Towards volumetric thresholds in RECIST 1.1: Therapeutic response assessment in hepatic metastases

Comparing quantitative imaging biomarker alliance volumetric CT classifications with RECIST response categories

Purpose

To assess agreement between CT volumetry change classifications derived from Quantitative Imaging Biomarker Alliance Profile cut-points (ie, QIBA CTvol classifications) and the Response Evaluation Criteria in Solid Tumors (RECIST) categories.

Materials and Methods

Target lesions in lung, liver, and lymph nodes were randomly chosen from patients in 10 historical clinical trials for various cancers, ensuring a balanced representation of lesion types, diameter ranges described in the QIBA Profile, and variations in change magnitudes. Three radiologists independently segmented these lesions at baseline and follow-up scans using 2 software tools. Two types of predefined disagreements were assessed: Type I: substantive disagreement, where the disagreement between QIBA CTvol classifications and RECIST categories could not be attributed to the improved sensitivity of volumetry in detecting changes; and Type II: disagreement potentially arising from the improved sensitivity of volumetry in detecting changes. The proportion of lesions with disagreements between QIBA CTvol and RECIST, as well as the type of disagreements, was reported along with 95% CIs, both overall and within subgroups representing various factors.

Results

A total of 2390 measurements from 478 lesions (158 lungs, 170 livers, 150 lymph nodes) in 281 patients were included. QIBA CTvol agreed with RECIST in 66.6% of interpretations. Of the 33.4% of interpretations with discrepancies, substantive disagreement (Type I) occurred in only 1.5% (95% CI: [0.8%, 2.1%]). Factors such as scanner vendor (P = .584), segmentation tool (P = .331), and lesion type (P = .492) were not significant predictors of disagreement. Significantly more disagreements were observed for larger lesions (≥50 mm, as defined in the QIBA Profile).

Conclusion

We conclude that QIBA CTvol classifications agree with RECIST categories.

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.

Improving assessment of lesions in longitudinal CT scans: a bi-institutional reader study on an AI-assisted registration and volumetric segmentation workflow

PURPOSE

AI-assisted techniques for lesion registration and segmentation have the potential to make CT-based tumor follow-up assessment faster and less reader-dependent. However, empirical evidence on the advantages of AI-assisted volumetric segmentation for lymph node and soft tissue metastases in follow-up CT scans is lacking. The aim of this study was to assess the efficiency, quality, and inter-reader variability of an AI-assisted workflow for volumetric segmentation of lymph node and soft tissue metastases in follow-up CT scans. Three hypotheses were tested: (H1) Assessment time for follow-up lesion segmentation is reduced using an AI-assisted workflow. (H2) The quality of the AI-assisted segmentation is non-inferior to the quality of fully manual segmentation. (H3) The inter-reader variability of the resulting segmentations is reduced with AI assistance.

MATERIALS AND METHODS

The study retrospectively analyzed 126 lymph nodes and 135 soft tissue metastases from 55 patients with stage IV melanoma. Three radiologists from two institutions performed both AI-assisted and manual segmentation, and the results were statistically analyzed and compared to a manual segmentation reference standard.

RESULTS

AI-assisted segmentation reduced user interaction time significantly by 33% (222 s vs. 336 s), achieved similar Dice scores (0.80-0.84 vs. 0.81-0.82) and decreased inter-reader variability (median Dice 0.85-1.0 vs. 0.80-0.82; ICC 0.84 vs. 0.80), compared to manual segmentation.

CONCLUSION

The findings of this study support the use of AI-assisted registration and volumetric segmentation for lymph node and soft tissue metastases in follow-up CT scans. The AI-assisted workflow achieved significant time savings, similar segmentation quality, and reduced inter-reader variability compared to manual segmentation.

Can Delta Radiomics Improve the Prediction of Best Overall Response, Progression-Free Survival, and Overall Survival of Melanoma Patients Treated with Immune Checkpoint Inhibitors?

BACKGROUND

The prevalence of metastatic melanoma is increasing, necessitating the identification of patients who do not benefit from immunotherapy. This study aimed to develop a radiomic biomarker based on the segmentation of all metastases at baseline and the first follow-up CT for the endpoints best overall response (BOR), progression-free survival (PFS), and overall survival (OS), encompassing various immunotherapies. Additionally, this study investigated whether reducing the number of segmented metastases per patient affects predictive capacity.

