Observer variability in RECIST-based tumour burden measurements: a meta-analysis

The ULS23 challenge: A baseline model and benchmark dataset for 3D universal lesion segmentation in computed tomography

Size measurements of tumor manifestations on follow-up CT examinations are crucial for evaluating treatment outcomes in cancer patients. Efficient lesion segmentation can speed up these radiological workflows. While numerous benchmarks and challenges address lesion segmentation in specific organs like the liver, kidneys, and lungs, the larger variety of lesion types encountered in clinical practice demands a more universal approach. To address this gap, we introduced the ULS23 benchmark for 3D universal lesion segmentation in chest-abdomen-pelvis CT examinations. The ULS23 training dataset contains 38,693 lesions across this region, including challenging pancreatic, colon and bone lesions. For evaluation purposes, we curated a dataset comprising 775 lesions from 284 patients. Each of these lesions was identified as a target lesion in a clinical context, ensuring diversity and clinical relevance within this dataset. The ULS23 benchmark is publicly accessible at https://uls23.grand-challenge.org, enabling researchers worldwide to assess the performance of their segmentation methods. Furthermore, we have developed and publicly released our baseline semi-supervised 3D lesion segmentation model. This model achieved an average Dice coefficient of 0.703 ± 0.240 on the challenge test set. We invite ongoing submissions to advance the development of future ULS models.

A promptable CT foundation model for solid tumor evaluation

Carcinogenesis is inherently complex, resulting in heterogeneous tumors with variable outcomes and frequent metastatic potential. Conventional longitudinal evaluation methods like RECIST 1.1 remain labor-intensive and prone to measurement errors, while existing AI solutions face critical limitations due to tumor heterogeneity, insufficient annotations, and lack of user interaction. We developed ONCOPILOT, an interactive CT-based foundation model dedicated to 3D tumor segmentation, significantly refining RECIST 1.1 evaluations with active radiologist engagement. Trained on more than 8000 CT scans, ONCOPILOT employs intuitive visual prompts, including point-click, bounding boxes, and edit-points. It attains segmentation accuracy that matches or exceeds state-of-the-art methods, provides radiologist-level precision for RECIST 1.1 measurements, reduces inter-observer variability, and enhances workflow efficiency. Integrating clinical expertise with interactive AI capabilities, ONCOPILOT facilitates widespread access to advanced biomarkers, notably volumetric tumor analyses, thereby supporting improved clinical decision-making, patient stratification, and accelerating advancements in oncology research.

A CT-based deep learning-driven tool for automatic liver tumor detection and delineation in patients with cancer

Liver tumors, whether primary or metastatic, significantly impact the outcomes of patients with cancer. Accurate identification and quantification are crucial for effective patient management, including precise diagnosis, prognosis, and therapy evaluation. We present SALSA (system for automatic liver tumor segmentation and detection), a fully automated tool for liver tumor detection and delineation. Developed on 1,598 computed tomography (CT) scans and 4,908 liver tumors, SALSA demonstrates superior accuracy in tumor identification and volume quantification, outperforming state-of-the-art models and inter-reader agreement among expert radiologists. SALSA achieves a patient-wise detection precision of 99.65%, and 81.72% at lesion level, in the external validation cohorts. Additionally, it exhibits good overlap, achieving a dice similarity coefficient (DSC) of 0.760, outperforming both state-of-the-art and the inter-radiologist assessment. SALSA's automatic quantification of tumor volume proves to have prognostic value across various solid tumors (p = 0.028). SALSA's robust capabilities position it as a potential medical device for automatic cancer detection, staging, and response evaluation.

ESR Essentials: assessing the radiological response of liver metastases to systemic therapy-practice recommendations by the European Society of Gastrointestinal and Abdominal Radiology

The liver is a common site for metastatic spread, especially in advanced colorectal, breast, and pancreatic cancers. Imaging evaluation of liver metastases after systemic treatments like chemotherapy, targeted therapy, or immunotherapy is essential to distinguish treatment response from disease progression. The widely used response evaluation criteria in solid tumours (RECIST 1.1) focus on lesion size changes to evaluate treatment response. However, newer therapies, mainly targeted therapy and immunotherapy, often induce changes beyond size reduction, such as tumour necrosis, fibrosis, cystic transformation, calcifications, and modifications at the liver-tumour interface. These morphological and enhancement changes can be evaluated on CT and MRI and may better reflect the biological response in specific clinical settings. Overall, RECIST 1.1 criteria are recommended for assessing the radiological response of liver metastases after systemic treatment. The use of alternative radiological criteria validated on CT (such as Chun or Choi criteria) is recommended in specific clinical settings (e.g. metastatic colorectal cancer or metastatic gastrointestinal stromal tumours). Additionally, CT and MR modifications that reflect fibrosis, necrosis, calcifications, and haemorrhage can serve as ancillary indicators of tumoural response. These alternative criteria and radiological findings should be systematically assessed, particularly in liver metastases with minimal size changes, to better identify responders. KEY POINTS: RECIST 1.1 is the standard for evaluating tumour response in solid tumours and is recommended for the assessment of liver metastases after systemic therapy. CT attenuation, enhancement, and liver/tumour interface may correlate better with tumoural response compared to size reduction. CT and MR changes suggesting necrosis, fibrosis, calcifications, and haemorrhage can be used as additional indicators of tumoural response.

Measurement variability of radiologists when measuring brain tumors

BACKGROUND

In oncology trials, response evaluation criteria are pivotal in developing new treatments. This study examines the influence of measurement variability in brain lesions on response classification, considering long-standing cut-offs for progression and response were determined before the era of submillimeter resolutions of medical imaging.

METHODS

We replicate a key study using modern radiological tools. Sixteen radiologists were tasked with measuring twelve near-spherical brain tumors using visual estimation (eyeballing), diameter measurements and artificial intelligence (AI) assisted segmentations. Analyses for inter- and intraobserver variability from the original were replicated. Additionally, we researched the effect of measurement error on the misclassification of progressive disease using a computer simulation model.

RESULTS

The combined effect of intra- and interobserver error varied between 13.6 and 22.2% for eyeballing and 6.8-7.2% for diameter measurement, using AI-assisted segmentation as reference. We observed erroneously declared progression (cut-off at 20% increase) in repeat measurements of the same tumor in 25.5% of instances for eyeballing and in 1.1% for diameter measurements. Response (cut-off at 30% decrease) was erroneously declared in 12.3% for eyeballing and in 0% for diameter measurements. The simulation model demonstrated a more pronounced impact of measurement error on cases with fewer total number of lesions.

CONCLUSIONS

This study provides a minimum expected measurement error using real-world data. The impact of measurement error on response evaluation criteria misclassification in brain lesions was most pronounced for eyeballing. Future research should focus on measurement error for different tumor types and assess its impact on response classification during patient follow-up.

Prospective evaluation of quantitative response parameter in patients with Gastrointestinal Stroma Tumor undergoing tyrosine kinase inhibitor therapy-Impact on clinical outcome

The purpose of this study was to determine if dual-energy CT (DECT) vital iodine tumor burden (ViTB), a direct assessment of tumor vascularity, allows reliable response assessment in patients with GIST compared to established CT criteria such as RECIST1.1 and modified Choi (mChoi). From 03/2014 to 12/2019, 138 patients (64 years [32-94 years]) with biopsy proven GIST were entered in this prospective, multi-center trial. All patients were treated with tyrosine kinase inhibitors (TKI) and underwent pre-treatment and follow-up DECT examinations for a minimum of 24 months. Response assessment was performed according to RECIST1.1, mChoi, vascular tumor burden (VTB) and DECT ViTB. A change in therapy management could be because of imaging (RECIST1.1 or mChoi) and/or clinical progression. The DECT ViTB criteria had the highest discrimination ability for progression-free survival (PFS) of all criteria in both first line and second line and thereafter treatment, and was significantly superior to RECIST1.1 and mChoi (p < .034). Both, the mChoi and DECT ViTB criteria demonstrated a significantly early median time-to-progression (both delta 2.5 months; both p < .036). Multivariable analysis revealed 6 variables associated with shorter overall survival: secondary mutation (HR = 4.62), polymetastatic disease (HR = 3.02), metastatic second line and thereafter treatment (HR = 2.33), shorter PFS determined by the DECT ViTB criteria (HR = 1.72), multiple organ metastases (HR = 1.51) and lower age (HR = 1.04). DECT ViTB is a reliable response criteria and provides additional value for assessing TKI treatment in GIST patients. A significant superior response discrimination ability for median PFS was observed, including non-responders at first follow-up and patients developing resistance while on therapy.

Dedicated software to harmonize the follow-up of oncological patients

Objective

To test and evaluate a sofware dedicated to the follow-up of oncological CT scans for potential use in the Radiology department.

Materials and methods

In this retrospective study, 37 oncological patients with baseline and follow-up CT scans were reinterpreted using a dedicated software. Baseline CT scans were chosen from the imaging reports available in our PACS (picture archiving and communicatin systems). Follow-up interpretations were independently assessed with the software. We evaluated the target lesion sums and the tumor response based on RECIST 1.1 (Response Evaluation Criteria in Solid Tumors).

Results

There was no significant difference in the target lesion sums and the tumor response assessments between the PACS data and the imaging software. There was no over or underestimation of the disease with the software. There was a sigificant deviation (progression versus stability) in three cases. For two patients, this difference was related to the evaluation of the response of non-target lesions. The difference in the third patient was due to comparison with a previous CT scan than to the baseline exam. There was a miscalculation in 13 % of the reports and in 28 % of the cases the examination was compared to the previous CT scan. Finally, the tumor response was not detailed in 43 % of the follow-up reports.

Conclusion

The use of dedicated oncology monitoring software could help in reducing intepretation time and in limiting human errors.

