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Review

Radiomics in surgical oncology: applications and challenges

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References

  • World Health Organization. Fact sheet: cancer. Geneva, Switzerland: WHO; 2018.
  • Schirrmacher V. From chemotherapy to biological therapy: a review of novel concepts to reduce the side effects of systemic cancer treatment. Int J Oncol. 2019;54(2):407–419.
  • Arruebo M, Vilaboa N, Saez-Gutierrez B, et al. Assessment of the evolution of cancer treatment therapies. Cancers. 2011;3(3):3279–3330.
  • Mokdad AA, Minter RM, Zhu H, et al. Neoadjuvant therapy followed by resection versus upfront resection for resectable pancreatic cancer: a propensity score matched analysis. J Clin Oncol. 2017;35(5):515–522.
  • Njei B. Surgery vs. chemo-radiotherapy plus surgery in resectable esophageal cancer: a systematic review of randomized controlled trials. Gastrointest Cancer Res. 2012;5(4 Suppl 2):S9.
  • Liu SV, Melstrom L, Yao K, et al. Neoadjuvant therapy for breast cancer. J Surg Oncol. 2010;101(4):283–291.
  • Julien LA, Thorson AG. Current neoadjuvant strategies in rectal cancer. J Surg Oncol. 2010;101(4):321–326.
  • Fathi AT, Brahmer JR. Chemotherapy for advanced stage non-small cell lung cancer. Semin Thorac Cardiovasc Surg. 2008;20(3):210–216.
  • Balmanoukian A, Ettinger DS. Managing the patient with borderline resectable lung cancer. Oncology. 2010;24(3):234–241.
  • Hayes DF, Schott AF. Neoadjuvant chemotherapy: What are the benefits for the patient and for the investigator? J Natl Cancer Inst Monogr. 2015;2015(51):36–39.
  • Glynne-Jones R, Grainger J, Harrison M, et al. Neoadjuvant chemotherapy prior to preoperative chemoradiation or radiation in rectal cancer: Should we be more cautious? Br J Cancer. 2006;94(3):363–371.
  • Browner I, Purtell M. Chemotherapy in the older patient with operable non-small cell lung cancer: neoadjuvant and adjuvant regimens. Thorac Surg Clin. 2009;19(3):377–389.
  • Rosenbaum E. Everyone’s guide to cancer supportive care: a comprehensive handbook for patients and their families. Kansas City (MO): Andrews McMeel Publishing; 2005.
  • DeVita VT, Chu E. A history of cancer chemotherapy. Cancer Res. 2008;68(21):8643–8653.
  • Sevin B-U, Knapstein PG, Kochli OR, et al. Multimodality therapy in gynecologic oncology. New York (NY): Thieme Publishing Group; 1996.
  • Tao JJ, Visvanathan K, Wolff AC. Long term side effects of adjuvant chemotherapy in patients with early breast cancer. The Breast. 2015;24:S149–S153.
  • Baker J, Kornguth PJ, Soo MS, et al. Sonography of solid breast lesions: observer variability of lesion description and assessment. AJR Am J Roentgenol. 1999;172(6):1621–1625.
  • Van de Steene J, Linthout N, de Mey J, et al. Definition of gross tumor volume in lung cancer: Inter-observer variability. Radiother Oncol. 2002;62(1):37–49.
  • Yip SS, Liu Y, Parmar C, et al. Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer. Sci Rep. 2017;7(1):1–11.
  • Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441–446.
  • Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology. 2016;278(2):563–577.
  • Parekh V, Jacobs MA. Radiomics: a new application from established techniques. Expert Rev Precis Med Drug Dev. 2016;1(2):207–226.
  • Biomarkers in cancer: introductory guide for advocates. Research Advocacy Network. 2010.
  • Wei W, Liu Z, Rong Y, et al. A computed tomography-based radiomic prognostic marker of advanced high-grade serous ovarian cancer recurrence: a multicenter study. Front Oncol. 2019;9:255.
  • Shu Z, Fang S, Ding Z, et al. MRI-based radiomics nomogram to detect primary rectal cancer with synchronous liver metastases. Sci Rep. 2019;9(1):1–10.
  • Jiang Y, Chen C, Xie J, et al. Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer. EBioMedicine. 2018;36:171–182.
  • Davis AM, Bell RS, Goodwin PJ. Prognostic factors in osteosarcoma: a critical review. J Clin Oncol. 1994;12(2):423–431.
