017;77(21):e104-7. doi: 10.1158/0008-5472.CAN-17-0339. PubMed PMID: 29092951. PubMed PMCID: PMC5672828.
  19. Larue RT, Defraene G, De Ruysscher D, Lambin P, van Elmpt W. Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br J Radiol. 2017;90(1070):20160665. doi: 10.1259/bjr.20160665. PubMed PMID: 27936886. PubMed PMCID: PMC5685111.
  20. Rizzo S, Botta F, Raimondi S, Origgi D, Fanciullo C, Morganti AG, Bellomi M. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp. 2018;2(1):36. doi: 10.1186/s41747-018-0068-z. PubMed PMID: 30426318. PubMed PMCID: PMC6234198.
  21. Wang J, Xu J, Zhao C, Peng Y, Wang H. An ensemble feature selection method for high-dimensional data based on sort aggregation. Syst Sci Control Eng. 2019;7(2):32-9. doi: 10.1080/21642583.2019.1620658.
  22. Berrendero JR, Cuevas A, Torrecilla JL. The mRMR variable selection method: a comparative study for functional data. J Stat Comput Simul. 2016;86(5):891-907. doi: 10.1080/00949655.2015.1042378.
  23. Islam MR, Kamal ARM, Sultana N, Islam R, Moni MA, ulhaq A, et al. Detecting Depression Using K-Nearest Neighbors (KNN) Classification Technique. 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2); Rajshahi, Bangladesh: IEEE; 2018. doi: 10.1109/IC4ME2.2018.8465641.
  24. Blanco-Justicia A, Domingo-Ferrer J. Machine learning explainability through comprehensible decision trees. International Cross-Domain Conference for Machine Learning and Knowledge Extraction. Springer; 2019. p. 15-26.
  25. 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. doi: 10.1016/j.csbj.2019.07.001. PubMed PMID: 31388413. PubMed PMCID: PMC6667772.
  26. Paul A, Mukherjee DP, Das P, Gangopadhyay A, Chintha AR, Kundu S. Improved Random Forest for Classification. IEEE Trans Image Process. 2018;27(8):4012-24. doi: 10.1109/TIP.2018.2834830. PubMed PMID: 29993742.
  27. Coroller TP, Grossmann P, Hou Y, Rios Velazquez E, Leijenaar RT, et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol. 2015;114(3):345-50. doi: 10.1016/j.radonc.2015.02.015. PubMed PMID: 25746350. PubMed PMCID: PMC4400248.
  28. Susmaga R. Confusion matrix visualization. Intelligent information processing and web mining; Berlin, Heidelberg: Springer; 2004. p. 107-16.
  29. Grimaldi S, Terroir M, Caramella C. Advances in oncological treatment: limitations of RECIST 1.1 criteria. Q J Nucl Med Mol Imaging. 2018;62(2):129-39. doi: 10.23736/S1824-4785.17.03038-2. PubMed PMID: 29166754.
  30. Green S, Weiss GR. Southwest Oncology Group standard response criteria, endpoint definitions and toxicity criteria. Invest New Drugs. 1992;10(4):239-53. doi: 10.1007/BF00944177. PubMed PMID: 1487397.
  31. Crombé A, Kind M, Ray-Coquard I, Isambert N, Chevreau C, André T, et al. Progressive Desmoid Tumor: Radiomics Compared With Conventional Response Criteria for Predicting Progression During Systemic Therapy-A Multicenter Study by the French Sarcoma Group. AJR Am J Roentgenol. 2020;215(6):1539-48. doi: 10.2214/AJR.19.22635. PubMed PMID: 32991215.
  32. Ligero M, Garcia-Ruiz A, Viaplana C, Villacampa G, Raciti MV, Landa J, et al. A CT-based Radiomics Signature Is Associated with Response to Immune Checkpoint Inhibitors in Advanced Solid Tumors. Radiology. 2021;299(1):109-19. doi: 10.1148/radiol.2021200928. PubMed PMID: 33497314.
  33. Vergote I, Rustin GJS, Eisenhauer EA, Kristensen GB, Pujade-Lauraine E, et al. Re: New Guidelines to Evaluate the Response to Treatment in Solid Tumors [Ovarian Cancer]. J Natl Cancer Inst. 2000;92(18):1534-5. doi: 10.1093/jnci/92.18.1534.
  34. Nishino M. Tumor Response Assessment for Precision Cancer Therapy: Response Evaluation Criteria in Solid Tumors and Beyond. Am Soc Clin Oncol Educ Book. 2018;38:1019-29. doi: 10.1200/EDBK_201441. PubMed PMID: 30231378.
  35. Huynh E, Coroller TP, Narayan V, Agrawal V, Hou Y, et al. CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer. Radiother Oncol. 2016;120(2):258-66. doi: 10.1016/j.radonc.2016.05.024. PubMed PMID: 27296412.
  36. Longadge R, Dongre S. Class Imbalance Problem in Data Mining Review. 2013;2:1305-11.
  37. Bethanney Janney J, Roslin SE, Kumar SK. 6 - Analysis of skin lesions using machine learning techniques. In: Verma JK, Paul S, Johri P., editors. Computational Intelligence and Its Applications in Healthcare. Elsevier Inc Academic Press; 2020.
  38. Zhou Y, He L, Huang Y, Chen S, Wu P, Ye W, et al. CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma. Abdom Radiol (NY). 2017;42(6):1695-704. doi: 10.1007/s00261-017-1072-0. PubMed PMID: 28180924.
  39. Bian Y, Jiang H, Ma C, Wang L, Zheng J, Jin G, Lu J. CT-Based Radiomics Score for Distinguishing Between Grade 1 and Grade 2 Nonfunctioning Pancreatic Neuroendocrine Tumors. AJR Am J Roentgenol. 2020;215(4):852-63. doi: 10.2214/AJR.19.22123. PubMed PMID: 32755201.
  40. Sala E, Mema E, Himoto Y, Veeraraghavan H, Brenton JD, Snyder A, Weigelt B, Vargas HA. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin Radiol. 2017;72(1):3-10. doi: 10.1016/j.crad.2016.09.013. PubMed PMID: 27742105. PubMed PMCID: PMC5503113.
  41. Hunter LA, Krafft S, Stingo F, Choi H, Martel MK, Kry SF, Court LE. High quality machine-robust image features: identification in nonsmall cell lung cancer computed tomography images. Med Phys. 2013;40(12):121916. doi: 10.1118/1.4829514. PubMed PMID: 24320527. PubMed PMCID: PMC4108720.
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