METHODS

The total tumour load, excluding cerebral metastases, from 146 baseline and 146 first follow-up CTs of melanoma patients treated with first-line immunotherapy was volumetrically segmented. Twenty-one random forest models were trained and compared for the endpoints BOR; PFS at 6, 9, and 12 months; and OS at 6, 9, and 12 months, using as input either only clinical parameters, whole-tumour-load delta radiomics plus clinical parameters, or delta radiomics from the largest ten metastases plus clinical parameters.

RESULTS

The whole-tumour-load delta radiomics model performed best for BOR (AUC 0.81); PFS at 6, 9, and 12 months (AUC 0.82, 0.80, and 0.77); and OS at 6 months (AUC 0.74). The model using delta radiomics from the largest ten metastases performed best for OS at 9 and 12 months (AUC 0.71 and 0.75). Although the radiomic models were numerically superior to the clinical model, statistical significance was not reached.

CONCLUSIONS

The findings indicate that delta radiomics may offer additional value for predicting BOR, PFS, and OS in metastatic melanoma patients undergoing first-line immunotherapy. Despite its complexity, volumetric whole-tumour-load segmentation could be advantageous.

Follow-up of liver metastases: a comparison of deep learning and RECIST 1.1

OBJECTIVES

To compare liver metastases changes in CT assessed by radiologists using RECIST 1.1 and with aided simultaneous deep learning-based volumetric lesion changes analysis.

METHODS

A total of 86 abdominal CT studies from 43 patients (prior and current scans) of abdominal CT scans of patients with 1041 liver metastases (mean = 12.1, std = 11.9, range 1-49) were analyzed. Two radiologists performed readings of all pairs; conventional with RECIST 1.1 and with computer-aided assessment. For computer-aided reading, we used a novel simultaneous multi-channel 3D R2U-Net classifier trained and validated on other scans. The reference was established by having an expert radiologist validate the computed lesion detection and segmentation. The results were then verified and modified as needed by another independent radiologist. The primary outcome measure was the disease status assessment with the conventional and the computer-aided readings with respect to the reference.

RESULTS

For conventional and computer-aided reading, there was a difference in disease status classification in 40 out of 86 (46.51%) and 10 out of 86 (27.9%) CT studies with respect to the reference, respectively. Computer-aided reading improved conventional reading in 30 CT studies by 34.5% for two readers (23.2% and 46.51%) with respect to the reference standard. The main reason for the difference between the two readings was lesion volume differences (p = 0.01).

CONCLUSIONS

AI-based computer-aided analysis of liver metastases may improve the accuracy of the evaluation of neoplastic liver disease status.

CLINICAL RELEVANCE STATEMENT

AI may aid radiologists to improve the accuracy of evaluating changes over time in metastasis of the liver.

KEY POINTS

• Classification of liver metastasis changes improved significantly in one-third of the cases with an automatically generated comprehensive lesion and lesion changes report. • Simultaneous deep learning changes detection and volumetric assessment may improve the evaluation of liver metastases temporal changes potentially improving disease management.

RECIST 1.1 Target Lesion Categorical Response in Metastatic Renal Cell Carcinoma: A Comparison of Conventional versus Volumetric Assessment

Purpose To investigate Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1) approximations of target lesion tumor burden by comparing categorical treatment response according to conventional RECIST versus actual tumor volume measurements of RECIST target lesions. Materials and Methods This is a retrospective cohort study of individuals with metastatic renal cell carcinoma enrolled in a clinical trial (from 2003 to 2017) and includes individuals who underwent baseline and at least one follow-up chest, abdominal, and pelvic CT study and with at least one target lesion. Target lesion volume was assessed by (a) Vmodel, a spherical model of conventional RECIST 1.1, which was extrapolated from RECIST diameter, and (b) Vactual, manually contoured volume. Volumetric responses were determined by the sum of target lesion volumes (Vmodel-sum TL and Vactual-sum TL, respectively). Categorical volumetric thresholds were extrapolated from RECIST. McNemar tests were used to compare categorical volume responses. Results Target lesions were assessed at baseline (638 participants), week 9 (593 participants), and week 17 (508 participants). Vmodel-sum TL classified more participants as having progressive disease (PD), compared with Vactual-sum TL at week 9 (52 vs 31 participants) and week 17 (57 vs 39 participants), with significant overall response discordance (P < .001). At week 9, 25 (48%) of 52 participants labeled with PD by Vmodel-sum TL were classified as having stable disease by Vactual-sum TL. Conclusion A model of RECIST 1.1 based on a single diameter measurement more frequently classified PD compared with response assessment by actual measured tumor volume. Keywords: Urinary, Kidney, Metastases, Oncology, Tumor Response, Volume Analysis, Outcomes Analysis ClinicalTrials.gov registration no. NCT01865747 © RSNA, 2023 Supplemental material is available for this article.

