Artificial intelligence in cancer imaging: Clinical challenges and applications

Area of Science:

• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Medical decision-making in cancer requires integrating complex patient data.
• Accurate cancer detection, characterization, and monitoring remain challenging despite technological advancements.
• Radiographic assessment often relies on visual evaluation, with potential for enhancement by computational analyses.

Purpose of the Study:

• To review the current applications of artificial intelligence (AI) in cancer medical imaging.
• To highlight AI's potential in improving tumor delineation, genotype extrapolation, outcome prediction, and treatment impact assessment.
• To illustrate AI's advances in lung, brain, breast, and prostate cancer imaging.

Main Methods:

• Review of current artificial intelligence applications in oncology.
• Analysis of AI advancements in four specific tumor types: lung, brain, breast, and prostate.
• Examination of AI's role in addressing common clinical problems in cancer imaging.

Main Results:

• AI demonstrates significant potential in qualitative interpretation of cancer imaging.
• AI can aid in volumetric tumor delineation, predicting clinical outcomes, and assessing treatment effects.
• Studies show AI's capability to automate image interpretation and influence clinical workflows.

Conclusions:

• AI is poised to revolutionize cancer imaging interpretation and clinical decision-making.
• While rigorous validation is ongoing, AI shows promise for clinical integration in oncology.
• AI advancements are driving future directions in cancer care through improved imaging analysis.