A Primer on RECIST 1.1 for Oncologic Imaging in Clinical Drug Trials

Time-to-Event Endpoints in Imaging Biomarker Studies

Time-to-event endpoints are widely used as measures of patients' well-being and indicators of prognosis. In imaging-based biomarker studies, there are increasingly more studies that focus on examining imaging biomarkers' prognostic or predictive utilities on those endpoints, whether in a trial or an observational study setting. In this educational review article, we briefly introduce some basic concepts of time-to-event endpoints and point out potential pitfalls in the context of imaging biomarker research in hope of improving radiologists' understanding of related subjects. Besides, we have included some review and discussions on the benefits of using time-to-event endpoints and considerations on selecting overall survival or progression-free survival for primary analysis. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 3.

Association between pathologic response and survival after neoadjuvant therapy in lung cancer

Neoadjuvant immunotherapy plus chemotherapy improves event-free survival (EFS) and pathologic complete response (0% residual viable tumor (RVT) in primary tumor (PT) and lymph nodes (LNs)), and is approved for treatment of resectable lung cancer. Pathologic response assessment after neoadjuvant therapy is the potential analog to radiographic response for advanced disease. However, %RVT thresholds beyond pathologic complete response and major pathologic response (≤10% RVT) have not been explored. Pathologic response was prospectively assessed in the randomized, phase 3 CheckMate 816 trial (NCT02998528), which evaluated neoadjuvant nivolumab (anti-programmed death protein 1) plus chemotherapy in patients with resectable lung cancer. RVT, regression and necrosis were quantified (0-100%) in PT and LNs using a pan-tumor scoring system and tested for association with EFS in a prespecified exploratory analysis. Regardless of LN involvement, EFS improved with 0% versus >0% RVT-PT (hazard ratio = 0.18). RVT-PT predicted EFS for nivolumab plus chemotherapy (area under the curve = 0.74); 2-year EFS rates were 90%, 60%, 57% and 39% for patients with 0-5%, >5-30%, >30-80% and >80% RVT, respectively. Each 1% RVT associated with a 0.017 hazard ratio increase for EFS. Combining pathologic response from PT and LNs helped differentiate outcomes. When compared with radiographic response and circulating tumor DNA clearance, %RVT best approximated EFS. These findings support pathologic response as an emerging survival surrogate. Further assessment of the full spectrum of %RVT in lung cancer and other tumor types is warranted. ClinicalTrials.gov registration: NCT02998528 .

Overview of approaches to estimate real-world disease progression in lung cancer

BACKGROUND

Randomized clinical trials of novel treatments for solid tumors normally measure disease progression using the Response Evaluation Criteria in Solid Tumors. However, novel, scalable approaches to estimate disease progression using real-world data are needed to advance cancer outcomes research. The purpose of this narrative review is to summarize examples from the existing literature on approaches to estimate real-world disease progression and their relative strengths and limitations, using lung cancer as a case study.

METHODS

A narrative literature review was conducted in PubMed to identify articles that used approaches to estimate real-world disease progression in lung cancer patients. Data abstracted included data source, approach used to estimate real-world progression, and comparison to a selected gold standard (if applicable).

RESULTS

A total of 40 articles were identified from 2008 to 2022. Five approaches to estimate real-world disease progression were identified including manual abstraction of medical records, natural language processing of clinical notes and/or radiology reports, treatment-based algorithms, changes in tumor volume, and delta radiomics-based approaches. The accuracy of these progression approaches were assessed using different methods, including correlations between real-world endpoints and overall survival for manual abstraction (Spearman rank ρ = 0.61-0.84) and area under the curve for natural language processing approaches (area under the curve = 0.86-0.96).

CONCLUSIONS

Real-world disease progression has been measured in several observational studies of lung cancer. However, comparing the accuracy of methods across studies is challenging, in part, because of the lack of a gold standard and the different methods used to evaluate accuracy. Concerted efforts are needed to define a gold standard and quality metrics for real-world data.

Incurable advanced salivary gland tumours: a retrospective analysis and peek into the perplexing clinical and molecular intricacies from a tertiary care centre in India

Background

Salivary gland tumours are rare cancers with variable course and prognosis. There is a paucity of data, especially for the advanced stages.

Materials and methods

This is a retrospective analysis carried out in our institute. All patients seeking treatment for incurable advanced salivary gland tumours from October 2018 to September 2022 were included. Relevant clinical data were collected and appropriate statistical analysis was applied.

Results

30 patients were included in the analysis. The parotid gland was the most common site of origin (73%). Adenoid cystic carcinoma (ACC) and salivary duct carcinoma (SDC) were equally (37%) the most common pathological subtypes. The majority of patients were males (73%) and lungs (57%) were the most common site of metastases. On molecular analysis, SDC had high rates of androgen receptor (AR) (90%) and human epidermal growth factor receptor 2 (HER2) (55%) positivity. Mucoepidermoid carcinoma (MEC) had AR and HER2 positivity rates of 17% and 20%, respectively, while for ACC it was even lower. A variety of treatment regimens including hormonal therapy, anti-HER2 targeted therapy and chemotherapy were used in first-line treatment. With an overall response rate (ORR) of 10/21 (48%), only 9/21 (43%) went on to receive second-line treatment with an ORR of 4/9 (44%). The progression-free survival (PFS) with first-line treatment (PFS1) was a median of 5 months. The median PFS1 was worst for MEC. The median overall survival (OS) was 10 months. Median OS for ACC, SDC and MEC were 11, 10 and 7 months, respectively. At 24 months, ACC had much higher survival (50%) than others (10%) indicating a proportion of ACC with an indolent course.

