Multisystem Imaging Recommendations/Guidelines: In the Pursuit of Precision Oncology

Abstract

With an increasing rate of cancers in almost all age groups and advanced screening techniques leading to an early diagnosis and longer longevity of patients with cancers, it is of utmost importance that radiologists assigned with cancer imaging should be prepared to deal with specific expected and unexpected circumstances that may arise during the lifetime of these patients. Tailored integration of preventive and curative interventions with current health plans and global escalation of efforts for timely diagnosis of cancers will pave the path for a cancer-free world. The commonly encountered circumstances in the current era, complicating cancer imaging, include coronavirus disease 2019 infection, pregnancy and lactation, immunocompromised states, bone marrow transplant, and screening of cancers in the relevant population. In this article, we discuss the imaging recommendations pertaining to cancer screening and diagnosis in the aforementioned clinical circumstances.

Keywords

AI - BMT - CI-RADS - COIN - COVID-19 - Immunocompromised - iTNM - oncology - Lactation - pregnancy

Note

The article is not under consideration for publication elsewhere. Each author participated sufficiently for the work to be submitted. Publication is approved by all authors.

Publication History

Article published online:
06 March 2023

• References

• 1 Sung H, Ferlay J, Siegel RL. et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71 (03) 209-249
• 2 Miles K. Can imaging help improve the survival of cancer patients?. Cancer Imaging 2011; 11 Spec No A (1A): S86-S92
• 3 Ward ZJ, Scott AM, Hricak H. et al. Estimating the impact of treatment and imaging modalities on 5-year net survival of 11 cancers in 200 countries: a simulation-based analysis. Lancet Oncol 2020; 21 (08) 1077-1088
• 4 Timmermans S, Mauck A. The promises and pitfalls of evidence-based medicine. Health Aff (Millwood) 2005; 24 (01) 18-28
• 5 Gdeedo A, Van Schil P, Corthouts B, Van Mieghem F, Van Meerbeeck J, Van Marck E. Comparison of imaging TNM [(i)TNM] and pathological TNM [pTNM] in staging of bronchogenic carcinoma. Eur J Cardiothorac Surg 1997; 12 (02) 224-227
• 6 Mahajan A, Suthar M, Agarwal U, Shukla S, Thiagarajan S, Sable N. Imaging-based T stage (iT) as a predictive and prognostic marker for outcome in T4 stage tongue carcinomas: a narrative review. Cancer Res Stat Treat 2021; 4 (04) 677-683
• 7 Frommhold J, Jocham D, Doehn C. Wie gut ist die Korrelation zwischen klinischen und pathologischen TNM-Stadien bei Nierentumoren?. [How accurate is the correlation between clinical and pathological TNM stages in rena l tumours?] Aktuelle Urol 2011;42(4):247–251. German. doi: 10.1055/s-0031-1271392. Epub 2011 Jun 29
• 8 An JY, Unsdorfer KML, Weinreb JC. BI-RADS, C-RADS, CAD-RADS, LI-RADS, Lung-RADS, NI-RADS, O-RADS, PI-RADS, TI-RADS: Reporting and Data Systems. Radiographics 2019; 39 (05) 1435-1436
• 9 Accessed December 30, 2022 at: https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/Bi-Rads
• 10 Mahajan A. Synoptic reporting in lung cancers using Lung Cancer Reporting and Data System (LC-RADS): the road ahead for standardization of imaging in lung cancer staging. Cancer Res Stat Treat 2021; 4 (01) 61-66