Efficacy and Safety of Low-Dose Apatinib Combined with Chemotherapy as Second-Line Treatment for Advanced Gastric Cancer: A Meta-Analysis

INTRODUCTION

At present, there are several studies on low-dose apatinib combined with chemotherapy as a second-line treatment of advanced gastric cancer (AGC), but the conclusions are controversial. Therefore, this meta-analysis aimed to evaluate the efficacy and safety of low-dose apatinib combined with chemotherapy as a second-line treatment of AGC.

METHODS

Nine databases were searched for records on apatinib combined with chemotherapy in treating AGC from inception to June 2022. The observation group received low-dose apatinib combined with chemotherapy, while the controls received chemotherapy alone or other non-placebo treatments. Outcomes included objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS), and adverse events. The relative risk (RR) and weighted mean difference (WMD) were used as effect sizes.

RESULTS

Eight studies involving 679 patients were included in this meta-analysis. The results of the meta-analysis showed that the observation group was superior to the controls in terms of ORR (RR = 1.38, 95% confidence interval [CI]: 1.05-1.81, p = 0.02), DCR (RR = 1.35, 95% CI: 1.20-1.53, p < 0.001), OS (WMD = 4.72, 95% CI: 0.71-8.72, p < 0.001), and PFS (WMD = 2.67, 95% CI: 1.7-3.63, p < 0.001). There were no significant differences between the two groups in adverse events of any grade except hypertension (RR = 2.82, 95% CI: 2.07-3.84, p < 0.001), hand-mouth syndrome (RR = 1.84, 95% CI: 1.84-2.48, p < 0.001), and proteinuria (RR = 3.63, 95% CI: 2.31-5.7, p < 0.001).

CONCLUSION

Low-dose apatinib combined with chemotherapy as a second-line therapy is more effective in improving the efficacy of AGC compared to chemotherapy alone. However, this option has the potential to increase the risk of hypertension, hand-mouth syndrome, and proteinuria.

Predictive power of deep-learning segmentation based prognostication model in non-small cell lung cancer

Purpose

The study aims to create a model to predict survival outcomes for non-small cell lung cancer (NSCLC) after treatment with stereotactic body radiotherapy (SBRT) using deep-learning segmentation based prognostication (DESEP).

Methods

The DESEP model was trained using imaging from 108 patients with NSCLC with various clinical stages and treatment histories. The model generated predictions based on unsupervised features learned by a deep-segmentation network from computed tomography imaging to categorize patients into high and low risk groups for overall survival (DESEP-predicted-OS), disease specific survival (DESEP-predicted-DSS), and local progression free survival (DESEP-predicted-LPFS). Serial assessments were also performed using auto-segmentation based volumetric RECISTv1.1 and computer-based unidimensional RECISTv1.1 patients was performed.

Results

There was a concordance between the DESEP-predicted-LPFS risk category and manually calculated RECISTv1.1 (φ=0.544, p=0.001). Neither the auto-segmentation based volumetric RECISTv1.1 nor the computer-based unidimensional RECISTv1.1 correlated with manual RECISTv1.1 (p=0.081 and p=0.144, respectively). While manual RECISTv1.1 correlated with LPFS (HR=6.97,3.51-13.85, c=0.70, p&lt;0.001), it could not provide insight regarding DSS (p=0.942) or OS (p=0.662). In contrast, the DESEP-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p&lt;0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p&lt;0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p&lt;0.001). The promising results of the DESEP model were reproduced for the independent, external datasets of Stanford University, classifying survival and ‘dead’ group in their Kaplan-Meyer curves (p = 0.019).

Conclusion

Deep-learning segmentation based prognostication can predict LPFS as well as OS, and DSS after SBRT for NSCLC. It can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients receiving SBRT.

Summary

While current standard of care, manual RECISTv1.1 correlated with local progression free survival (LPFS) (HR=6.97,3.51-13.85, c=0.70, p&lt;0.001), it could not provide insight regarding disease specific survival (DSS) (p=0.942) or overall survival (OS) (p=0.662). In contrast, the deep-learning segmentation based prognostication (DESEP)-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p&lt;0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p&lt;0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p&lt;0.001). DESEP can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients.