Reliability of Automated RECIST 1.1 and Volumetric RECIST Target Lesion Response Evaluation in Follow-Up CT-A Multi-Center, Multi-Observer Reading Study

OBJECTIVES

To evaluate the performance of a custom-made convolutional neural network (CNN) algorithm for fully automated lesion tracking and segmentation, as well as RECIST 1.1 evaluation, in longitudinal computed tomography (CT) studies compared to a manual Response Evaluation Criteria in Solid Tumors (RECIST 1.1) evaluation performed by three radiologists.

METHODS

Baseline and follow-up CTs of patients with stage IV melanoma (n = 58) was investigated in a retrospective reading study. Three radiologists performed manual measurements of metastatic lesions. Fully automated segmentations were generated, and diameters and volumes were computed from the segmentation results, with subsequent RECIST 1.1 evaluation. We measured (1) the intra- and inter-reader variability in the manual diameter measurements, (2) the agreement between manual and automated diameter measurements, as well as the resulting RECIST 1.1 categories, and (3) the agreement between the RECIST 1.1 categories derived from automated diameter measurement compared to automated volume measurements.

RESULTS

In total, 114 target lesions were measured at baseline and follow-up. The intraclass correlation coefficients (ICCs) for the intra- and inter-reader reliability of the diameter measurements were excellent, being >0.90 for all readers. There was moderate to almost perfect agreement when comparing the timepoint response category derived from the mean manual diameter measurements from all three readers with those derived from automated diameter measurements (Cohen's k 0.67-0.76). The agreement between the manual and automated volumetric timepoint responses was substantial (Fleiss' k 0.66-0.68) and that between the automated diameter and volume timepoint responses was substantial to almost perfect (Cohen's k 0.81).

CONCLUSIONS

The automated diameter measurement of preselected target lesions in follow-up CT is reliable and can potentially help to accelerate RECIST evaluation.

Lenvatinib plus pembrolizumab for patients with previously treated, advanced, triple-negative breast cancer: Results from the triple-negative breast cancer cohort of the phase 2 LEAP-005 Study

BACKGROUND

Novel treatments are needed for patients with advanced, triple-negative breast cancer (TNBC) that progresses or recurs after first-line treatment with chemotherapy. The authors report results from the TNBC cohort of the multicohort, open-label, single-arm, phase 2 LEAP-005 study of lenvatinib plus pembrolizumab in patients with advanced solid tumors (ClinicalTrials.gov identifier NCT03797326).

METHODS

Eligible patients had metastatic or unresectable TNBC with disease progression after one or two lines of therapy. Patients received lenvatinib (20 mg daily) plus pembrolizumab (200 mg every 3 weeks; up to 35 cycles). The primary end points were the objective response rate according to Response Evaluation Criteria in Solid Tumors, version 1.1, and safety (adverse events graded by the National Cancer Institute's Common Terminology Criteria for Adverse Events, version 4.0). Duration of response, progression-free survival, and overall survival were secondary end points.

RESULTS

Thirty-one patients were enrolled. The objective response rate by investigator assessment was 23% (95% confidence interval [CI], 10%-41%). Overall, the objective response rate by blinded independent central review (BICR) was 32% (95% CI, 17%-51%); and, in patients who had programmed cell death ligand 1 combined positive scores ≥10 (n = 8) and <10 (n = 22), the objective response rate was 50% (95% CI, 16%-84%) and 27% (95% CI, 11%-50%), respectively. The median duration of response by BICR was 12.1 months (range, from 3.0+ to 37.9+ months). The median progression-free survival by BICR was 5.1 months (95% CI, 1.9-11.8 months) and the median overall survival was 11.4 months (95% CI, 4.1-21.7 months). Treatment-related adverse events occurred in 94% of patients (grade 3, 52%; grade 4, 0%). One patient died due to a treatment-related adverse event of subarachnoid hemorrhage.

CONCLUSIONS

The combination of lenvatinib plus pembrolizumab demonstrated antitumor activity with a manageable safety profile in patients with previously treated, advanced TNBC.

RECIST 1.1 assessments variability: a systematic pictorial review of blinded double reads

Reader variability is intrinsic to radiologic oncology assessments, necessitating measures to enhance consistency and accuracy. RECIST 1.1 criteria play a crucial role in mitigating this variability by standardizing evaluations, aiming to establish an accepted "truth" confirmed by histology or patient survival. Clinical trials utilize Blind Independent Centralized Review (BICR) techniques to manage variability, employing double reads and adjudicators to address inter-observer discordance effectively. It is essential to dissect the root causes of variability in response assessments, with a specific focus on the factors influencing RECIST evaluations. We propose proactive measures for radiologists to address variability sources such as radiologist expertise, image quality, and accessibility of contextual information, which significantly impact interpretation and assessment precision. Adherence to standardization and RECIST guidelines is pivotal in diminishing variability and ensuring uniform results across studies. Variability factors, including lesion selection, new lesion appearance, and confirmation bias, can have profound implications on assessment accuracy and interpretation, underscoring the importance of identifying and addressing these factors. Delving into the causes of variability aids in enhancing the accuracy and consistency of response assessments in oncology, underscoring the role of standardized evaluation protocols and mitigating risk factors that contribute to variability. Access to contextual information is crucial. CRITICAL RELEVANCE STATEMENT: By understanding the causes of diagnosis variability, we can enhance the accuracy and consistency of response assessments in oncology, ultimately improving patient care and clinical outcomes. KEY POINTS: Baseline lesion selection and detection of new lesions play a major role in the occurrence of discordance. Image interpretation is influenced by contextual information, the lack of which can lead to diagnostic uncertainty. Radiologists must be trained in RECIST criteria to reduce errors and variability.

Can blood-based markers predict RECIST progression in non-small cell lung cancer treated with immunotherapy?

PURPOSE

In this study, we aimed to evaluate the potential of routine blood markers, serum tumour markers and their combination in predicting RECIST-defined progression in patients with stage IV non-small cell lung cancer (NSCLC) undergoing treatment with immune checkpoint inhibitors.

METHODS

We employed time-varying statistical models and machine learning classifiers in a Monte Carlo cross-validation approach to investigate the association between RECIST-defined progression and blood markers, serum tumour markers and their combination, in a retrospective cohort of 164 patients with NSCLC.

RESULTS

The performance of the routine blood markers in the prediction of progression free survival was moderate. Serum tumour markers and their combination with routine blood markers generally improved performance compared to routine blood markers alone. Elevated levels of C-reactive protein (CRP) and alkaline phosphatase (ALP) ranked as the top predictive routine blood markers, and CYFRA 21.1 was consistently among the most predictive serum tumour markers. Using these classifiers to predict overall survival yielded moderate to high performance, even when cases of death-defined progression were excluded. Performance varied across the treatment journey.

CONCLUSION

Routine blood tests, especially when combined with serum tumour markers, show moderate predictive value  of RECIST-defined progression in NSCLC patients receiving immune checkpoint inhibitors. The relationship between overall survival and RECIST-defined progression may be influenced by confounding factors.

How Does Target Lesion Selection Affect RECIST? A Computer Simulation Study

OBJECTIVES

Response Evaluation Criteria in Solid Tumors (RECIST) is grounded on the assumption that target lesion selection is objective and representative of the change in total tumor burden (TTB) during therapy. A computer simulation model was designed to challenge this assumption, focusing on a particular aspect of subjectivity: target lesion selection.

MATERIALS AND METHODS

Disagreement among readers and the disagreement between individual reader measurements and TTB were analyzed as a function of the total number of lesions, affected organs, and lesion growth.

RESULTS

Disagreement rises when the number of lesions increases, when lesions are concentrated on a few organs, and when lesion growth borders the thresholds of progressive disease and partial response. There is an intrinsic methodological error in the estimation of TTB via RECIST 1.1, which depends on the number of lesions and their distributions. For example, for a fixed number of lesions at 5 and 15, distributed over a maximum of 4 organs, the error rates are observed to be 7.8% and 17.3%, respectively.

CONCLUSIONS

Our results demonstrate that RECIST can deliver an accurate estimate of TTB in localized disease, but fails in cases of distal metastases and multiple organ involvement. This is worsened by the "selection of the largest lesions," which introduces a bias that makes it hardly possible to perform an accurate estimate of the TTB. Including more (if not all) lesions in the quantitative analysis of tumor burden is desirable.

Reproducing RECIST lesion selection via machine learning: Insights into intra and inter-radiologist variation

Background

The Response Evaluation Criteria in Solid Tumors (RECIST) aims to provide a standardized approach to assess treatment response in solid tumors. However, discrepancies in the selection of measurable and target lesions among radiologists using these criteria pose a significant limitation to their reproducibility and accuracy. This study aimed to understand the factors contributing to this variability.

Methods

Machine learning models were used to replicate, in parallel, the selection process of measurable and target lesions by two radiologists in a cohort of 40 patients from an internal pan-cancer dataset. The models were trained on lesion characteristics such as size, shape, texture, rank, and proximity to other lesions. Ablation experiments were conducted to evaluate the impact of lesion diameter, volume, and rank on the selection process.

Results

The models successfully reproduced the selection of measurable lesions, relying primarily on size-related features. Similarly, the models reproduced target lesion selection, relying mostly on lesion rank. Beyond these features, the importance placed by different radiologists on different visual characteristics can vary, specifically when choosing target lesions. Worth noting that substantial variability was still observed between radiologists in both measurable and target lesion selection.

Conclusions

Despite the successful replication of lesion selection, our results still revealed significant inter-radiologist disagreement. This underscores the necessity for more precise guidelines to standardize lesion selection processes and minimize reliance on individual interpretation and experience as a means to bridge existing ambiguities.

Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases

BACKGROUND

We developed models for tumor segmentation to automate the assessment of total tumor volume (TTV) in patients with colorectal liver metastases (CRLM).