• 11 Mahajan A, Agarwal U, Padashetty S. et al. A narrative review of the role of cross-sectional imaging in the management of thyroid carcinoma: imaging guidelines and T-CIRADS. Cancer Research, Statistics, and Treatment 2022; 5 (03) 490-498
• 12 Mahajan A, Agarwal U, Gupta A. et al. Synoptic reporting in head and neck cancers—Head and Neck Cancer Imaging Reporting and Data Systems (HN-CIRADS): the journey ahead for standardization of imaging in head and neck cancer staging. Cancer Res Stat Treat 2022; 5 (02) 322-330
• 13 Ayer T, Chen Q, Burnside ES. Artificial neural networks in mammography interpretation and diagnostic decision making. Comput Math Methods Med 2013; 2013: 832509
• 14 Steyerberg EW. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. New York: Springer; 2009
• 15 Carrino JA, Ohno-Machado L. Development of radiology prediction models using feature analysis. Acad Radiol 2005; 12 (04) 415-421
• 16 European Society of Radiology (ESR). ESR position paper on imaging biobanks. Insights Imaging 2015; 6 (04) 403-410
• 17 Vaidya T, Agrawal A, Mahajan S, Thakur MH, Mahajan A. The continuing evolution of molecular functional imaging in clinical oncology: the road to precision medicine and radiogenomics (Part II). Mol Diagn Ther 2019; 23 (01) 27-51 Erratum in: Mol Diagn Ther. 2018 Nov 20; PMID: 30387041
• 18 Miller AB, Hoogstraten B, Staquet M, Winkler A. Reporting results of cancer treatment. Cancer 1981; 47 (01) 207-214
• 19 Therasse P, Arbuck SG, Eisenhauer EA. et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst 2000; 92 (03) 205-216
• 20 Kim JH. Comparison of the EORTC criteria and PERCIST in solid tumors: a pooled analysis and review. Oncotarget 2016; 7 (36) 58105-58110
• 21 Mahajan A, Cook G. Clinical applications of PET/CT in oncology. In: Basic Science of PET Imaging; 2017: 429
• 22 Mahajan A, Cook G. Physiologic and molecular basis of PET in cancer imaging. In: Basic Science of PET Imaging; 2017: 399-427
• 23 Ko CC, Yeh LR, Kuo YT, Chen JH. Imaging biomarkers for evaluating tumor response: RECIST and beyond. Biomark Res 2021; 9 (01) 52
• 24 Somarouthu B, Lee SI, Urban T, Sadow CA, Harris GJ, Kambadakone A. Immune-related tumour response assessment criteria: a comprehensive review. Br J Radiol 2018; 91 (1084): 20170457
• 25 Choi H, Charnsangavej C, Faria SC. et al. Correlation of computed tomography and positron emission tomography in patients with metastatic gastrointestinal stromal tumor treated at a single institution with imatinib mesylate: proposal of new computed tomography response criteria. J Clin Oncol 2007; 25 (13) 1753-1759
• 26 Lencioni R, Llovet JM. Modified RECIST (mRECIST) assessment for hepatocellular carcinoma. Semin Liver Dis 2010; 30 (01) 52-60
• 27 Wahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nucl Med 2009; 50 (Suppl. 01) 122S-150S
• 28 Chinot OL, Macdonald DR, Abrey LE, Zahlmann G, Kerloëguen Y, Cloughesy TF. Response assessment criteria for glioblastoma: practical adaptation and implementation in clinical trials of antiangiogenic therapy. Curr Neurol Neurosci Rep 2013; 13 (05) 347
