The goal of assessing tumour response on imaging is to identify patients who are likely to benefitor notfrom anticancer treatment, especially in relation to survival. The World Health Organization was the first to develop assessment criteria. This early score, which assessed tumour burden by standardising lesion size measurements, laid the groundwork for many of the criteria that followed. This was then improved by the Response Evaluation Criteria in Solid Tumours (RECIST) which was quickly adopted by the oncology community. At the same time, many interventional oncology treatments were developed to target specific features of liver tumours that result in significant changes in tumours but have little effect on tumour size. New criteria focusing on the viable part of tumours were therefore designed to provide more appropriate feedback to guide patient management. Targeted therapy has resulted in a breakthrough that challenges conventional response criteria due to the non-linear relationship between response and tumour size, requiring the development of methods that emphasize the appearance of tumours. More recently, research into functional and quantitative imaging has created new opportunities in liver imaging. These results have suggested that certain parameters could serve as early predictors of response or could predict later tumour response at baseline. These approaches have now been extended by machine learning and deep learning. This clinical review focuses on the progress made in the evaluation of liver tumours on imaging, discussing the rationale for this approach, addressing challenges and controversies in the field, and suggesting possible future developments.

BackgroundCancer patients often have a history of chemotherapy, putting them at increased risk of liver toxicity and pancytopenia, leading to elevated liver fat and elevated liver iron respectively. T1-in-and-out-of-phase, the conventional MR technique for liver fat assessment, fails to detect elevated liver fat in the presence of concomitantly elevated liver iron. IDEAL-IQ is a more recently introduced MR fat quantification method that corrects for multiple confounding factors, including elevated liver iron.MethodsThis retrospective study was approved by the institutional review board with a waiver for informed consent. We reviewed the MRI studies of 50 cancer patients (30 males, 20 females, 50–78 years old) whose exams included (1) T1-in-and-out-of-phase, (2) IDEAL-IQ, and (3) T2* mapping. Two readers independently assessed fat and iron content from conventional and IDEAL-IQ MR methods. Intraclass correlation coefficient (ICC) was estimated to evaluate agreement between conventional MRI and IDEAL-IQ in measuring R2* level (a surrogate for iron level), and in measuring fat level. Agreement between the two readers was also assessed. Wilcoxon signed rank test was employed to compare iron level and fat fraction between conventional MRI and IDEAL-IQ.ResultsTwenty percent of patients had both elevated liver iron and moderate/severe hepatic steatosis. Across all patients, there was high agreement between readers for IDEAL-IQ fat fraction (ICC = 0.957) and IDEAL R2* (ICC = 0.971) measurements, but lower agreement for conventional fat fraction measurements (ICC = 0.626). The fat fractions calculated with IOP were statistically significantly different from those calculated with IDEAL-IQ (reader 1: p < 0.001, reader 2: p < 0.001).ConclusionFat measurements using IDEAL-IQ and IOP diverged in patients with concomitantly elevated liver fat and liver iron. Given prior work validating IDEAL-IQ, these diverging measurements indicate that IOP is inadequate to screen for hepatic steatosis in our cancer population.

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The reprogramming of cellular metabolism is a hallmark of cancer diagnosis and prognosis. Proton magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic technique for investigating brain metabolism to establish cancer diagnosis and IDH gene mutation diagnosis as well as facilitate pre-operative planning and treatment response monitoring. By allowing tissue metabolism to be quantified, MRSI provides added value to conventional MRI. MRSI can generate metabolite maps from a single volume or multiple volume elements within the whole brain. Metabolites such as NAA, Cho and Cr, as well as their ratios Cho:NAA ratio and Cho:Cr ratio, have been used to provide tumor diagnosis and aid in radiation therapy planning as well as treatment assessment. In addition to these common metabolites, 2-hydroxygluterate (2HG) has also been quantified using MRSI following the recent discovery of IDH mutations in gliomas. This has opened up targeted drug development to inhibit the mutant IDH pathway. This review provides guidance on MRSI in brain gliomas, including its acquisition, analysis methods, and evolving clinical applications.

Second-opinion review by subspecialized oncologic radiologists can impact patient care, specifically in terms of management decision.

Our results demonstrate better image quality and reduced artifacts in rFOV images compared with large FOV DWI. Measurements from ADC maps derived from rFOV DWI show significantly lower ADC values when compared with ADC maps derived from large FOV DWI.

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The novel CSE-MRI method described here provides increased reproducibility and confidence in diagnosing hepatic steatosis in a oncologic clinical setting. IDEAL-IQ has been proved to be more reproducible than conventional IOP imaging.