METHODS

In this prospective cohort study, pre- and post-systemic treatment computed tomography (CT) scans of 259 patients with initially unresectable CRLM of the CAIRO5 trial (NCT02162563) were included. In total, 595 CT scans comprising 8,959 CRLM were divided into training (73%), validation (6.5%), and test sets (21%). Deep learning models were trained with ground truth segmentations of the liver and CRLM. TTV was calculated based on the CRLM segmentations. An external validation cohort was included, comprising 72 preoperative CT scans of patients with 112 resectable CRLM. Image segmentation evaluation metrics and intraclass correlation coefficient (ICC) were calculated.

RESULTS

In the test set (122 CT scans), the autosegmentation models showed a global Dice similarity coefficient (DSC) of 0.96 (liver) and 0.86 (CRLM). The corresponding median per-case DSC was 0.96 (interquartile range [IQR] 0.95-0.96) and 0.80 (IQR 0.67-0.87). For tumor segmentation, the intersection-over-union, precision, and recall were 0.75, 0.89, and 0.84, respectively. An excellent agreement was observed between the reference and automatically computed TTV for the test set (ICC 0.98) and external validation cohort (ICC 0.98). In the external validation, the global DSC was 0.82 and the median per-case DSC was 0.60 (IQR 0.29-0.76) for tumor segmentation.

CONCLUSIONS

Deep learning autosegmentation models were able to segment the liver and CRLM automatically and accurately in patients with initially unresectable CRLM, enabling automatic TTV assessment in such patients.

RELEVANCE STATEMENT

Automatic segmentation enables the assessment of total tumor volume in patients with colorectal liver metastases, with a high potential of decreasing radiologist's workload and increasing accuracy and consistency.

KEY POINTS

• Tumor response evaluation is time-consuming, manually performed, and ignores total tumor volume. • Automatic models can accurately segment tumors in patients with colorectal liver metastases. • Total tumor volume can be accurately calculated based on automatic segmentations.

Can we predict discordant RECIST 1.1 evaluations in double read clinical trials?

Background

In lung clinical trials with imaging, blinded independent central review with double reads is recommended to reduce evaluation bias and the Response Evaluation Criteria In Solid Tumor (RECIST) is still widely used. We retrospectively analyzed the inter-reader discrepancies rate over time, the risk factors for discrepancies related to baseline evaluations, and the potential of machine learning to predict inter-reader discrepancies.

Materials and methods

We retrospectively analyzed five BICR clinical trials for patients on immunotherapy or targeted therapy for lung cancer. Double reads of 1724 patients involving 17 radiologists were performed using RECIST 1.1. We evaluated the rate of discrepancies over time according to four endpoints: progressive disease declared (PDD), date of progressive disease (DOPD), best overall response (BOR), and date of the first response (DOFR). Risk factors associated with discrepancies were analyzed, two predictive models were evaluated.

Results

At the end of trials, the discrepancy rates between trials were not different. On average, the discrepancy rates were 21.0%, 41.0%, 28.8%, and 48.8% for PDD, DOPD, BOR, and DOFR, respectively. Over time, the discrepancy rate was higher for DOFR than DOPD, and the rates increased as the trial progressed, even after accrual was completed. It was rare for readers to not find any disease, for less than 7% of patients, at least one reader selected non-measurable disease only (NTL). Often the readers selected some of their target lesions (TLs) and NTLs in different organs, with ranges of 36.0-57.9% and 60.5-73.5% of patients, respectively. Rarely (4-8.1%) two readers selected all their TLs in different locations. Significant risk factors were different depending on the endpoint and the trial being considered. Prediction had a poor performance but the positive predictive value was higher than 80%. The best classification was obtained with BOR.

Conclusion

Predicting discordance rates necessitates having knowledge of patient accrual, patient survival, and the probability of discordances over time. In lung cancer trials, although risk factors for inter-reader discrepancies are known, they are weakly significant, the ability to predict discrepancies from baseline data is limited. To boost prediction accuracy, it would be necessary to enhance baseline-derived features or create new ones, considering other risk factors and looking into optimal reader associations.

The Role of Additional Staining in the Assessment of the Peritoneal Regression Grading Score (PRGS) in Peritoneal Metastasis of Gastric Origin

INTRODUCTION

The Peritoneal Regression Grading Score (PRGS) is a 4-tied histologic regression grading score for determining the response of peritoneal metastasis to chemotherapy. Peritoneal biopsies in every abdominal quadrant are recommended. A positive therapy response is defined as a decreasing or stable mean PRGS between 2 therapy cycles. The added value of periodic acid satin (PAS) and Ber-EP4 staining over HE staining for diagnosing PRGS1 (the absence of vital tumor cells) is unclear.

MATERIALS AND METHODS

A total of 339 biopsies obtained during 76 laparoscopies in 33 patients with peritoneal metastasis of gastric cancer were analyzed. Biopsies classified as PRGS 1 (no residual tumor, n=95) or indefinite (n=50) were stained with PAS, and remaining indefinite or PRGS1 cases additionally stained with BerEP4.

RESULTS

After PAS-staining tumor cells were detected in 28 out of 145 biopsies (19%), the remaining 117 biopsies were immunostained with Ber-EP4. Tumor cells were detected in 22 biopsies (19%). In total, additional staining allowed the detection of residual tumor cells in 50 out of 339 biopsies (15%) and changed the therapy response assessment in 7 out of 33 (21%) patients.

CONCLUSIONS

In summary, 25% (24 out of 95) of initially tumor-free samples (PRGS1) showed residual tumor cells after additional staining with PAS and/or BerEp4. Immunohistochemistry provided important additional information (the presence of tumor cells) in 22 of all 339 biopsies (11.2%). Further staining reduced the instances of unclear diagnosis from 50 to 0 and changed the therapy response assessment in 7 out of 33 patients (21%). We recommend additional staining in PRGS1 or unclear cases.

Challenges, Complexities, and Considerations in the Design and Interpretation of Late-Phase Oncology Trials

Optimal management of cancer patients relies heavily on late-phase oncology randomized controlled trials. A comprehensive understanding of the key considerations in designing and interpreting late-phase trials is crucial for improving subsequent trial design, execution, and clinical decision-making. In this review, we explore important aspects of late-phase oncology trial design. We begin by examining the selection of primary endpoints, including the advantages and disadvantages of using surrogate endpoints. We address the challenges involved in assessing tumor progression and discuss strategies to mitigate bias. We define informative censoring bias and its impact on trial results, including illustrative examples of scenarios that may lead to informative censoring. We highlight the traditional roles of the log-rank test and hazard ratio in survival analyses, along with their limitations in the presence of nonproportional hazards as well as an introduction to alternative survival estimands, such as restricted mean survival time or MaxCombo. We emphasize the distinctions between the design and interpretation of superiority and noninferiority trials, and compare Bayesian and frequentist statistical approaches. Finally, we discuss appropriate utilization of phase II and phase III trial results in shaping clinical management recommendations and evaluate the inherent risks and benefits associated with relying on phase II data for treatment decisions.

Discrepant Assessments of Progressive Disease in Clinical Trials between Routine Clinical Reads and Formal RECIST 1.1 Interpretations

Purpose To analyze the frequency of discrepant interpretations of progressive disease (PD) between routine clinical and formal Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 interpretations in patients enrolled in solid tumor clinical trials and investigate the causes of discordance. Materials and Methods This retrospective study included patients in solid tumor clinical trials undergoing imaging response assessments based on RECIST 1.1 from January to July 2021. Routine clinical interpretations (RCIs) performed as part of standard workflow and not requiring formal use of any established response criteria were compared with separate local core laboratory interpretations (CLIs) by specially trained radiologists who used software that tracks target lesion measurements, changes in nontarget lesions, and appearance of new lesions longitudinally. The comparison focused on discordant interpretations of PD. Results Among 1053 patients who had both RCIs and CLIs performed, PD was diagnosed on one or both reads in 327 patients (median age, 63.6 [range, 22.4-83.2] years; 57.8% female patients). The RCIs and CLIs agreed with PD status in 65% (213 of 327) of assessments. In 32% (105 of 327) of assessments, RCIs overdiagnosed PD when CLIs diagnosed stable disease, and in 3% (nine of 327), CLIs diagnosed PD when RCIs diagnosed stable disease. Reasons for discrepant RCIs of PD included erroneous target lesion measurements (58%, 61 of 105), erroneous diagnosis of nontarget progression (30%, 32 of 105), and misclassification of new lesions as cancer (11%, 12 of 105). Most patients (93%, 98 of 105) with RCI overdiagnosis of PD remained in the clinical trial for one or more treatment cycles. Conclusion PD was frequently overdiagnosed on RCIs versus formal RECIST 1.1 CLIs which could result in patients removed from the clinical trial inappropriately. Keywords: Oncology, Cancer, Tumor Response, MR Imaging, CT © RSNA, 2023 See also commentary by Margolis and Ruchalski in this issue.

Monitoring of circulating tumour DNA in advanced pancreatic ductal adenocarcinoma predicts clinical outcome and reveals disease progression earlier than radiological imaging

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease with a need for better tools to guide treatment selection and follow-up. The aim of this prospective study was to investigate the prognostic value and treatment monitoring potential of longitudinal circulating tumour DNA (ctDNA) measurements in patients with advanced PDAC undergoing palliative chemotherapy. Using KRAS peptide nucleic acid clamp-PCR, we measured ctDNA levels in plasma samples obtained at baseline and every 4 weeks during chemotherapy from 81 patients with locally advanced and metastatic PDAC. Cox proportional hazard regression showed that ctDNA detection at baseline was an independent predictor of progression-free and overall survival. Joint modelling demonstrated that the dynamic ctDNA level was a strong predictor of time to first disease progression. Longitudinal ctDNA measurements during chemotherapy successfully revealed disease progression in 20 (67%) of 30 patients with ctDNA detected at baseline, with a median lead time of 23 days (P = 0.01) over radiological imaging. Here, we confirmed the clinical relevance of ctDNA in advanced PDAC with regard to both the prediction of clinical outcome and disease monitoring during treatment.

