Table 1.

Potential utility of artificial intelligence in imaging in cardio-oncology.

Imaging in cardio-oncology	Utility before cancer treatment	Utility during cancer treatment	Utility after cancer treatment
Echocardiography	Establishment of baseline cardiac assessment using automated LVEF and GLS measurements. Predicting CV outcomes with ML algorithms to guide decision-making. AI-guided echo acquisition can expand the use of echo to primary care and oncology settings	Follow-up cardiac assessment using automated LVEF and GLS measurements to predict CV outcomes with ML algorithms to guide decision-making; AI-guided echo acquisition can expand the use of echo to primary care, oncology, and other settings	Follow-up cardiac assessment using automated LVEF and GLS measurements to predict CV outcomes using ML algorithms to guide decision-making; AI-guided echo acquisition can expand the use of echo to primary care, oncology, and other settings
	AI can facilitate the detection of subtle abnormalities in TTE that may not be visually seen by an interpreting cardiologist to improve prognostic/diagnostic accuracy	AI can facilitate the detection of subtle changes in TTE that may not be visually seen by an interpreting cardiologist to improve prognostic/diagnostic accuracy	AI can facilitate the detection of subtle changes in TTE that may not be visually seen by an interpreting cardiologist to improve prognostic/diagnostic accuracy
Cardiovascular magnetic resonance imaging	AI approaches applied to CMR can facilitate efficient diagnostic performance for cardiac amyloidosis, simulating CMR reading by experienced operators	AI approaches applied to CMR can facilitate efficient diagnostic performance for cardiac amyloidosis, simulating CMR reading by experienced operators	AI approaches applied to CMR can facilitate efficient diagnostic performance for cardiac amyloidosis, simulating CMR reading by experienced operators
	Successful application of AI to CMR tissue characterization using radiomics and texture analysis can improve prognostic and diagnostic accuracy of subtle abnormalities in the myocardium	Application of AI to CMR to address etiological concerns can be key to identifying cardiovascular toxicity and can be crucial to inform decisions to cease or continue cancer therapy or initiate immunosuppression	Successful application of AI to CMR tissue characterization using radiomics and texture analysis can improve diagnostic accuracy of imaging scar, wall thickening differentiation, and inflammation
Computed tomography (CAC)	Chest CT obtained for planning cancer treatments can be automated to assess CAC which is a robust target for primary cardiovascular risk reduction	Chest CT previously obtained for planning cancer treatments can be automated to assess CAC which is a robust target for primary cardiovascular risk reduction	Chest CT previously obtained for planning cancer treatments can be automated to assess CAC which is a robust target for primary cardiovascular risk reduction
	Cancer surveillance chest CT can be automated to assess CAC which is a robust target for cardiovascular risk reduction	Cancer surveillance chest CT can be automated to assess CAC which is a robust target for cardiovascular risk reduction	Cancer surveillance chest CT can be automated to assess CAC which is a robust target for cardiovascular risk reduction
Single proton emission computed tomographya	ML algorithms can be applied to SPECT to provide additional neutrality (supplementing subjective assessments by reading clinicians) in processing data relating to myocardial perfusion	ML algorithms can be applied to SPECT to provide additional neutrality (supplementing subjective assessments by reading clinicians) in processing data relating to incident myocardial perfusion	ML algorithms can be applied to SPECT to provide additional neutrality (supplementing subjective assessments by reading clinicians) in processing data relating to evolving or incident myocardial perfusion
	Combining ML algorithms with SPECT can improve prediction accuracy in the determination of baseline cardiac abnormalities for high-risk patients	Combining ML algorithms with SPECT can improve prediction accuracy in the determination of short-term adverse cardiac effects especially for high-risk patients	Combining ML algorithms with SPECT can improve prediction accuracy in the determination of long-term adverse cardiac effects especially for high-risk patients
Positron emission tomographya	MACE and myocardial ischemia can be challenging to predict and might gain from ML to clarify baseline risk assessment	MACE and myocardial ischemia can be challenging to predict and might gain from ML to clarify evolving cardiac injury and ongoing prognosis	Using ML algorithms in conjunction with cardiac PET can augment the detection of damage to coronary arteries post-radiation
	–	AI can automate PET scan assessment of new inflammation resulting from cancer immunotherapy	AI can automate PET scan assessment of persistent inflammation resulting from cancer immunotherapy
Multimodality imaging	Automation of detection and characterization, including analysis of size, shape, and textural patterns, of tumors can define and refine the diagnosis through incorporation of data from CT, MRI, FDG-PET and large image databases	Monitoring response to treatment by tracking size and texture of tumors, and presence of any additional tumors, can be automated, with incorporation of data from CT, MRI, FDG-PET and large image databases	Post-treatment monitoring can be automated for surveillance of size, texture, and presence of recurrent or additional tumors, through incorporation of data from CT, MRI, FDG-PET and large image databases
	Incorporation of AI algorithms can help determine prognosis and treatment of masses in or near the heart	Incorporation of AI algorithms can help optimize prognosis and treatment of masses in or near the heart	Incorporation of AI algorithms can help optimize prognosis and treatment of masses in or near the heart

AI = artificial intelligence; CAC = coronary artery calcification; CMR = cardiac magnetic resonance; CT = computed tomography; CV = cardiovascular; CVD = cardiovascular disease; FDG-PET = Fluorodeoxyglucose (FDG)-positron emission tomography; GLS = global longitudinal strain; LVEF = left ventricular ejection fraction; MACE = major adverse cardiovascular events; ML = machine learning; MRI = magnetic resonance imaging; PET = positron emission tomography; SPECT = single-photon emission computerized tomography; TTE = transthoracic echocardiography.

^aUse of SPECT and PET in cardio-oncology is currently limited and may expand in the future.