• 29 Cheson BD, Pfistner B, Juweid ME. et al; International Harmonization Project on Lymphoma. Revised response criteria for malignant lymphoma. J Clin Oncol 2007; 25 (05) 579-586
• 30 Abdel-Sattar MH, Abdelaziz O, Othman AO, El-Refaei SM. The use of Deauville criteria in follow-up assessment of response to therapy in extra-nodal Non-Hodgkin's lymphoma. Egypt J Radiol Nucl Med 2018; 49 (01) 209-215
• 31 Nielsen N, Nielsen A, Gerke O, Juul-Jensen K, Larsen T, Hildebrandt M. A head-to-head comparison of the Lugano Classification and PERCIST for FDG-PET/CT response assessment in diffuse large B-cell lymphoma. J Nucl Med 2021; 62 (Suppl 1): 1688
• 32 Costelloe CM, Chuang HH, Madewell JE, Ueno NT. Cancer response criteria and bone metastases: RECIST 1.1, MDA and PERCIST. J Cancer 2010; 1: 80-92
• 33 Aykan NF, Özatlı T. Objective response rate assessment in oncology: current situation and future expectations. World J Clin Oncol 2020; 11 (02) 53-73
• 34 Tirkes T, Hollar MA, Tann M, Kohli MD, Akisik F, Sandrasegaran K. Response criteria in oncologic imaging: review of traditional and new criteria. Radiographics 2013; 33 (05) 1323-1341
• 35 Ranganathan P, Sengar M, Chinnaswamy G. et al; National Cancer Grid of India. Impact of COVID-19 on cancer care in India: a cohort study. Lancet Oncol 2021; 22 (07) 970-976
• 36 Richards M, Anderson M, Carter P, Ebert BL, Mossialos E. The impact of the COVID-19 pandemic on cancer care. Nat Can 2020; 1 (06) 565-567
• 37 Johannesen TB, Smeland S, Aaserud S. et al. COVID-19 in cancer patients, risk factors for disease and adverse outcome, a population-based study from Norway. Front Oncol 2021; 11: 652535
• 38 Haghighat S, Dehghani M. COVID-19 in immunosuppressed and cancer patients: a review. Asian Pacific J Cancer Care 2020; 5 (S1): 69-73
• 39 Fillmore NR, La J, Szalat RE. et al. Prevalence and outcome of COVID-19 infection in cancer patients: A National Veterans Affairs Study. J Natl Cancer Inst 2021; 113 (06) 691-698
• 40 Aljondi R, Alghamdi S. Diagnostic value of imaging modalities for COVID-19: scoping review. J Med Internet Res 2020; 22 (08) e19673
• 41 Cleverley J, Piper J, Jones MM. The role of chest radiography in confirming covid-19 pneumonia. BMJ 2020; 370: m2426
• 42 Nnaji CA, Moodley J. Impact of the COVID-19 pandemic on cancer diagnosis, treatment and research in African health systems: a review of current evidence and contextual perspectives. Ecancermedicalscience 2021; 15: 1170
• 43 Kulkarni T, Sharma P, Pande P, Agrawal R, Rane S, Mahajan A. COVID-19: a review of protective measures. Cancer Res, Statistics Treat 2020; 3 (02) 244-253
• 44 Curigliano G, Banerjee S, Cervantes A. et al; Panel Members. Managing cancer patients during the COVID-19 pandemic: an ESMO multidisciplinary expert consensus. Ann Oncol 2020; 31 (10) 1320-1335
• 45 Boettcher AN, Hammoud DA, Weinberg JB, Agarwal P, Mendiratta-Lala M, Luker GD. Cancer imaging and patient care during the COVID-19 pandemic. Radiol Imaging Cancer 2020; 2 (06) e200058
• 46 McCollough CH, Schueler BA, Atwell TD. et al. Radiation exposure and pregnancy: when should we be concerned?. Radiographics 2007; 27 (04) 909-917 , discussion 917–918