Conclusion

Our analysis highlights the variable disease biology of advanced salivary gland tumours and throws light on the various possible treatment targets and strategies. Molecular profiling and advancement in targeted therapies are expected to increase survival in this group of rare cancers.

Pretherapy Ferumoxytol-enhanced MRI to Predict Response to Liposomal Irinotecan in Metastatic Breast Cancer

Purpose To investigate ferumoxytol (FMX)-enhanced MRI as a pretreatment predictor of response to liposomal irinotecan (nal-IRI) for thoracoabdominal and brain metastases in women with metastatic breast cancer (mBC). Materials and Methods In this phase 1 expansion trial (ClinicalTrials.gov identifier, NCT01770353; 27 participants), 49 thoracoabdominal (19 participants; mean age, 48 years ± 11 [SD]) and 19 brain (seven participants; mean age, 54 years ± 8) metastases were analyzed on MR images acquired before, 1-4 hours after, and 16-24 hours after FMX administration. In thoracoabdominal metastases, tumor transverse relaxation rate (R*₂) was normalized to the mean R*₂ in the spleen (rR*₂), and the tumor histogram metric rR*_2,N, representing the average of rR*₂ in voxels above the nth percentile, was computed. In brain metastases, a novel compartmentation index was derived by applying the MRI signal equation to phantom-calibrated coregistered FMX-enhanced MRI brain scans acquired before, 1-4 hours after, and 16-24 hours after FMX administration. The fraction of voxels with an FMX compartmentation index greater than 1 was computed over the whole tumor (FCIGT1) and from voxels above the 90th percentile R*₂ (FCIGT1 R*_2,90). Results rR*_2,90 computed from pretherapy MRI performed 16-24 hours after FMX administration, without reference to calibration phantoms, predicted response to nal-IRI in thoracoabdominal metastases (accuracy, 74%). rR*_2,90 performance was robust to the inclusion of some peritumoral tissue within the tumor region of interest. FCIGT1 R*_2,90 provided 79% accuracy on cross-validation in prediction of response in brain metastases. Conclusion This first in-human study focused on mBC suggests that FMX-enhanced MRI biologic markers can be useful for pretherapy prediction of response to nal-IRI in patients with mBC. Keywords: MRI Contrast Agent, MRI, Breast, Head/Neck, Tumor Response, Experimental Investigations, Brain/Brain Stem Clinical trial registration no. NCT01770353 Supplemental material is available for this article. © RSNA, 2023 See also commentary by Daldrup-Link in this issue.

Twenty Years On: RECIST as a Biomarker of Response in Solid Tumours an EORTC Imaging Group - ESOI Joint Paper

Response evaluation criteria in solid tumours (RECIST) v1.1 are currently the reference standard for evaluating efficacy of therapies in patients with solid tumours who are included in clinical trials, and they are widely used and accepted by regulatory agencies. This expert statement discusses the principles underlying RECIST, as well as their reproducibility and limitations. While the RECIST framework may not be perfect, the scientific bases for the anticancer drugs that have been approved using a RECIST-based surrogate endpoint remain valid. Importantly, changes in measurement have to meet thresholds defined by RECIST for response classification within thus partly circumventing the problems of measurement variability. The RECIST framework also applies to clinical patients in individual settings even though the relationship between tumour size changes and outcome from cohort studies is not necessarily translatable to individual cases. As reproducibility of RECIST measurements is impacted by reader experience, choice of target lesions and detection/interpretation of new lesions, it can result in patients changing response categories when measurements are near threshold values or if new lesions are missed or incorrectly interpreted. There are several situations where RECIST will fail to evaluate treatment-induced changes correctly; knowledge and understanding of these is crucial for correct interpretation. Also, some patterns of response/progression cannot be correctly documented by RECIST, particularly in relation to organ-site (e.g. bone without associated soft-tissue lesion) and treatment type (e.g. focal therapies). These require specialist reader experience and communication with oncologists to determine the actual impact of the therapy and best evaluation strategy. In such situations, alternative imaging markers for tumour response may be used but the sources of variability of individual imaging techniques need to be known and accounted for. Communication between imaging experts and oncologists regarding the level of confidence in a biomarker is essential for the correct interpretation of a biomarker and its application to clinical decision-making. Though measurement automation is desirable and potentially reduces the variability of results, associated technical difficulties must be overcome, and human adjudications may be required.

Circulating tumor DNA: Opportunities and challenges for pharmacometric approaches

To support further development of model-informed drug development approaches leveraging circulating tumor DNA (ctDNA), we performed an exploratory analysis of the relationships between treatment-induced changes to ctDNA levels, clinical response and tumor size dynamics in patients with cancer treated with checkpoint inhibitors and targeted therapies. This analysis highlights opportunities for pharmacometrics approaches such as for optimizing sampling design strategies. It also highlights challenges related to the nature of the data and associated variability overall emphasizing the importance of mechanistic modeling studies of the underlying biology of ctDNA processes such as shedding, release and clearance and their relationships with tumor size dynamic and treatment effects.