Standard diametric versus volumetric early tumor shrinkage as a predictor of survival in metastatic colorectal cancer: subgroup findings of the randomized, open-label phase III trial FIRE-3 / AIO KRK-0306

OBJECTIVES

Early tumor shrinkage (ETS) quantifies the objective response at the first assessment during systemic treatment. In metastatic colorectal cancer (mCRC), ETS gains relevance as an early available surrogate for patient survival. The aim of this study was to increase the predictive accuracy of ETS by using semi-automated volumetry instead of standard diametric measurements.

METHODS

Diametric and volumetric ETS were retrospectively calculated in 253 mCRC patients who received 5-fluorouracil, leucovorin, and irinotecan (FOLFIRI) combined with either cetuximab or bevacizumab. The association of diametric and volumetric ETS with overall survival (OS) and progression-free survival (PFS) was compared.

RESULTS

Continuous diametric and volumetric ETS predicted survival similarly regarding concordance indices (p > .05). In receiver operating characteristics, a volumetric threshold of 45% optimally identified short-term survivors. For patients with volumetric ETS ≥ 45% (vs < 45%), median OS was longer (32.5 vs 19.0 months, p < .001) and the risk of death reduced for the first and second year (hazard ratio [HR] = 0.25, p < .001, and HR = 0.39, p < .001). Patients with ETS ≥ 45% had a reduced risk of progressive disease only for the first 6 months (HR = 0.26, p < .001). These survival times and risks were comparable to those of diametric ETS ≥ 20% (vs < 20%).

CONCLUSIONS

The accuracy of ETS in predicting survival was not increased by volumetric instead of diametric measurements. Continuous diametric and volumetric ETS similarly predicted survival, regardless of whether patients received cetuximab or bevacizumab. A volumetric ETS threshold of 45% and a diametric ETS threshold of 20% equally identified short-term survivors.

KEY POINTS

• ETS based on volumetric measurements did not predict survival more accurately than ETS based on standard diametric measurements. • Continuous diametric and volumetric ETS predicted survival similarly in patients receiving FOLFIRI with cetuximab or bevacizumab. • A volumetric ETS threshold of 45% and a diametric ETS threshold of 20% equally identified short-term survivors.

Assessment of immunotherapy response in intracranial malignancy using semi-automatic segmentation on magnetic resonance images

Objective

To explore multi-aspect radiologic assessment of immunotherapy response in intracranial malignancies based on a semi-automatic segmentation technique, and to explore volumetric thresholds with good performance according to RECIST 1.1 thresholds.

Methods

Patients diagnosed with intracranial malignancies and treated with immunotherapy were included retrospectively. In all MR images, target lesions were measured using a semi-automatic segmentation technique that could intelligently generate visual diagrams including RECIST 1.1, total volume, and max. 3D diameter. The changes in parameters were calculated for each patient after immunotherapy. The ROC curve was used to analyze the sensitivity and specificity of the size change of the legion. This was useful to find new volumetric thresholds with better efficiency in response assessment. The changes in total volume were assessed by conventional volumetric thresholds, while RECIST 1.1 thresholds were for the max. 3D diameter. A chi-square test was used to compare the concordance and diagnostic correlation between the response assessment results of the three criteria.

Results

A total of 20 cases (average age, 58 years; range, 23 to 84 years) and 58 follow-up MR examinations after immunotherapy were included in the analysis. The P-value of the chi-square test between RECIST 1.1 and total volume is 0 (P <0.05), same as that in RECIST 1.1 and max. 3D diameter. The kappa value of the former two was 0.775, and the kappa value for the latter two was 0.742. The above results indicate a significant correlation and good concordance for all three criteria. In addition, we also found that the volumetric assessment had the best sensitivity and specificity for the immunotherapy response in intracranial malignancies, with a PR threshold of -64.9% and a PD threshold of 21.4%.

Conclusions

Radiologic assessment of immunotherapy response in intracranial malignancy can be performed by multiple criteria based on semi-automatic segmentation technique on MR images, such as total volume, max. 3D diameter and RECIST 1.1. In addition, new volumetric thresholds with good sensitivity and specificity were found by volumetric assessment.