• 47 Patel SJ, Reede DL, Katz DS, Subramaniam R, Amorosa JK. Imaging the pregnant patient for nonobstetric conditions: algorithms and radiation dose considerations. Radiographics 2007; 27 (06) 1705-1722
• 48 American Institute of Ultrasound in Medicine. Statement on the safe use of Doppler ultrasound during 11–14 week scans (or earlier in pregnancy). Laurel, MD: AIUM; 2011. Accessed December 30, 2022 at: http://www.aium.org/officialStatements/42
• 49 Chen MM, Coakley FV, Kaimal A, Laros Jr RK. Guidelines for computed tomography and magnetic resonance imaging use during pregnancy and lactation. Obstet Gynecol 2008; 112 (2 Pt 1): 333-340
• 50 Leyendecker JR, Gorengaut V, Brown JJ. MR imaging of maternal diseases of the abdomen and pelvis during pregnancy and the immediate postpartum period. Radiographics 2004; 24 (05) 1301-1316
• 51 Kanal E, Barkovich AJ, Bell C. et al; Expert Panel on MR Safety. ACR guidance document on MR safe practices: 2013. J Magn Reson Imaging 2013; 37 (03) 501-530
• 52 De Santis M, Straface G, Cavaliere AF, Carducci B, Caruso A. Gadolinium periconceptional exposure: pregnancy and neonatal outcome. Acta Obstet Gynecol Scand 2007; 86 (01) 99-101
• 53 Ray JG, Vermeulen MJ, Bharatha A, Montanera WJ, Park AL. Association between MRI exposure during pregnancy and fetal and childhood outcomes. JAMA 2016; 316 (09) 952-961
• 54 Sachs HC. Committee On Drugs. The transfer of drugs and therapeutics into human breast milk: an update on selected topics. Pediatrics 2013; 132 (03) e796-e809
• 55 De Santis M, Cesari E, Nobili E, Straface G, Cavaliere AF, Caruso A. Radiation effects on development. Birth Defects Res C Embryo Today 2007; 81 (03) 177-182
• 56 Brent RL. Saving lives and changing family histories: appropriate counseling of pregnant women and men and women of reproductive age, concerning the risk of diagnostic radiation exposures during and before pregnancy. Am J Obstet Gynecol 2009; 200 (01) 4-24
• 57 Wagner LK, Applegate K. American College of Radiology. ACR practice guideline for imaging pregnant or potentially pregnant adolescents and women with ionizing radiation. American College of Radiology Practice guidelines & technical standards. 2008
• 58 Ratnapalan S, Bona N, Chandra K, Koren G. Physicians' perceptions of teratogenic risk associated with radiography and CT during early pregnancy. AJR Am J Roentgenol 2004; 182 (05) 1107-1109
• 59 Doing WC, Do WY. Radiation Exposure and Pregnancy, Health Physics Society Fact Sheet https://hps.org/documents/pregnancy_fact_sheet.pdf
• 60 Wang PI, Chong ST, Kielar AZ. et al. Imaging of pregnant and lactating patients: part 1, evidence-based review and recommendations. AJR Am J Roentgenol 2012; 198 (04) 778-784
• 61 Valentin J. Chapter 3. Effects of in utero irradiation. Ann ICRP 2000; 30 (01) 9-12
• 62 National Research Council. Health Risks from Exposure to Low Levels of Ionizing Radiation: BEIR VII Phase 2. Washington, DC: The National Academies Press;
• 63 American College of Obstetricians and Gynecologists. Guidelines for diagnostic imaging during pregnancy. ACOG Comm Opin 1995; (Sep): 158
• 64 Khaddour K, Hana CK, Mewawalla P. Hematopoietic Stem Cell Transplantation. Treasure Island, FL: StatPearls Publishing; InStatPearls [internet] 2021 Jul 25.
