Integration of quantitative and qualitative imaging methods for cancer diagnosis

Abstract

Author(s): Aman Chandrakar*, Chandrapratap Dhimar and Poorti Sharma

The integration of quantitative and qualitative imaging techniques has improved cancer detection by providing a more complete picture of patient outcomes. The diagnostic value and therapeutic strategy can both be improved with the use of quantitative imaging since it gives objective assessments of physiological and molecular changes. Qualitative imaging, on the other hand, delivers visual clues and contextual information necessary for deciphering complicated biological processes. When combined, the two methods provide a more complete picture that is useful for making accurate diagnoses and keeping tabs on patients during therapy. Challenges in data harmonization, validation, and clinical translation arise when integrating disparate data sources from quantitative and qualitative imaging approaches. Effectively combining the two forms of information requires ensuring consistent acquisition techniques and building Robust Analysis Processes (RAP). With the goal of improving diagnostic accuracy and allowing for more detailed illness characterization, a Hybrid Imaging Diagnostic Machine Learning-based Framework (HIDML-F) is suggested to fuse and analyse the hybrid data. HIDML-F is useful for treating both solid tumors and hematological malignancies. It is helpful in determining the aggressiveness of tumors, measuring the effectiveness of treatment, and differentiating benign from malignant growths. Furthermore, HIDML-F captures both functional and morphological information, which enables individualized treatment regimens. The value of HIDML-F is demonstrated through simulated patient scenarios and subsequent simulation analysis. HIDML-F has been shown to be superior to traditional imaging approaches in a number of ways, including its ability to detect subtle changes, reduce false positives, and boost diagnostic confidence, among others. The potential for early treatment response assessment to guide therapeutic interventions is further demonstrated by longitudinal simulations.

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