The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization

Simple Summary

Modern, personalized therapy approaches are increasingly changing advanced cancer into a chronic disease. Compared to imaging, novel omics methodologies in molecular biology have already achieved an individual characterization of cancerous lesions. With quantitative imaging biomarkers, analyzed by radiomics or deep learning, an imaging-based assessment of tumoral biology can be brought into clinical practice. Combining these with other non-invasive methods, e.g., liquid profiling, could allow for more individual decision making regarding therapies and applications.

Abstract

Similar to the transformation towards personalized oncology treatment, emerging techniques for evaluating oncologic imaging are fostering a transition from traditional response assessment towards more comprehensive cancer characterization via imaging. This development can be seen as key to the achievement of truly personalized and optimized cancer diagnosis and treatment. This review gives a methodological introduction for clinicians interested in the potential of quantitative imaging biomarkers, treating of radiomics models, texture visualization, convolutional neural networks and automated segmentation, in particular. Based on an introduction to these methods, clinical evidence for the corresponding imaging biomarkers—(i) dignity and etiology assessment; (ii) tumoral heterogeneity; (iii) aggressiveness and response; and (iv) targeting for biopsy and therapy—is summarized. Further requirements for the clinical implementation of these imaging biomarkers and the synergistic potential of personalized molecular cancer diagnostics and liquid profiling are discussed.

[Radiologic response assessment in metastatic colorectal cancers]

The increasing indications of cytostatic biotherapies and the improvement in metastatic surgery have profoundly changed the management of metastatic colorectal cancer (mCRC) patients. Then the development of prognostic and predictive scores would be useful to stratify the treatments. Tumor radiological measurement is crucial to estimate treatment efficacy, and to predict pathological response and survival, and this parameter is included when a prognostic score is developed. But the standard size-based radiologic criteria, the Response Evaluation Criteria in Solid Tumors (RECIST), was designed ten years ago to assess tumor volume reduction after cytotoxic chemotherapy only. Nowadays, this method may be insufficient for mCRC patients. The aim of this review is to describe the different radiological assessments evaluated in mCRC, and to underline their correlations with patient's survival and pathologic response. A better knowledge of these radiological measurements would help to better integrate them in prospective trials, and in the prognostic and predictive scores. The choice of radiological measurement could be discussed regarding patient's situation, combining different approaches, and assessing tumoral mass quantification.

Heterogeneous radiological response to neoadjuvant therapy is associated with poor prognosis after resection of colorectal liver metastases

INTRODUCTION

Surgery combined with perioperative chemotherapy has become standard of care in patients with resectable colorectal liver metastases. However, poor outcome is expected for a significant subgroup. The clinical implications of inter-metastatic heterogeneity remain largely unknown. In a prospective, population-based series of patients undergoing resection of multiple colorectal liver metastases, the aim was to investigate the prevalence and prognostic impact of heterogeneous response to neoadjuvant chemotherapy.

MATERIALS AND METHODS

Radiological response to treatment was evaluated in a lesion-specific manner in 2-5 metastases per patient. Change of lesion diameter was evaluated and response/progression was classified according to three different size thresholds; 3, 4 and 5 mm. A heterogeneous response was defined as progression and response of different metastases in the same patient.

RESULTS

In total, 142 patients with 585 liver metastases were examined with the same radiological method (MRI or CT) before and after neoadjuvant treatment. Heterogeneous response to treatment was seen in 16 patients (11%) using the 3 mm size change threshold, and this group had a 5-year cancer-specific survival of 19% compared to 49% for patients with response in all lesions (p = 0.003). Cut-off values of 4-5 mm were less sensitive for detecting a heterogeneous response, but the survival difference was similar and significant.

CONCLUSION

A subgroup of patients with multiple colorectal liver metastases had heterogeneous radiological response to neoadjuvant chemotherapy and poor prognosis. The evaluation of response pattern is easy to perform, feasible in clinical practice and, if validated, a promising biomarker for treatment decisions.

How clinical imaging can assess cancer biology

Human cancers represent complex structures, which display substantial inter- and intratumor heterogeneity in their genetic expression and phenotypic features. However, cancers usually exhibit characteristic structural, physiologic, and molecular features and display specific biological capabilities named hallmarks. Many of these tumor traits are imageable through different imaging techniques. Imaging is able to spatially map key cancer features and tumor heterogeneity improving tumor diagnosis, characterization, and management. This paper aims to summarize the current and emerging applications of imaging in tumor biology assessment.