• 65 Saad A, de Lima M, Anand S. et al. Hematopoietic cell transplantation, version 2.2020, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw 2020; 18 (05) 599-634
• 66 D'Souza A, Pasquini M, Spellecy R. Is 'informed consent' an 'understood consent' in hematopoietic cell transplantation?. Bone Marrow Transplant 2015; 50 (01) 10-14
• 67 Pandey T, Maximin S, Bhargava P. Imaging of complications from hematopoietic stem cell transplant. Indian J Radiol Imaging 2014; 24 (04) 327-338
• 68 Aljurf M, Snowden JA, Hayden P, Orchard KH, McGrath E. Quality Management and Accreditation in Hematopoietic Stem Cell Transplantation and Cellular Therapy: The JACIE Guide. Springer Nature; 2021
• 69 Maxim LD, Niebo R, Utell MJ. Screening tests: a review with examples. Inhal Toxicol 2014; 26 (13) 811-828
• 70 Kopans DB, Monsees B, Smith R, Feig S. Ten criteria for effective screening. AJR Am J Roentgenol 2002; 178 (02) 508-510
• 71 Croswell JM, Ransohoff DF, Kramer BS. Principles of cancer screening: lessons from history and study design issues. Semin Oncol 2010; 37 (03) 202-215 WB Saunders
• 72 Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer 2010; 127 (12) 2893-2917
• 73 Bashar MA, Aggarwal AK. Organizing a cancer screening camp in low-resource settings: experience from North India. Indian J Med Paed Oncol 2019; 40 (S01): S194-S195
• 74 Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018; 68 (06) 394-424
• 75 Takkar N, Kochhar S, Garg P, Pandey AK, Dalal UR, Handa U. Screening methods (clinical breast examination and mammography) to detect breast cancer in women aged 40-49 years. J Midlife Health 2017; 8 (01) 2-10
• 76 Gradishar WJ, Anderson BO, Abraham J. et al. Breast cancer, version 3.2020, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw 2020; 18 (04) 452-478
• 77 Siu AL, Bibbins-Domingo K, Grossman DC, LeFevre ML. U.S. Preventive Services Task Force. Convergence and divergence around breast cancer screening. Ann Intern Med 2016; 164 (04) 301-302
• 78 Qaseem A, Lin JS, Mustafa RA. et al; Clinical Guidelines Committee of the American College of Physicians. Screening for breast cancer in average-risk women: a guidance statement from the American College of Physicians. Ann Intern Med 2019; 170 (08) 547-560
• 79 Marmot MG, Altman DG, Cameron DA, Dewar JA, Thompson SG, Wilcox M. The benefits and harms of breast cancer screening: an independent review. Br J Cancer 2013; 108 (11) 2205-2240
• 80 Murthy NS, Chaudhry K, Nadayil D, Agarwal UK, Saxena S. Changing trends in incidence of breast cancer: Indian scenario. Indian J Cancer 2009; 46 (01) 73-74
• 81 Keegan N, Goldgar C, Keahey D. Colorectal cancer and computed tomography colonography: a new screening option?. J Physician Assist Educ 2010; 21 (01) 35-42
• 82 Singh S, Shrivastava JP, Dwivedi A. Breast cancer screening existence in India: A nonexisting reality. Indian J Med Paediatr Oncol 2015; 36 (04) 207-209
• 83 Shulman LN, Willett W, Sievers A, Knaul FM. Breast cancer in developing countries: opportunities for improved survival. J Oncol 2010; 2010: 595167 . Epub 2010 Dec 29. PMID: 21253541; PMCID: PMC3021855
• 84 Bleyer A, Welch HG. Effect of three decades of screening mammography on breast-cancer incidence. N Engl J Med 2012; 367 (21) 1998-2005
• 85 Badwe RA, Gupta S. Mammographic screening for breast cancer: Are the chickens coming home to roost?. South Asian J Cancer 2013; 2 (01) 1-2