Appropriate use of morphological imaging for assessing treatment response and disease progression of neuroendocrine tumors

Neuroendocrine tumors (NETs) are relatively rare neoplasms displaying heterogeneous clinical behavior, ranging from indolent to aggressive forms. Patients diagnosed with NETs usually receive a varied array of treatments, including somatostatin analogs, locoregional treatments (ablation, intra-arterial therapy), cytotoxic chemotherapy, peptide receptor radionuclide therapy (PRRT), and targeted therapies. To maximize therapeutic efficacy while limiting toxicity (both physical and economic), there is a need for accurate and reliable tools to monitor disease evolution and progression and to assess the effectiveness of these treatments. Imaging morphological methods, primarily relying on computed tomography (CT) and magnetic resonance imaging (MRI), are indispensable modalities for the initial evaluation and continuous monitoring of patients with NETs, therefore playing a pivotal role in gauging the response to treatment. The primary goal of assessing tumor response is to anticipate and weigh the benefits of treatments, especially in terms of survival gain. The World Health Organization took the pioneering step of introducing assessment criteria based on cross-sectional imaging. This initial proposal standardized the measurement of lesion sizes, laying the groundwork for subsequent criteria. The Response Evaluation Criteria in Solid Tumors (RECIST) subsequently refined and enhanced these standards, swiftly gaining acceptance within the oncology community. New treatments were progressively introduced, targeting specific features of NETs (such as tumor vascularization or expression of specific receptors), and achieving significant qualitative changes within tumors, although associated with minimal or paradoxical effects on tumor size. Several alternative criteria, adapted from those used in other cancer types and focusing on tumor viability, the slow growth of NETs, or refining the existing size-based RECIST criteria, have been proposed in NETs. This review article aims to describe and discuss the optimal utilization of CT and MRI for assessing the response of NETs to treatment; it provides a comprehensive overview of established and emerging criteria for evaluating tumor response, along with comparative analyses. Molecular imaging will not be addressed here and is covered in a dedicated article within this special issue.

Performance of an automated registration-based method for longitudinal lesion matching and comparison to inter-reader variability

Objective.Patients with metastatic disease are followed throughout treatment with medical imaging, and accurately assessing changes of individual lesions is critical to properly inform clinical decisions. The goal of this work was to assess the performance of an automated lesion-matching algorithm in comparison to inter-reader variability (IRV) of matching lesions between scans of metastatic cancer patients.Approach.Forty pairs of longitudinal PET/CT and CT scans were collected and organized into four cohorts: lung cancers, head and neck cancers, lymphomas, and advanced cancers. Cases were also divided by cancer burden: low-burden (<10 lesions), intermediate-burden (10-29), and high-burden (30+). Two nuclear medicine physicians conducted independent reviews of each scan-pair and manually matched lesions. Matching differences between readers were assessed to quantify the IRV of lesion matching. The two readers met to form a consensus, which was considered a gold standard and compared against the output of an automated lesion-matching algorithm. IRV and performance of the automated method were quantified using precision, recall, F1-score, and the number of differences.Main results.The performance of the automated method did not differ significantly from IRV for any metric in any cohort (p> 0.05, Wilcoxon paired test). In high-burden cases, the F1-score (median [range]) was 0.89 [0.63, 1.00] between the automated method and reader consensus and 0.93 [0.72, 1.00] between readers. In low-burden cases, F1-scores were 1.00 [0.40, 1.00] and 1.00 [0.40, 1.00], for the automated method and IRV, respectively. Automated matching was significantly more efficient than either reader (p< 0.001). In high-burden cases, median matching time for the readers was 60 and 30 min, respectively, while automated matching took a median of 3.9 minSignificance.The automated lesion-matching algorithm was successful in performing lesion matching, meeting the benchmark of IRV. Automated lesion matching can significantly expedite and improve the consistency of longitudinal lesion-matching.

Evaluation of total tumor volume reduction ratio in initially unresectable colorectal liver metastases after first-line systemic treatment

PURPOSE

Total tumor volume (TTV) may play an essential role in the estimation of tumor burden. This study is aimed to investigate the clinical value of the reduction ratio of TTV as a valuable indicator of clinical outcomes in patients with colorectal liver metastases (CRLM).

METHODS

A total of 240 initially unresectable CRLM patients who underwent first-line systemic treatment were enrolled in this study. TTV at baseline and at the end of first-line treatment were assessed using a three-dimensional reconstruction system according to CT or MRI images. Survival was evaluated using Kaplan-Meier analysis and compared using Cox proportional hazard ratios (HR).

RESULTS

A total of 212 (88.3%) patients achieved tumor regression with a median reduction ratio of TTV of 86.0%. An increasing reduction ratio of TTV was associated with a gradually ascending successful conversion outcome. Patients with a reduction ratio >86.0% had better survival than those with a reduction ratio 0-86.0% or <0 (5-year overall survival (OS) rates, 64.4% vs. 44.9% vs. 23.5%, P < 0.001; 5-year progression-free survival (PFS) rates, 36.3% vs. 28.2% vs. 6.5%, P < 0.001). Multivariate analysis indicated that the reduction ratio of TTV ≤ 86.0% (OR [95%CI]: 4.956 [2.654-9.253], P < 0.001) was an independent factor for conversion failure outcome. Cox analyses revealed that the reduction ratio of TTV ≤ 86.0% was an independent factor for both unfavorable OS (HR [95%CI]: 2.216 [1.332-3.688], P = 0.002) and PFS (HR [95%CI]: 2.023 [1.376-2.974], P < 0.001).

CONCLUSIONS

The reduction ratio of TTV was an effective indicator for conversion outcome and long-term prognosis in patients with initially unresectable CRLM after first-line systemic treatment.

Evaluation of the capability and reproducibility of RECIST 1.1. measurements by technologists in breast cancer follow-up: a pilot study

The evaluation of tumor follow-up according to RECIST 1.1 has become essential in clinical practice given its role in therapeutic decision making. At the same time, radiologists are facing an increase in activity while facing a shortage. Radiographic technologists could contribute to the follow-up of these measures, but no studies have evaluated their ability to perform them. Ninety breast cancer patients were performed three CT follow-ups between September 2017 and August 2021. 270 follow-up treatment CT scans were analyzed including 445 target lesions. The rate of agreement of classifications RECIST 1.1 between five technologists and radiologists yielded moderate (k value between 0.47 and 0.52) and substantial (k value = 0.62 and k = 0.67) agreement values. 112 CT were classified as progressive disease (PD) by the radiologists, and 414 new lesions were identified. The analysis showed a percentage of strict agreement of progressive disease classification between reader-technologists and radiologists ranging from substantial to almost perfect agreement (range 73-97%). Analysis of intra-observer agreement was strong at almost perfect (k > 0.78) for 3 technologists. These results are encouraging regarding the ability of selected technologists to perform measurements according to RECIST 1.1 criteria by CT scan with good identification of disease progression.

Breaking down the RECIST 1.1 double read variability in lung trials: What do baseline assessments tell us?

Background

In clinical trials with imaging, Blinded Independent Central Review (BICR) with double reads ensures data blinding and reduces bias in drug evaluations. As double reads can cause discrepancies, evaluations require close monitoring which substantially increases clinical trial costs. We sought to document the variability of double reads at baseline, and variabilities across individual readers and lung trials.

Material and methods

We retrospectively analyzed data from five BICR clinical trials evaluating 1720 lung cancer patients treated with immunotherapy or targeted therapy. Fifteen radiologists were involved. The variability was analyzed using a set of 71 features derived from tumor selection, measurements, and disease location. We selected a subset of readers that evaluated ≥50 patients in ≥two trials, to compare individual reader’s selections. Finally, we evaluated inter-trial homogeneity using a subset of patients for whom both readers assessed the exact same disease locations. Significance level was 0.05. Multiple pair-wise comparisons of continuous variables and proportions were performed using one-way ANOVA and Marascuilo procedure, respectively.

Results

Across trials, on average per patient, target lesion (TL) number ranged 1.9 to 3.0, sum of tumor diameter (SOD) 57.1 to 91.9 mm. MeanSOD=83.7 mm. In four trials, MeanSOD of double reads was significantly different. Less than 10% of patients had TLs selected in completely different organs and 43.5% had at least one selected in different organs. Discrepancies in disease locations happened mainly in lymph nodes (20.1%) and bones (12.2%). Discrepancies in measurable disease happened mainly in lung (19.6%). Between individual readers, the MeanSOD and disease selection were significantly different (p&lt;0.001). In inter-trials comparisons, on average per patient, the number of selected TLs ranged 2.1 to 2.8, MeanSOD 61.0 to 92.4 mm. Trials were significantly different in MeanSOD (p&lt;0.0001) and average number of selected TLs (p=0.007). The proportion of patients having one of the top diseases was significantly different only between two trials for lung. Significant differences were observed for all other disease locations (p&lt;0.05).

Conclusions

We found significant double read variabilities at baseline, evidence of reading patterns and a means to compare trials. Clinical trial reliability is influenced by the interplay of readers, patients and trial design.