  • Lin P, Yang P-F, Chen S, et al. A Delta-radiomics model for preoperative evaluation of neoadjuvant chemotherapy response in high-grade osteosarcoma. Cancer Imag. 2020;20(1):7.
  • Drukker K, Edwards AV, Doyle C, et al. Breast MRI radiomics for the pretreatment prediction of response to neoadjuvant chemotherapy in node-positive breast cancer patients. J Med Imaging. 2019;6(3):034502.
  • Braman N, Prasanna P, Whitney J, et al. Association of peritumoral radiomics with tumor biology and pathologic response to preoperative targeted therapy for HER2 (ERBB2)– positive breast cancer. JAMA Netw Open. 2019;2(4):e192561–e192561.
  • Li Z, Zhang D, Dai Y, Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming 650118, China, et al. Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: a pilot study. Chin J Cancer Res. 2018;30(4):406–414.
  • Zhou J, Lu J, Gao C, et al. Predicting the response to neoadjuvant chemotherapy for breast cancer: Wavelet transforming radiomics in MRI. BMC Cancer. 2020;20(1):100.
  • Khalvati F, Zhang Y, Baig S, et al. Prognostic value of CT radiomic features in resectable pancreatic ductal adenocarcinoma. Sci Rep. 2019;9(1):1–9.
  • Wang J, Shen L, Zhong H, et al. Radiomics features on radiotherapy treatment planning CT can predict patient survival in locally advanced rectal cancer patients. Sci Rep. 2019;9(1):1–9.
  • Wang Q, Zhou S, Court LE, et al. Radiomics predicts clinical outcome in primary gastroesophageal junction adenocarcinoma treated by chemo/radiotherapy and surgery. Phys Imag Radiat Oncol. 2017;3:37–42.
  • Rabinovici-Cohen S, Tlusty T, Abutbul A, et al. Radiomics for predicting response to neoadjuvant chemotherapy treatment in breast cancer. In: Medical imaging 2020: imaging informatics for healthcare, research, and applications. Vol. 113181B. Houston (TX): International Society for Optics and Photonics; 2020.
  • Tian X, Sun C, Liu Z, et al. Prediction of response to preoperative neoadjuvant chemotherapy in locally advanced cervical cancer using multicenter CT-based radiomic analysis. Front Oncol. 2020;10:77.
  • Liu Z, Li Z, Qu J, et al. Radiomics of multiparametric MRI for pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study. Clin Cancer Res. 2019;25(12):3538–3547.
  • Jiang Y, Wang H, Wu J, et al. Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer. Ann Oncol. 2020;31(6):760–768.
  • Bourbonne V, Fournier G, Vallieres M, et al. External validation of an MRI-derived radiomics model to predict biochemical recurrence after surgery for high-risk prostate cancer. Cancers. 2020;12(4):814.
  • Pavic M, Bogowicz M, Kraft J, et al. FDG PET versus CT radiomics to predict outcome in malignant pleural mesothelioma patients. EJNMMI Res. 2020;10(1):1–8.
  • Spraker MB, Wootton LS, Hippe DS, et al. MRI radiomic features are independently associated with overall survival in soft tissue sarcoma. Adv Radiat Oncol. 2019;4(2):413–421.
  • Bani-Sadr A, Eker OF, Berner L-P, et al. Conventional MRI radiomics in patients with suspected early- or pseudo-progression . Neurooncol Adv. 2019;1(1):vdz01.
  • Wormald BW, Doran SJ, Ind TE, et al. Radiomic features of cervical cancer on T2-and diffusion-weighted MRI: prognostic value in low-volume tumors suitable for trachelectomy. Gynecologic Oncology. 2020;156(1):107–114.
  • Xie D, Wang T-T, Huang S-J, et al. Radiomics nomogram for prediction disease-free survival and adjuvant chemotherapy benefits in patients with resected stage I lung adenocarcinoma. Transl Lung Cancer Res. 2020;9(4):1112–1123.
  • Vaidya P, Bera K, Gupta A, et al. CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multicohort study for outcome prediction. The Lancet Digit Health. 2020;2(3):e116–e128.
  • Deng L, Yu D. Deep learning: Methods and applications. FNT Sig Process. 2014;7(3–4):197–387.
  • Kaissis G, Braren R. Pancreatic cancer detection and characterization—state of the art cross-sectional imaging and imaging data analysis. Transl Gastroenterol Hepatol. 2019;4:35–35.
  • Becker AS, Schneider MA, Wurnig MC, et al. Radiomics of liver MRI predict metastases in mice. Eur Radiol Exp. 2018;2(1):10–11.