• 86 Gupta A, Shridhar K, Dhillon PK. A review of breast cancer awareness among women in India: Cancer literate or awareness deficit?. Eur J Cancer 2015; 51 (14) 2058-2066
• 87 Mittra I, Mishra GA, Singh S. et al. A cluster randomized, controlled trial of breast and cervix cancer screening in Mumbai, India: methodology and interim results after three rounds of screening. Int J Cancer 2010; 126 (04) 976-984
• 88 Bluekens AM, Holland R, Karssemeijer N, Broeders MJ, den Heeten GJ. Comparison of digital screening mammography and screen-film mammography in the early detection of clinically relevant cancers: a multicenter study. Radiology 2012; 265 (03) 707-714
• 89 Mercado CL. BI-RADS update. Radiol Clin North Am 2014; 52 (03) 481-487
• 90 D'Orsi CJ. Imaging for the diagnosis and management of ductal carcinoma in situ. J Natl Cancer Inst Monogr 2010; 2010 (41) 214-217
• 91 Uematsu T. The emerging role of breast tomosynthesis. Breast Cancer 2013; 20 (03) 204-212
• 92 Berg WA, Blume JD, Cormack JB. et al; ACRIN 6666 Investigators. Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer. JAMA 2008; 299 (18) 2151-2163
• 93 Berg WA, Bandos AI, Mendelson EB, Lehrer D, Jong RA, Pisano ED. Ultrasound as the primary screening test for breast cancer: analysis from ACRIN 6666. J Natl Cancer Inst 2015; 108 (04) djv367
• 94 Agrawal A, Tripathi P, Sahu A, Daftary J. Breast screening revisited. J Family Med Prim Care 2014; 3 (04) 340-344
• 95 Saadatmand S, Geuzinge HA, Rutgers EJT. et al; FaMRIsc study group. MRI versus mammography for breast cancer screening in women with familial risk (FaMRIsc): a multicentre, randomised, controlled trial. Lancet Oncol 2019; 20 (08) 1136-1147
• 96 Torre LA, Siegel RL, Jemal A. Lung cancer statistics. Adv Exp Med Biol 2016; 893: 1-19
• 97 Behera D, Balamugesh T. Lung cancer in India. Indian J Chest Dis Allied Sci 2004; 46 (04) 269-281
• 98 Moyer VA. U.S. Preventive Services Task Force. Screening for lung cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 2014; 160 (05) 330-338
• 99 Humphrey LL, Deffebach M, Pappas M. et al. Screening for lung cancer with low-dose computed tomography: a systematic review to update the US Preventive services task force recommendation. Ann Intern Med 2013; 159 (06) 411-420
• 100 Wood DE, Kazerooni EA, Baum SL. et al. Lung cancer screening, version 3.2018, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw 2018; 16 (04) 412-441
• 101 Issa IA, Noureddine M. Colorectal cancer screening: an updated review of the available options. World J Gastroenterol 2017; 23 (28) 5086-5096
• 102 Li D. Recent advances in colorectal cancer screening. Chronic Dis Transl Med 2018; 4 (03) 139-147
• 103 Burt RW, Barthel JS, Dunn KB. et al; NCCN. NCCN clinical practice guidelines in oncology. Colorectal cancer screening. J Natl Compr Canc Netw 2010; 8 (01) 8-61
• 104 Chan CK, Aimagambetova G, Ukybassova T, Kongrtay K, Azizan A. Human papillomavirus infection and cervical cancer: epidemiology, screening, and vaccination—review of current perspectives. J Oncol 2019; 2019: 3257939
• 105 Walboomers JM, Jacobs MV, Manos MM. et al. Human papillomavirus is a necessary cause of invasive cervical cancer worldwide. J Pathol 1999; 189 (01) 12-19