Development of a Sensitive Digital Droplet PCR Screening Assay for the Detection of GPR126 Non-Coding Mutations in Bladder Cancer Urine Liquid Biopsies

Recent whole-genome sequencing studies identified two novel recurrent mutations in the enhancer region of GPR126 in urothelial bladder cancer (UBC) tumor samples. This mutational hotspot is the second most common after the TERT promoter in UBC. The aim of the study was to develop a digital droplet PCR screening assay for the simultaneous detection of GPR126 mutations in a single tube. Its performance combined with TERT promoter mutation analysis was evaluated in urine of healthy volunteers (n = 50) and patients with cystitis (n = 22) and UBC (n = 70). The developed assay was validated using DNA constructs carrying the studied variants. None of the mutations were detected in control and cystitis group samples. GPR126 mutations were observed in the urine of 25/70 UBC patients (area under the ROC curve (AUC) of 0.679; mutant allele fraction (MAF) of 21.61 [8.30-44.52] %); TERT mutations-in 40/70 (AUC of 0.786; MAF = 28.29 [19.03-38.08] %); ≥1 mutation-in 47/70 (AUC of 0.836)). The simultaneous presence of GPR126 and TERT mutations was observed in 18/70 cases, with no difference in MAFs for the paired samples (31.96 [14.78-47.49] % vs. 27.13 [17.00-37.62] %, p = 0.349, respectively). The combined analysis of these common non-coding mutations in urine allows the sensitive and non-invasive detection of UBC.

Repeatability of metabolic tumor burden and lesion glycolysis between clinical readers

The Metabolic Tumor Volume (MTV) and Tumor Lesion Glycolysis (TLG) has been shown to be independent prognostic predictors for clinical outcome in Diffuse Large B-cell Lymphoma (DLBCL). However, definitions of these measurements have not been standardized, leading to many sources of variation, operator evaluation continues to be one major source. In this study, we propose a reader reproducibility study to evaluate computation of TMV (& TLG) metrics based on differences in lesion delineation. In the first approach, reader manually corrected regional boundaries after automated detection performed across the lesions in a body scan (Reader M using a manual process, or manual). The other reader used a semi-automated method of lesion identification, without any boundary modification (Reader A using a semi- automated process, or auto). Parameters for active lesion were kept the same, derived from standard uptake values (SUVs) over a 41% threshold. We systematically contrasted MTV & TLG differences between expert readers (Reader M & A). We find that MTVs computed by Readers M and A were both concordant between them (concordant correlation coefficient of 0.96) and independently prognostic with a P-value of 0.0001 and 0.0002 respectively for overall survival after treatment. Additionally, we find TLG for these reader approaches showed concordance (CCC of 0.96) and was prognostic for over -all survival (p ≤ 0.0001 for both). In conclusion, the semi-automated approach (Reader A) provides acceptable quantification & prognosis of tumor burden (MTV) and TLG in comparison to expert reader assisted measurement (Reader M) on PET/CT scans.

Automatic segmentation of hepatic metastases on DWI images based on a deep learning method: assessment of tumor treatment response according to the RECIST 1.1 criteria

BACKGROUND

Evaluation of treated tumors according to Response Evaluation Criteria in Solid Tumors (RECIST) criteria is an important but time-consuming task in medical imaging. Deep learning methods are expected to automate the evaluation process and improve the efficiency of imaging interpretation.

OBJECTIVE

To develop an automated algorithm for segmentation of liver metastases based on a deep learning method and assess its efficacy for treatment response assessment according to the RECIST 1.1 criteria.

METHODS

One hundred and sixteen treated patients with clinically confirmed liver metastases were enrolled. All patients had baseline and post-treatment MR images. They were divided into an initial (n = 86) and validation cohort (n = 30) according to the examined time. The metastatic foci on DWI images were annotated by two researchers in consensus. Then the treatment responses were assessed by the two researchers according to RECIST 1.1 criteria. A 3D U-Net algorithm was trained for automated liver metastases segmentation using the initial cohort. Based on the segmentation of liver metastases, the treatment response was assessed automatically with a rule-based program according to the RECIST 1.1 criteria. The segmentation performance was evaluated using the Dice similarity coefficient (DSC), volumetric similarity (VS), and Hausdorff distance (HD). The area under the curve (AUC) and Kappa statistics were used to assess the accuracy and consistency of the treatment response assessment by the deep learning model and compared with two radiologists [attending radiologist (R1) and fellow radiologist (R2)] in the validation cohort.

RESULTS

In the validation cohort, the mean DSC, VS, and HD were 0.85 ± 0.08, 0.89 ± 0.09, and 25.53 ± 12.11 mm for the liver metastases segmentation. The accuracies of R1, R2 and automated segmentation-based assessment were 0.77, 0.65, and 0.74, respectively, and the AUC values were 0.81, 0.73, and 0.83, respectively. The consistency of treatment response assessment based on automated segmentation and manual annotation was moderate [K value: 0.60 (0.34-0.84)].

CONCLUSION

The deep learning-based liver metastases segmentation was capable of evaluating treatment response according to RECIST 1.1 criteria, with comparable results to the junior radiologist and superior to that of the fellow radiologist.

Quantification and reduction of cross-vendor variation in multicenter DWI MR imaging: results of the Cancer Core Europe imaging task force

OBJECTIVES

In the Cancer Core Europe Consortium (CCE), standardized biomarkers are required for therapy monitoring oncologic multicenter clinical trials. Multiparametric functional MRI and particularly diffusion-weighted MRI offer evident advantages for noninvasive characterization of tumor viability compared to CT and RECIST. A quantification of the inter- and intraindividual variation occurring in this setting using different hardware is missing. In this study, the MRI protocol including DWI was standardized and the residual variability of measurement parameters quantified.

METHODS

Phantom and volunteer measurements (single-shot T2w and DW-EPI) were performed at the seven CCE sites using the MR hardware produced by three different vendors. Repeated measurements were performed at the sites and across the sites including a traveling volunteer, comparing qualitative and quantitative ROI-based results including an explorative radiomics analysis.

RESULTS

For DWI/ADC phantom measurements using a central post-processing algorithm, the maximum deviation could be decreased to 2%. However, there is no significant difference compared to a decentralized ADC value calculation at the respective MRI devices. In volunteers, the measurement variation in 2 repeated scans did not exceed 11% for ADC and is below 20% for single-shot T2w in systematic liver ROIs. The measurement variation between sites amounted to 20% for ADC and < 25% for single-shot T2w. Explorative radiomics classification experiments yield better results for ADC than for single-shot T2w.

CONCLUSION

Harmonization of MR acquisition and post-processing parameters results in acceptable standard deviations for MR/DW imaging. MRI could be the tool in oncologic multicenter trials to overcome the limitations of RECIST-based response evaluation.

KEY POINTS

• Harmonizing acquisition parameters and post-processing homogenization, standardized protocols result in acceptable standard deviations for multicenter MR-DWI studies. • Total measurement variation does not to exceed 11% for ADC in repeated measurements in repeated MR acquisitions, and below 20% for an identical volunteer travelling between sites. • Radiomic classification experiments were able to identify stable features allowing for reliable discrimination of different physiological tissue samples, even when using heterogeneous imaging data.

Primary cutaneous lymphoma: recommendations for clinical trial design and staging update from the ISCL, USCLC, and EORTC

The number of patients with primary cutaneous lymphoma (PCL) relative to other non-Hodgkin lymphomas (NHLs) is small and the number of subtypes large. Although clinical trial guidelines have been published for mycosis fungoides/Sézary syndrome, the most common type of PCL, none exist for the other PCLs. In addition, staging of the PCLs has been evolving based on new data on potential prognostic factors, diagnosis, and assessment methods of both skin and extracutaneous disease and a desire to align the latter with the Lugano guidelines for all NHLs. The International Society for Cutaneous Lymphomas (ISCL), the United States Cutaneous LymphomaConsortium (USCLC), and the Cutaneous Lymphoma Task Force of the European Organization for the Research and Treatment of Cancer (EORTC) now propose updated staging and guidelines for the study design, assessment, endpoints, and response criteria in clinical trials for all the PCLs in alignment with that of the Lugano guidelines. These recommendations provide standardized methodology that should facilitate planning and regulatory approval of new treatments for these lymphomas worldwide, encourage cooperative investigator-initiated trials, and help to assess the comparative efficacy of therapeutic agents tested across sites and studies.

Reducing number of target lesions for RECIST1.1 to predict survivals in patients with advanced non-small-cell lung cancer undergoing anti-PD1/PD-L1 monotherapy

OBJECTIVES

The Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 provides conventional and standardized response assessment for multiple solid tumors. We investigated the smallest number of target lesions that can be measured without compromising response categorization and survival prediction in patients with advanced non-small-cell lung cancer (aNSCLC) undergoing anti-PD-1/PD-L1 monotherapy.

MATERIAL AND METHODS

125 aNSCLC patients with at least two measurable lesions undergoing PD-1/PD-L1 inhibitor treatment were retrospectively studied. Tumor measurements allowing up to two lesions per organ and five lesions in total were reviewed. Inter-individual agreement and κ values for inter-method concordance on response status were evaluated based on up to five target lesions versus the largest one through four lesions. C-index was calculated to evaluate the prognostic accuracy of response categorization based on the selected number of target lesions for predicting overall survival (OS). Cox regression analysis was conducted for survival analysis.

RESULTS

The highly consistent response assignment (99.2%) could be obtained when measuring the largest two lesions versus up to five lesions. Using the largest two through four lesions produced κ values of 0.986, 1.000 and 1.000 for response assessment, values significantly higher than those obtained when measuring the largest single lesion (κ = 0.850). C-index for overall survival (OS) was similar when assessing the largest one through five lesions, ranging from 0.646 to 0.654. Cox regression analyses showed that radiological response significantly predicted OS, irrespective of the number of target lesions selected.

CONCLUSIONS

Reducing the number of target lesions does not affect OS prediction in aNSCLC patients treated with anti-PD-1/PD-L1 therapy. Considering the high intra-individual and inter-method concordance, using the largest two lesions in total is proposed to assess response.

An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer

Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.

