Comparative Effectiveness of Tumor Response Assessment Methods: Standard of Care Versus Computer-Assisted Response Evaluation

Imaging for Response Assessment in Cancer Clinical Trials

The use of biomarkers is integral to the routine management of cancer patients, including diagnosis of disease, clinical staging and response to therapeutic intervention. Advanced imaging metrics with computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) are used to assess response during new drug development and in cancer research for predictive metrics of response. Key components and challenges to identifying an appropriate imaging biomarker are selection of integral vs integrated biomarkers, choosing an appropriate endpoint and modality, and standardization of the imaging biomarkers for cooperative and multicenter trials. Imaging biomarkers lean on the original proposed quantified metrics derived from imaging such as tumor size or longest dimension, with the most commonly implemented metrics in clinical trials coming from the Response Evaluation Criteria in Solid Tumors (RECIST) criteria, and then adapted versions such as immune-RECIST (iRECIST) and Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST) for immunotherapy response and PET imaging, respectively. There have been many widely adopted biomarkers in clinical trials derived from MRI including metrics that describe cellularity and vascularity from diffusion-weighted (DW)-MRI apparent diffusion coefficient (ADC) and Dynamic Susceptibility Contrast (DSC) or dynamic contrast enhanced (DCE)-MRI (Ktrans, relative cerebral blood volume (rCBV)), respectively. Furthermore, Fluorodexoyglucose (FDG), fluorothymidine (FLT), and fluoromisonidazole (FMISO)-PET imaging, which describe molecular markers of glucose metabolism, proliferation and hypoxia have been implemented into various cancer types to assess therapeutic response to a wide variety of targeted- and chemotherapies. Recently, there have been many functional and molecular novel imaging biomarkers that are being developed that are rapidly being integrated into clinical trials (with anticipation of being implemented into clinical workflow in the future), such as artificial intelligence (AI) and machine learning computational strategies, antibody and peptide specific molecular imaging, and advanced diffusion MRI. These include prostate-specific membrane antigen (PSMA) and trastuzumab-PET, vascular tumor burden extracted from contrast-enhanced CT, diffusion kurtosis imaging, and CD8 or Granzyme B PET imaging. Further excitement surrounds theranostic procedures such as the combination of 68Ga/111In- and 177Lu-DOTATATE to use integral biomarkers to direct care and personalize therapy. However, there are many challenges in the implementation of imaging biomarkers that remains, including understand the accuracy, repeatability and reproducibility of both acquisition and analysis of these imaging biomarkers. Despite the challenges associated with the biological and technical validation of novel imaging biomarkers, a distinct roadmap has been created that is being implemented into many clinical trials to advance the development and implementation to create specific and sensitive novel imaging biomarkers of therapeutic response to continue to transform medical oncology.

Response evaluation for immunotherapy through semi-automatic software based on RECIST 1.1, irRC, and iRECIST criteria: comparison with subjective assessment

BACKGROUND

Pseudoprogression is difficult to diagnose in patients undergoing immunotherapy. Subjective response assessment is still common in clinical practice.

PURPOSE

To evaluate the differences between response evaluation criteria in solid tumors version 1.1 (RECIST 1.1), immune-related response criteria (irRC), and modified RECIST 1.1 for immunotherapy (iRECIST) through semi-automatic software, and to compare iRECIST-based response evaluation with subjective assessment.

MATERIAL AND METHODS

The best overall response of each patient based on RECIST 1.1, irRC, and iRECIST was determined on CT scans through semi-automatic software and the differences between the criteria were evaluated. Criteria-based response evaluation through semi-automatic software was compared with subjective assessment on radiology report by correlating the best overall response to overall survival.

RESULTS

A total of 21 patients were included (five patients with melanoma, 12 patients with non-small-cell lung cancer, and four patients with hepatocellular carcinoma). Two patients with progressive disease by RECIST 1.1 but non-progressive disease by irRC and iRECIST eventually experienced tumor response and had favorable outcomes, indicating pseudoprogression. The survival difference between patients with non-progressive disease and progressive disease was better stratified through iRECIST-based response evaluation (P = 0.078) than that through subjective assessment (P = 0.501).

CONCLUSION

Pseudoprogression in immunotherapy may be captured through semi-automatic software utilizing irRC or iRECIST criteria. iRECIST-based response evaluation may provide a better survival stratification compared with subjective assessment.

Society of Abdominal Radiology disease-focused panel on renal cell carcinoma: update on past, current, and future goals

The disease-focused panel (DFP) program was created by the Society of Abdominal Radiology (SAR) as a mechanism to "improve patient care, education, and research" in a "particular disease or a particular aspect of a disease". The DFP on renal cell carcinoma (RCC) was proposed in 2014 and has been functional for 4 years. Although nominally focused on RCC, the scope of the DFP has included indeterminate renal masses because many cannot be assigned a specific diagnosis when detected. Since its founding, the DFP has been active in a variety of clinical, research, and educational projects to optimize the care of patients with known or suspected RCC. The DFP is utilizing multi-institutional and cross-disciplinary collaboration to differentiate benign from malignant disease, optimize the management of early stage RCC, and ultimately to differentiate indolent from aggressive cancers. Several additional projects have worked to develop a quantitative biomarker that predicts metastatic RCC response to anti-angiogenic therapy. While disease focus is the premise by which all DFPs are created, it is likely that in the future the RCC DFP will need to expand or create new panels that will focus on other specific aspects of RCC-a result that the program's founders envisioned. New knowledge creates a need for more focus.