• 106 Saslow D, Solomon D, Lawson HW. et al; American Cancer Society, American Society for Colposcopy and Cervical Pathology, American Society for Clinical Pathology. American Cancer Society, American Society for Colposcopy and Cervical Pathology, and American Society for Clinical Pathology screening guidelines for the prevention and early detection of cervical cancer. Am J Clin Pathol 2012; 137 (04) 516-542
• 107 Srivastava AN, Misra JS, Srivastava S, Das BC, Gupta S. Cervical cancer screening in rural India: status and current concepts. Indian J Med Res 2018; 148 (06) 687-696
• 108 Rawla P. Epidemiology of prostate cancer. World J Oncol 2019; 10 (02) 63-89
• 109 Mohler JL, Antonarakis ES, Armstrong AJ. et al. Prostate cancer, version 2.2019, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw 2019; 17 (05) 479-505
• 110 Daly MB, Pilarski R, Yurgelun MB. et al. NCCN guidelines insights: genetic/familial high-risk assessment: breast, ovarian, and pancreatic, version 1.2020. J Natl Compr Canc Netw 2020; 18 (04) 380-391
• 111 Mahajan A, Vaidya T, Gupta A, Rane S, Gupta S. Artificial intelligence in healthcare in developing nations: the beginning of a transformative journey. Cancer Res, Stat Treat 2019; 2 (02) 182-189
• 112 Bothra M, Mahajan A. Mining artificial intelligence in oncology: Tata Memorial Hospital journey. Cancer Res Stat Treat 2020; 3: 622-624
• 113 Shur JD, Doran SJ, Kumar S. et al. Radiomics in oncology: a practical guide. Radiographics 2021; 41 (06) 1717-1732
• 114 Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med 2021; 13 (01) 152
• 115 Montagnon E, Cerny M, Cadrin-Chênevert A. et al. Deep learning workflow in radiology: a primer. Insights Imaging 2020; 11 (01) 22
• 116 Tseng H-H, Wei L, Cui S, Luo Y, Ten Haken RK, El Naqa I. Machine learning and imaging informatics in oncology. Oncology 2020; 98 (06) 344-362
• 117 Choy G, Khalilzadeh O, Michalski M. et al. Current applications and future impact of machine learning in radiology. Radiology 2018; 288 (02) 318-328
• 118 Wang S, Summers RM. Machine learning and radiology. Med Image Anal 2012; 16 (05) 933-951
• 119 Cheng PM, Montagnon E, Yamashita R. et al. Deep learning: an update for radiologists. Radiographics 2021; 41 (05) 1427-1445
• 120 Rieke N, Hancox J, Li W. et al. The future of digital health with federated learning. NPJ Digit Med 2020; 3 (01) 119
• 121 Accessed March 26, 2022 at: https://www.javatpoint.com/machine-learning-vs-deep-learning
• 122 Sapate S, Talbar S, Mahajan A, Sable N, Desai S, Thakur M. Breast cancer diagnosis using abnormalities on ipsilateral views of digital mammograms. Biocybern Biomed Eng 2020; 40 (01) 290-305
• 123 Sapate SG, Mahajan A, Talbar SN, Sable N, Desai S, Thakur M. Radiomics based detection and characterization of suspicious lesions on full field digital mammograms. Comput Meth Prog Biomed 2018; 163: 1-20
• 124 Baid U, Ghodasara S, Mohan S. et al. The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314. 2021 Jul 5.
• 125 Baid U, Talbar S, Rane S. et al. A novel approach for fully automatic intra-tumor segmentation with 3D U-net architecture for gliomas. Front Comput Neurosci 2020; 14: 10
• 126 Mehta R, Filos A, Baid U, Sako C, McKinley R, Rebsamen M, Dätwyler K, Meier R, Radojewski P, Krishnan Murugesan G, Nalawade S. QU-BraTS: MICCAI BraTS 2020 challenge on quantifying uncertainty in brain tumor segmentation–analysis of ranking metrics and benchmarking results. arXiv e-prints. 2021 Dec:arXiv-2112.
• 127 Pati S, Baid U, Zenk M. et al. The federated tumor segmentation (FETS) challenge. arXiv preprint arXiv:2105.05874. 2021 May 12.