Exploring the Interobserver Agreement in Computer-Aided Radiologic Tumor Measurement and Evaluation of Tumor Response

The accurate, objective, and reproducible evaluation of tumor response to therapy is indispensable in clinical trials. This study aimed at investigating the reliability and reproducibility of a computer-aided contouring (CAC) tool in tumor measurements and its impact on evaluation of tumor response in terms of RECIST 1.1 criteria. A total of 200 cancer patients were retrospectively collected in this study, which were randomly divided into two sets of 100 patients for experiential learning and testing. A total of 744 target lesions were identified by a senior radiologist in distinctive body parts, of which 278 lesions were in data set 1 (learning set) and 466 lesions were in data set 2 (testing set). Five image analysts were respectively instructed to measure lesion diameter using manual and CAC tools in data set 1 and subsequently tested in data set 2. The interobserver variability of tumor measurements was validated by using the coefficient of variance (CV), the Pearson correlation coefficient (PCC), and the interobserver correlation coefficient (ICC). We verified that the mean CV of manual measurement remained constant between the learning and testing data sets (0.33 vs. 0.32, p = 0.490), whereas it decreased for the CAC measurements after learning (0.24 vs. 0.19, p < 0.001). The interobserver measurements with good agreement (CV < 0.20) were 29.9% (manual) vs. 49.0% (CAC) in the learning set (p < 0.001) and 30.9% (manual) vs. 64.4% (CAC) in the testing set (p < 0.001). The mean PCCs were 0.56 ± 0.11 mm (manual) vs. 0.69 ± 0.10 mm (CAC) in the learning set (p = 0.013) and 0.73 ± 0.07 mm (manual) vs. 0.84 ± 0.03 mm (CAC) in the testing set (p < 0.001). ICCs were 0.633 (manual) vs. 0.698 (CAC) in the learning set (p < 0.001) and 0.716 (manual) vs. 0.824 (CAC) in the testing set (p < 0.001). The Fleiss’ kappa analysis revealed that the overall agreement was 58.7% (manual) vs. 58.9% (CAC) in the learning set and 62.9% (manual) vs. 74.5% (CAC) in the testing set. The 80% agreement of tumor response evaluation was 55.0% (manual) vs. 66.0% in the learning set and 60.6% (manual) vs. 79.7% (CAC) in the testing set. In conclusion, CAC can reduce the interobserver variability of radiological tumor measurements and thus improve the agreement of imaging evaluation of tumor response.

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.

An Expandable Informatics Framework for Enhancing Central Cancer Registries with Digital Pathology Specimens, Computational Imaging Tools, and Advanced Mining Capabilities

BACKGROUND

Population-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case that is reported across all U.S. states and territories. The goal of our project is to enrich NCI's Surveillance, Epidemiology, and End Results (SEER) registry data with high-quality population-based biospecimen data in the form of digital pathology, machine-learning-based classifications, and quantitative histopathology imaging feature sets (referred to here as Pathomics features).

MATERIALS AND METHODS

As part of the project, the underlying informatics infrastructure was designed, tested, and implemented through close collaboration with several participating SEER registries to ensure consistency with registry processes, computational scalability, and ability to support creation of population cohorts that span multiple sites. Utilizing computational imaging algorithms and methods to both generate indices and search for matches makes it possible to reduce inter- and intra-observer inconsistencies and to improve the objectivity with which large image repositories are interrogated.

RESULTS

Our team has created and continues to expand a well-curated repository of high-quality digitized pathology images corresponding to subjects whose data are routinely collected by the collaborating registries. Our team has systematically deployed and tested key, visual analytic methods to facilitate automated creation of population cohorts for epidemiological studies and tools to support visualization of feature clusters and evaluation of whole-slide images. As part of these efforts, we are developing and optimizing advanced search and matching algorithms to facilitate automated, content-based retrieval of digitized specimens based on their underlying image features and staining characteristics.

CONCLUSION

To meet the challenges of this project, we established the analytic pipelines, methods, and workflows to support the expansion and management of a growing repository of high-quality digitized pathology and information-rich, population cohorts containing objective imaging and clinical attributes to facilitate studies that seek to discriminate among different subtypes of disease, stratify patient populations, and perform comparisons of tumor characteristics within and across patient cohorts. We have also successfully developed a suite of tools based on a deep-learning method to perform quantitative characterizations of tumor regions, assess infiltrating lymphocyte distributions, and generate objective nuclear feature measurements. As part of these efforts, our team has implemented reliable methods that enable investigators to systematically search through large repositories to automatically retrieve digitized pathology specimens and correlated clinical data based on their computational signatures.

Natural speed of growth of untreated soft-tissue sarcomas: A dimension-based imaging analysis

PURPOSE

The interval from first symptoms to diagnosis, staging and referral to reference center can last months for soft-tissue sarcoma (STS) patients. Meanwhile, patients can undergo different imaging that capture the 'natural' tumor changes, before medical intervention. Aim was to depict these 'natural' dimensional variations and to correlate them with patients' outcome.

METHODS

Single-center retrospective study including all consecutive adults with newly-diagnosed STS, ≥2 pre-treatment imaging (CT-scan or MRI) on the tumor (Exam-0 and Exam-1), and managed in reference center between 2007 and 2018. Longest diameter (LD) and volume were calculated on both examinations to obtain the naïve dimensional growth before any intervention. SARCULATOR nomogram was applied on data at Exam-0 and Exam-1. Correlations with overall, metastatic and local relapse-free survivals (OS, MFS and LFS), and with pre-treatment pathological features were performed.

RESULTS

137 patients were included (median age: 65 years). Average naïve growth was +39.4% in LD and +503% in volume during an average Exam-0-to-Exam-1 interval of 130 days. The 10-year distant metastasis and OS predictions were different at Exam-0 and Exam-1 (P < 0.0001 for both). All the changes in radiological measurements significantly correlated with pre-treatment number of mitosis, grade and complex genomic (P-value range: <0.0001-0.0481). Multivariate Cox modeling identified the relative change in LD/month and absolute change in LD/month as independent predictors for OS and LFS, respectively (P = 0.0003 and 0.0001, respectively).

CONCLUSION

When available, the natural speed of growth on pre-treatment imaging should be evaluated to improve the estimation of pre-treatment histological grade and patients' OS and LFS.

The Prognostic Value of Total Tumor Volume Response Compared With RECIST1.1 in Patients With Initially Unresectable Colorectal Liver Metastases Undergoing Systemic Treatment

Objectives

Compare total tumor volume (TTV) response after systemic treatment to Response Evaluation Criteria in Solid Tumors (RECIST1.1) and assess the prognostic value of TTV change and RECIST1.1 for recurrence-free survival (RFS) in patients with colorectal liver-only metastases (CRLM).

Background

RECIST1.1 provides unidimensional criteria to evaluate tumor response to systemic therapy. Those criteria are accepted worldwide but are limited by interobserver variability and ignore potentially valuable information about TTV.

Methods

Patients with initially unresectable CRLM receiving systemic treatment from the randomized, controlled CAIRO5 trial (NCT02162563) were included. TTV response was assessed using software specifically developed together with SAS analytics. Baseline and follow-up computed tomography (CT) scans were used to calculate RECIST1.1 and TTV response to systemic therapy. Different thresholds (10%, 20%, 40%) were used to define response of TTV as no standard currently exists. RFS was assessed in a subgroup of patients with secondarily resectable CRLM after induction treatment.

Results

A total of 420 CT scans comprising 7820 CRLM in 210 patients were evaluated. In 30% to 50% (depending on chosen TTV threshold) of patients, discordance was observed between RECIST1.1 and TTV change. A TTV decrease of >40% was observed in 47 (22%) patients who had stable disease according to RECIST1.1. In 118 patients with secondarily resectable CRLM, RFS was shorter for patients with less than 10% TTV decrease compared with patients with more than 10% TTV decrease (P = 0.015), while RECIST1.1 was not prognostic (P = 0.821).

Conclusions

TTV response assessment shows prognostic potential in the evaluation of systemic therapy response in patients with CRLM.

End-to-End Non-Small-Cell Lung Cancer Prognostication Using Deep Learning Applied to Pretreatment Computed Tomography

PURPOSE

Clinical TNM staging is a key prognostic factor for patients with lung cancer and is used to inform treatment and monitoring. Computed tomography (CT) plays a central role in defining the stage of disease. Deep learning applied to pretreatment CTs may offer additional, individualized prognostic information to facilitate more precise mortality risk prediction and stratification.

METHODS

We developed a fully automated imaging-based prognostication technique (IPRO) using deep learning to predict 1-year, 2-year, and 5-year mortality from pretreatment CTs of patients with stage I-IV lung cancer. Using six publicly available data sets from The Cancer Imaging Archive, we performed a retrospective five-fold cross-validation using pretreatment CTs of 1,689 patients, of whom 1,110 were diagnosed with non-small-cell lung cancer and had available TNM staging information. We compared the association of IPRO and TNM staging with patients' survival status and assessed an Ensemble risk score that combines IPRO and TNM staging. Finally, we evaluated IPRO's ability to stratify patients within TNM stages using hazard ratios (HRs) and Kaplan-Meier curves.

RESULTS

IPRO showed similar prognostic power (concordance index [C-index] 1-year: 0.72, 2-year: 0.70, 5-year: 0.68) compared with that of TNM staging (C-index 1-year: 0.71, 2-year: 0.71, 5-year: 0.70) in predicting 1-year, 2-year, and 5-year mortality. The Ensemble risk score yielded superior performance across all time points (C-index 1-year: 0.77, 2-year: 0.77, 5-year: 0.76). IPRO stratified patients within TNM stages, discriminating between highest- and lowest-risk quintiles in stages I (HR: 8.60), II (HR: 5.03), III (HR: 3.18), and IV (HR: 1.91).

CONCLUSION

Deep learning applied to pretreatment CT combined with TNM staging enhances prognostication and risk stratification in patients with lung cancer.