  • Sun K-Y, Hu H-T, Chen S-L, et al. CT-based radiomics scores predict response to neoadjuvant chemotherapy and survival in patients with gastric cancer. BMC Cancer. 2020;20(1):1–11.
  • Conroy T, Lamfichekh N, Etienne P-L, et al. Total neoadjuvant therapy with mFOLFIRINOX versus preoperative chemoradiation in patients with locally advanced rectal cancer: final results of PRODIGE 23 phase III trial, a UNICANCER GI trial. 2020;4007.
  • Liu Z, Zhang X-Y, Shi Y-J, et al. Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res. 2017;23(23):7253–7262.
  • Coroller TP, Agrawal V, Narayan V, et al. Radiomic phenotype features predict pathological response in non-small cell lung cancer. Radiother Oncol. 2016;119(3):480–486.
  • Braman NM, Etesami M, Prasanna P, et al. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res. 2017;19(1):1–14.
  • Zheng J, Chakraborty J, Chapman WC, Research Staff in the Department of Surgery at Washington University School of Medicine, et al. Preoperative prediction of microvascular invasion in hepatocellular carcinoma using quantitative image analysis. J Am Coll Surg. 2017;225(6):778–788.
  • Attiyeh MA, Chakraborty J, Gazit L, et al. Preoperative risk prediction for intraductal papillary mucinous neoplasms by quantitative CT image analysis. HPB. 2019;21(2):212–218.
  • Polk SL, Choi JW, McGettigan MJ, et al. Multiphase computed tomography radiomics of pancreatic intraductal papillary mucinous neoplasms to predict malignancy. WJG. 2020;26(24):3458–3471.
  • Tobaly D, Santinha J, Sartoris R, et al. Ct-based radiomics analysis to predict malignancy in patients with intraductal papillary mucinous neoplasm (IPMN) of the pancreas. Cancers. 2020;12(11):3089.
  • Zhong Y, Yuan M, Zhang T, et al. Radiomics approach to prediction of occult mediastinal lymph node metastasis of lung adenocarcinoma. AJR Am J Roentgenol. 2018;211(1):109–113.
  • Huang Y-Q, Liang C-H, He L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol. 2016;34(18):2157–2164.
  • Vos M, Starmans M, Timbergen M, et al. Radiomics approach to distinguish between well differentiated liposarcomas and lipomas on MRI. Br J Surg. 2019;106(13):1800–1809.
  • Wu G, Woodruff HC, Sanduleanu S, et al. Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study. Eur Radiol. 2020;30(5):2612–2680.
  • Van Tienhoven G, Versteijne E, Suker M, et al. Preoperative chemoradiotherapy versus immediate surgery for resectable and borderline resectable pancreatic cancer (PREOPANC1): a randomized, controlled, multicenter phase III trial. ASCO. 2018;LBA4002-LBA4002.
  • Yang H, Liu H, Chen Y, on behalf of the AME Thoracic Surgery Collaborative Group, et al. Neoadjuvant chemoradiotherapy followed by surgery versus surgery alone for locally advanced squamous cell carcinoma of the esophagus (NEOCRTEC5010): a phase III multicenter, randomized, open-label clinical trial. J Clin Oncol. 2018;36(27):2796–2803.
  • Overdevest JB, Theodorescu D, Lee JK. Utilizing the molecular gateway: the path to personalized cancer management. Clin Chem. 2009;55(4):684–697.
  • Liu Z, Wang S, Di Dong JW, et al. The applications of radiomics in precision diagnosis and treatment of oncology: Opportunities and challenges. Theranostics. 2019;9(5):1303–1322.
  • Lambin P, Leijenaar RT, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749–762.
  • Crivelli P, Ledda RE, Parascandolo N, et al. A new challenge for radiologists: radiomics in breast cancer. Biomed Res Int. 2018;2018:6120703.
  • Qiu Q, Duan J, Yin Y. Radiomics in radiotherapy: applications and future challenges. Prec Radiat Oncol. 2020;4(1):29–33.
  • Rieke N, Hancox J, Li W, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3(1):1–7.
  • Kaissis GA, Makowski MR, Ruckert D, et al. Secure, privacy-preserving and federated machine learning in medical imaging. Nat Mach Intell. 2020;2(6):305–311.
  • Zwanenburg A, Vallieres M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295(2):328–338.
  • Collins GS, Reitsma JB, Altman DG, TRIPOD Group, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. The TRIPOD Group. Circulation. 2015;131(2):211–219.