• 128 Singadkar G, Mahajan A, Thakur M, Talbar S. Deep deconvolutional residual network based automatic lung nodule segmentation. J Digit Imaging 2020; 33 (03) 678-684
• 129 Singadkar G, Mahajan A, Thakur M, Talbar S. Automatic lung segmentation for the inclusion of juxtapleural nodules and pulmonary vessels using curvature based border correction. J King Saud Univ-Comput Informat Sci 2021; 33 (08) 975-987
• 130 Kumar YR, Muthukrishnan NM, Mahajan A. et al. Statistical parameter-based automatic liver tumor segmentation from abdominal CT scans: a potential radiomic signature. Procedia Comput Sci 2016; 93: 446-452
• 131 Rela M, Krishnaveni BV, Kumar P, Lakshminarayana G. Computerized segmentation of liver tumor using integrated fuzzy level set method. AIP Conf Proc 2021; 2358 (01) 60001
• 132 Hambarde P, Talbar SN, Sable N, Mahajan A, Chavan SS, Thakur M. Radiomics for peripheral zone and intra-prostatic urethra segmentation in MR imaging. Biomed Signal Process Control 2019; 51: 19-29
• 133 Davatzikos C, Barnholtz-Sloan JS, Bakas S. et al. AI-based prognostic imaging biomarkers for precision neuro-oncology: the ReSPOND consortium. Neuro-oncol 2020; 22 (06) 886-888
• 134 Bakas S, Reyes M, Jakab A. et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint arXiv:1811.02629. 2018 Nov 5.
• 135 Baid U, Rane SU, Talbar S. et al. Overall survival prediction in glioblastoma with radiomic features using machine learning. Front Comput Neurosci 2020; 14: 61
• 136 Akbari H, Mohan S, Garcia JA. et al. Prediction of glioblastoma cellular infiltration and recurrence using machine learning and multi-parametric MRI analysis: results from the multi-institutional respond consortium. Neuro-oncol 2021; 23 (Supplement_6): vi132-vi133
• 137 Mantarro A, Scalise P, Neri E. Imaging biobanks, big data, and population-based imaging biomarkers. In: Imaging Biomarkers: Development and Clinical Integration. 2017: 153-157
• 138 Tang A, Tam R, Cadrin-Chênevert A. et al; Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group. Canadian Association of Radiologists white paper on artificial intelligence in radiology. Can Assoc Radiol J 2018; 69 (02) 120-135
• 139 van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-“how-to” guide and critical reflection. Insights Imaging 2020; 11 (01) 91
• 140 Rizzo S, Botta F, Raimondi S. et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp 2018; 2 (01) 36
• 141 Vial A, Stirling D, Field M. et al. The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review. Translational Cancer Research 2018; 7 (03) 803-816
• 142 Geis JR, Brady A, Wu CC. et al. Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Insights Imaging 2019; 10 (01) 101
• 143 Accessed October 20, 2022 at: https://www.cancerimagingarchive.net/
• 144 Accessed October 20, 2022 at: https://www.rcr.ac.uk/sites/default/files/abstract_posters/a_white_paper_on_artificial_intelligence_in_radiology_getting_over_the_hype.pdf
• 145 Bossuyt PM, Reitsma JB, Bruns DE. et al; STARD Group. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. Clin Chem 2015; 61 (12) 1446-1452
• 146 Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Br J Surg 2015; 102 (03) 148-158
• 147 Last accessed October 20, 2022 at: https://www.rcr.ac.uk/posts/rcr-position-statement-artificial-intelligence
• 148 Lambin P, Leijenaar RTH, Deist TM. et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017; 14 (12) 749-762
• 149 Accessed October 10, 2022 at: https://www.niti.gov.in/sites/default/files/2021-02/Responsible-AI-22022021.pdf
• 150 Akinci D'Antonoli T. Ethical considerations for artificial intelligence: an overview of the current radiology landscape. Diagn Interv Radiol 2020; 26 (05) 504-511
• 151 Kourou K, Exarchos KP, Papaloukas C, Sakaloglou P, Exarchos T, Fotiadis DI. Applied machine learning in cancer research: a systematic review for patient diagnosis, classification and prognosis. Comput Struct Biotechnol J 2021; 19: 5546-5555
• 152 Kumar Y, Gupta S, Singla R, Hu Y-C. A systematic review of artificial intelligence techniques in cancer prediction and diagnosis. Arch Comput Methods Eng 2022; 29 (04) 2043-2070
• 153 Accessed October 20, 2022 at: https://car.ca/news/new-car-white-paper-on-ai-provides-guidance-on-de-identification-of-medical-imaging-data/