Dual-Energy CT Vital Iodine Tumor Burden for Response Assessment in Patients With Metastatic GIST Undergoing TKI Therapy: Comparison to Standard CT and FDG PET/CT Criteria

Background: CT-based criteria for assessing gastroinstestinal stromal tumor (GIST) response to tyroskine kinase inhibitor (TKI) therapy are limited partly because tumor attenuation is influenced by treatment-related changes including hemorrhage and calcification. Iodine concentration may be less impacted by such changes. Objective: To determine whether DECT vital iodine tumor burden (TB) provides improved differentiation between responders and non-responders in patients with metastatic GIST undergoing TKI therapy compared to established CT and PET/CT criteria. Methods: An anthropomorphic phantom with spherical inserts mimicking GIST lesions of varying iodine concentrations and having non-enhancing central necrotic cores underwent DECT to determine a threshold iodine concentration. Forty patients (median age 57 years; 25 women, 15 men) treated with TKI for metaststic GIST were retrospectively evaluated. Patients underwent baseline and follow-up DECT and FDG PET/CT. Response assessment was performed using RECIST 1.1, modified Choi (mChoi), vascular tumor burden (VTB), DECT vital iodine TB, and European Organization for Research and Treatment of Cancer (EORTC PET) criteria. DECT vital iodine TB used the same percentage changes as RECIST 1.1 response categories. Progression-free survival (PFS) was compared between responders and non-responders for each response criteria using Cox proportional hazard ratios and Harrell's c-indices. Results: The phantom experiment identified a 0.5 mg/mL threshold to differentiate vital from non-vital tissue. Using DECT vital iodine TB, median PFS was significantly different between non-responders and responders (587 vs 167 days, respectively; p=.02). Hazard ratio for progression for DECT vital iodine TB non-responders versus responders was 6.9, versus 7.6 for EORTC PET, 3.3 for VTB, 2.3 for RECIST 1.1, and 2.1 for mChoi. C-index was 0.74 for EORTC PET, 0.73 for DECT vital iodine TB, 0.67 for VTB, 0.61 for RECIST 1.1, and 0.58 for mChoi. C-index was significantly greater for DECT vital iodine TB than RECIST 1.1 (p=.02) and mChoi (p=.002), but not different than VTB and EORTC PET (p>.05). Conclusion: DECT vital iodine TB criteria showed comparable performance as EORTC PET and outperformed RECIST 1.1 and mChoi for response assessment of metastatic GIST under TKI therapy. Clinical Impact: DECT vital iodine TB could help guide early management decisions in patients on TKI therapy.

Blinded Independent Central Review (BICR) in New Therapeutic Lung Cancer Trials

BACKGROUND

Double reads in blinded independent central reviews (BICRs) are recommended to control the quality of trials but they are prone to discordances. We analyzed inter-reader discordances in a pool of lung cancer trials using RECIST 1.1.

METHODS

We analyzed six lung cancer BICR trials that included 1833 patients (10,684 time points) involving 17 radiologists. We analyzed the rate of discrepancy of each trial at the time-point and patient levels as well as testing inter-trial differences. The analysis of adjudication made it possible to compute the readers' endorsement rates, the root causes of adjudications, and the proportions of "errors" versus "medically justifiable differences".

RESULTS

The trials had significantly different discrepancy rates both at the time-point (average = 34.3%) and patient (average = 59.2%) levels. When considering only discrepancies for progressive disease, homogeneous discrepancy rates were found with an average of 32.9%, while readers' endorsement rates ranged between 27.7% and 77.8%. Major causes of adjudication were different per trial, with medically justifiable differences being the most common, triggering 74.2% of total adjudications.

CONCLUSIONS

We provide baseline performances for monitoring reader performance in trials with double reads. Intelligent reading system implementation along with appropriate reader training and monitoring are solutions that could mitigate a large portion of the commonly encountered reading errors.

Imaging biomarkers for evaluating tumor response: RECIST and beyond

Response Evaluation Criteria in Solid Tumors (RECIST) is the gold standard for assessment of treatment response in solid tumors. Morphologic change of tumor size evaluated by RECIST is often correlated with survival length and has been considered as a surrogate endpoint of therapeutic efficacy. However, the detection of morphologic change alone may not be sufficient for assessing response to new anti-cancer medication in all solid tumors. During the past fifteen years, several molecular-targeted therapies and immunotherapies have emerged in cancer treatment which work by disrupting signaling pathways and inhibited cell growth. Tumor necrosis or lack of tumor progression is associated with a good therapeutic response even in the absence of tumor shrinkage. Therefore, the use of unmodified RECIST criteria to estimate morphological changes of tumor alone may not be sufficient to estimate tumor response for these new anti-cancer drugs. Several studies have reported the low reliability of RECIST in evaluating treatment response in different tumors such as hepatocellular carcinoma, lung cancer, prostate cancer, brain glioma, bone metastasis, and lymphoma. There is an increased need for new medical imaging biomarkers, considering the changes in tumor viability, metabolic activity, and attenuation, which are related to early tumor response. Promising imaging techniques, beyond RECIST, include dynamic contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI), diffusion-weight imaging (DWI), magnetic resonance spectroscopy (MRS), and ¹⁸ F-fluorodeoxyglucose (FDG) positron emission tomography (PET). This review outlines the current RECIST with their limitations and the new emerging concepts of imaging biomarkers in oncology.

Deep learning for semi-automated unidirectional measurement of lung tumor size in CT

BACKGROUND

Performing Response Evaluation Criteria in Solid Tumor (RECISTS) measurement is a non-trivial task requiring much expertise and time. A deep learning-based algorithm has the potential to assist with rapid and consistent lesion measurement.

PURPOSE

The aim of this study is to develop and evaluate deep learning (DL) algorithm for semi-automated unidirectional CT measurement of lung lesions.

METHODS

This retrospective study included 1617 lung CT images from 8 publicly open datasets. A convolutional neural network was trained using 1373 training and validation images annotated by two radiologists. Performance of the DL algorithm was evaluated 244 test images annotated by one radiologist. DL algorithm's measurement consistency with human radiologist was evaluated using Intraclass Correlation Coefficient (ICC) and Bland-Altman plotting. Bonferroni's method was used to analyze difference in their diagnostic behavior, attributed by tumor characteristics. Statistical significance was set at p < 0.05.

RESULTS

The DL algorithm yielded ICC score of 0.959 with human radiologist. Bland-Altman plotting suggested 240 (98.4 %) measurements realized within the upper and lower limits of agreement (LOA). Some measurements outside the LOA revealed difference in clinical reasoning between DL algorithm and human radiologist. Overall, the algorithm marginally overestimated the size of lesion by 2.97 % compared to human radiologists. Further investigation indicated tumor characteristics may be associated with the DL algorithm's diagnostic behavior of over or underestimating the lesion size compared to human radiologist.

CONCLUSIONS

The DL algorithm for unidirectional measurement of lung tumor size demonstrated excellent agreement with human radiologist.

RECIST 1.1 and lesion selection: How to deal with ambiguity at baseline?

Response Evaluation Criteria In Solid Tumors (RECIST) is still the predominant criteria base for assessing tumor burden in oncology clinical trials. Despite several improvements that followed its first publication, RECIST continues to allow readers a lot of freedom in their evaluations. Notably in the selection of tumors at baseline. This subjectivity is the source of many suboptimal evaluations. When starting a baseline analysis, radiologists cannot always identify tumor malignancy with any certainty. Also, with RECIST, some findings can be deemed equivocal by radiologists with no confirmatory ground truth to rely on. In the specific case of Blinded Independent Central Review clinical trials with double reads using RECIST, the selection of equivocal tumors can have two major consequences: inter-reader variability and modified sensitivity of the therapeutic response. Apart from the main causes leading to the selection of an equivocal lesion, due to the uncertainty of the radiological characteristics or due to the censoring of on-site evaluations, several other situations can be described more precisely. These latter involve cases where an equivocal is selected as target or non-target lesions, the management of equivocal lymph nodes and the case of few target lesions. In all cases, awareness of the impact of selecting a non-malignant lesion will lead radiologists to make selections in the most rational way. Also, in clinical trials where the primary endpoint differs between phase 2 (response-related) and phase 3 (progression-related) trials, our impact analysis will help them to devise strategies for the management of equivocal lesions.

Tumor Growth Rate to Predict the Outcome of Patients with Neuroendocrine Tumors: Performance and Sources of Variability

INTRODUCTION

Tumor growth rate (TGR), percentage of change in tumor volume/month, has been previously identified as an early radiological biomarker for treatment monitoring in neuroendocrine tumor (NET) patients. We assessed the performance and reproducibility of TGR at 3 months (TGR3m) as a predictor factor of progression-free survival (PFS), including the impact of imaging method and reader variability.

METHODS

Baseline and 3-month (±1 month) CT/MRI images from patients with advanced, grade 1-2 NETs were retrospectively reviewed by 2 readers. Influence of number of targets, tumor burden, and location of lesion on the performance of TGR3m to predict PFS was assessed by uni/multivariable Cox regression analysis. Agreement between readers was assessed by Lin's concordance coefficient (LCC) and kappa coefficient (KC).

RESULTS

A total of 790 lesions were measured in 222 patients. Median PFS was 22.9 months. On univariable analysis, number of lesions (</≥4), tumor burden, and presence of liver metastases were significantly correlated with PFS. On multivariate analysis, ≥4 lesions (HR: 1.89 [95% CI: 1.01-3.57]), TGR3m ≥0.8%/month (HR: 4.01 [95% CI: 2.31-6.97]), and watch and wait correlated with shorter PFS. No correlation was found between TGR3m and number of lesions (rho: -0.2; p value: 0.1930). No difference in mean TGR3m across organs was shown (p value: 0.6). Concordance between readers was acceptable (LCC: 0.52 [95% CI: 0.38-0.65]; KC: 0.57, agreement: 81.55%). TGR3m remained a significant prognostic factor when data from the second reader were employed (HR: 4.35 [95% CI: 2.44-7.79]; p value <0.001) regardless his expertise (HR: 1.21 [95% CI: 0.70-2.09]; p value: 0.493).

DISCUSSION/CONCLUSION

TGR3m is a robust and early radiological biomarker able to predict PFS. It may be used to identify patients with advanced NETs who require closer radiological follow-up.

Excellent outcome of young children with nodular desmoplastic medulloblastoma treated on "Head Start" III: a multi-institutional, prospective clinical trial

BACKGROUND

"Head Start" III, was a prospective clinical trial using intensive induction followed by myeloablative chemotherapy and autologous hematopoietic cell rescue (AuHCR) to either avoid or reduce the dose/volume of irradiation in young children with medulloblastoma.

METHODS

Following surgery, patients received 5 cycles of induction followed by myeloablative chemotherapy using carboplatin, thiotepa, and etoposide with AuHCR. Irradiation was reserved for children >6 years old at diagnosis or with residual tumor post-induction.

RESULTS

Between 2003 and 2009, 92 children <10 years old with medulloblastoma were enrolled. Five-year event-free survival (EFS) and overall survival (OS) rates (±SE) were 46 ± 5% and 62 ± 5% for all patients, 61 ± 8% and 77 ± 7% for localized medulloblastoma, and 35 ± 7% and 52 ± 7% for disseminated patients. Nodular/desmoplastic (ND) medulloblastoma patients had 5-year EFS and OS (±SE) rates of 89 ± 6% and 89 ± 6% compared with 26 ± 6% and 53 ± 7% for classic and 38 ± 13% and 46 ± 14% for large-cell/anaplastic (LCA) medulloblastoma, respectively. In multivariate Cox regression analysis, histology was the only significant independent predictor of EFS after adjusting for stage, extent of resection, regimen, age, and sex (P <0.0001). Five-year irradiation-free EFS was 78 ± 8% for ND and 21 ± 5% for classic/LCA medulloblastoma patients. Myelosuppression was the most common toxicity, with 2 toxic deaths. Twenty-four survivors completed neurocognitive evaluation at a mean of 4.9 years post-diagnosis. IQ and memory scores were within average range overall, whereas processing speed and adaptive functioning were low-average.

CONCLUSION

We report excellent survival and preservation of mean IQ and memory for young children with ND medulloblastoma using high-dose chemotherapy, with most patients surviving without irradiation.

Radiological Assessment and Outcome of Local Disease Progression after Neoadjuvant Chemotherapy in Children and Adolescents with Localized Osteosarcoma

Objective: We examined the interobserver reliability of local progressive disease (L-PD) determination using two major radiological response evaluation criteria systems (Response evaluation Criteria in Solid Tumors (RECIST) and the European and American Osteosarcoma Study (EURAMOS)) in patients diagnosed with localized osteosarcoma (OS). Additionally, we describe the outcomes of patients determined to experience L-PD. Materials and Methods: Forty-seven patients diagnosed with localized OS between 2000 and 2012 at our institution were identified. Paired magnetic resonance imaging of the primary tumor from diagnosis and post-neoadjuvant chemotherapy were blindly assessed by two experienced radiologists and determined L-PD as per RECIST and EURAMOS radiological criteria. Interobserver reliability was measured using the kappa statistic (κ). The Kaplan Meier method and log-rank test was used to assess differences between groups. Results: Of 47 patients (median age at diagnosis 12.9 years), 16 (34%) had L-PD (by RECIST or EURAMOS radiological definition). There was less agreement between the radiologists using EURAMOS radiological criteria for L-PD (80.9%, κ = 0.48) than with RECIST criteria (97.9%, κ = 0.87). Patients with radiologically defined L-PD had a 5-year progression-free survival (PFS) of 55.6%, compared to a 5 year-PFS of 82.7% in the group of patients without L-PD (n = 31) (Log rank p = 0.0185). Conclusions: The interobserver reliability of L-PD determination is higher using RECIST than EURAMOS. RECIST can be considered for response assessment in OS clinical trials. The presence of L-PD was associated with worse outcomes.

Comparative Analysis of Artery Anatomy Evaluated by Postmortem Tomography, CT Angiography, and Postmortem and Predeath CT Scans

BACKGROUND

To date, no study has been performed analyzing changes in the vascular system comparing paired examinations of patients alive and after death with the use of cardiopulmonary bypass and computed tomography (CT) angiography.

MATERIALS AND METHODS

The aim of this study was to analyze in a large series (38 patients) the aorta and its branches by CT (without contrast) and CT angiography of patients still alive and after death comparing their diameters and length variations.

RESULTS

The variation between in vivo tomography and virtopsy methods was greater in the evaluation of distances between vascular segments than in the diameters; less than 30% of the distances evaluated in the entire study had acceptable variation between methods, regardless of the use of contrast scans. We observed better repeatability rates in the comparison between in vivo and postmortem contrast-enhanced examinations. Comparing the examinations of the still alive individuals with the contrast-enhanced tomography after death, we observed a higher concordance rate. The best variations between the methods were observed in the evaluation of the diameters in the contrast-enhanced examination of the ascending aorta, aortic arch, thoracic aorta, and thoracoabdominal transition.

CONCLUSIONS

The measurements obtained in postmortem angiography images partially reflect the vascular anatomy of the main branches in the thoracoabdominal region in vivo. However, postmortem CT without contrast was not performed in the same comparison. We believe that adjustments to the contrast injection technique may eventually improve these results.

Volumetric tumor response assessment is inefficient without overt clinical benefit compared to conventional, manual veterinary response assessment in canine nasal tumors

Accurate assessment of tumor response to therapy is critical in guiding management of veterinary oncology patients and is most commonly performed using response evaluation criteria in solid tumors criteria. This process can be time consuming and have high intra- and interobserver variability. The primary aim of this serial measurements, secondary analysis study was to compare manual linear tumor response assessment to semi-automated, contoured response assessment in canine nasal tumors. The secondary objective was to determine if tumor measurements or clinical characteristics, such as stage, would correlate to progression-free interval. Three investigators evaluated paired CT scans of skulls of 22 dogs with nasal tumors obtained prior to and following radiation therapy. The automatically generated tumor volumes were not useful for canine nasal tumors in this study, characterized by poor intraobserver agreement between automatically generated contours and hand-adjusted contours. The radiologist's manual linear method of determining response evaluation criteria in solid tumors categorization and tumor volume is significantly faster (P < .0001) but significantly underestimates nasal tumor volume (P < .05) when compared to a contour-based method. Interobserver agreement was greater for volume determination using the contour-based method when compared to response evaluation criteria in solid tumors categorization utilizing the same method. However, response evaluation criteria in solid tumors categorization and percentage volume change were strongly correlated, providing validity to response evaluation criteria in solid tumors as a rapid method of tumor response assessment for canine nasal tumors. No clinical characteristics or tumor measurements were significantly associated with progression-free interval.

Impact of tumour size measurement inter-operator variability on model-based drug effect evaluation

Purpose

During oncology clinical trials, tumour size (TS) measurements are commonly used to monitor disease progression and to assess drug efficacy. We explored inter-operator variability within a subset of a phase III clinical trial conducted from August 1995 to February 1997 and its impact on drug effect evaluation using a tumour growth inhibition model.

Methods

One hundred twenty lesions were measured twice at each time point; once at the hospital and once at the centralised centre. A visual analysis was performed to identify trends within the profiles over time. Linear regression and relative error ratios were used to explore the inter-operator variability of raw TS measurements and model-based estimates.

Results

While correlation between patient-level estimates of drug effect was poor (r² = 0.28), variability between the study-level estimates was much less affected (9%).

Conclusions

The global evaluation of drug effect using modelling approaches might not be affected by inter-operator variability. However, the exploration of covariates for drug effect and the characterisation of an exposure–tumour shrinkage relationship seems limited by the high measurement variability that translates to a poor correlation of individual drug effect estimates. This might be addressed by the use of more precise computer-aided measurement methods.

Electronic supplementary material

The online version of this article (10.1007/s00280-020-04049-5) contains supplementary material, which is available to authorized users.

Quantitative Imaging Assessment for Clinical Trials in Oncology

BACKGROUND

Objective radiographic assessment is crucial for accurately evaluating therapeutic efficacy and patient outcomes in oncology clinical trials. Imaging assessment workflow can be complex; can vary with institution; may burden medical oncologists, who are often inadequately trained in radiology and response criteria; and can lead to high interobserver variability and investigator bias. This article reviews the development of a tumor response assessment core (TRAC) at a comprehensive cancer center with the goal of providing standardized, objective, unbiased tumor imaging assessments, and highlights the web-based platform and overall workflow. In addition, quantitative response assessments by the medical oncologists, radiologist, and TRAC are compared in a retrospective cohort of patients to determine concordance.

PATIENTS AND METHODS

The TRAC workflow includes an image analyst who pre-reviews scans before review with a board-certified radiologist and then manually uploads annotated data on the proprietary TRAC web portal. Patients previously enrolled in 10 lung cancer clinical trials between January 2005 and December 2015 were identified, and the prospectively collected quantitative response assessments by the medical oncologists were compared with retrospective analysis of the same dataset by a radiologist and TRAC.

RESULTS

This study enlisted 49 consecutive patients (53% female) with a median age of 60 years (range, 29-78 years); 2 patients did not meet study criteria and were excluded. A linearly weighted kappa test for concordance for TRAC versus radiologist was substantial at 0.65 (95% CI, 0.46-0.85; standard error [SE], 0.10). The kappa value was moderate at 0.42 (95% CI, 0.20-0.64; SE, 0.11) for TRAC versus oncologists and only fair at 0.34 (95% CI, 0.12-0.55; SE, 0.11) for oncologists versus radiologist.

CONCLUSIONS

Medical oncologists burdened with the task of tumor measurements in patients on clinical trials may introduce significant variability and investigator bias, with the potential to affect therapeutic response and clinical trial outcomes. Institutional imaging cores may help bridge the gap by providing unbiased and reproducible measurements and enable a leaner workflow.