CT attenuation of liver metastases before targeted therapy is a prognostic factor of overall survival in colorectal cancer patients. Results from the randomised, open-label FIRE-3/AIO KRK0306 trial

Textural heterogeneity of liver lesions in CT imaging - comparison of colorectal and pancreatic metastases

PURPOSE

Tumoral heterogeneity poses a challenge for personalized cancer treatments. Especially in metastasized cancer, it remains a major limitation for successful targeted therapy, often leading to drug resistance due to tumoral escape mechanisms. This work explores a non-invasive radiomics-based approach to capture textural heterogeneity in liver lesions and compare it between colorectal cancer (CRC) and pancreatic cancer (PDAC).

MATERIALS AND METHODS

In this retrospective single-center study 73 subjects (42 CRC, 31 PDAC) with 1291 liver metastases (430 CRC, 861 PDAC) were segmented fully automated on contrast-enhanced CT images by a UNet for medical images. Radiomics features were extracted using the Python package Pyradiomics. The mean coefficient of variation (CV) was calculated patient-wise for each feature to quantify the heterogeneity. An unpaired t-test identified features with significant differences in feature variability between CRC and PDAC metastases.

RESULTS

In both colorectal and pancreatic liver metastases, interlesional heterogeneity in imaging can be observed using quantitative imaging features. 75 second-order features were extracted to compare the varying textural characteristics. In total, 18 radiomics features showed a significant difference (p < 0.05) in their expression between the two malignancies. Out of these, 16 features showed higher levels of variability within the cohort of pancreatic metastases, which, as illustrated in a radar plot, suggests greater textural heterogeneity for this entity.

CONCLUSIONS

Radiomics has the potential to identify the interlesional heterogeneity of CT texture among individual liver metastases. In this proof-of-concept study for the quantification and comparison of imaging-related heterogeneity in liver metastases a variation in the extent of heterogeneity levels in CRC and PDAC liver metastases was shown.

Predictive Value of Quantitative Parameters of 18F-FDG PET/CT in Patients with Liposarcoma

The purpose of this study was to evaluate the predictive features of baseline F-18-fluorodeoxy-D-glucose positron emission tomography (18F-FDG PET)/computed tomography (CT) parameters in patients with dedifferentiated liposarcomas (DDLPSs) and well-differentiated liposarcomas (WDLPSs) receiving systemic treatment. A total of 24 patients with liposarcoma who underwent longitudinal 18F-FDG PET/CT in systemic therapy were included. All volumetric segmentation of each tumor section and semiquantitative imaging parameters were extracted from the axial field of view from both PET and CT images. Maximum, mean, and minimum standardized uptake values (SUVmax, SUVmean, and SUVmin), Hounsfield units (HUs), and their respective changes from baseline and posttreatment were calculated. The voxel values from unenhanced CT images were correlated with PET-derived parameters. The 18F-FDG uptake decreased by more than 56% on average in responders for both SUVmax and SUVmean in DDLPS. There was a decrease in HUmax in DDLPS among responders. Using AUC > 0.8 as a reasonable predictor, we found that the ratios of SUVmaxD/HUmean, SUVmaxD/HUmedian, and SUVmeanD/HUmedian at baseline were significant indicators of the response to treatment in patients with liposarcoma. The changes in SUVmean and not just SUVmax parameters could be considered as accurate tumor response indicators. For the first time, we introduced baseline SUV/HU ratios as a valuable diagnostic tool in predicting liposarcoma treatment outcomes. This ability was not revealed by classic semiquantitative PET or CT parameters at baseline.

Radiomics analysis of baseline computed tomography to predict oncological outcomes in patients treated for resectable colorectal cancer liver metastasis

OBJECTIVE

The purpose of this study was to determine and compare the performance of pre-treatment clinical risk score (CRS), radiomics models based on computed (CT), and their combination for predicting time to recurrence (TTR) and disease-specific survival (DSS) in patients with colorectal cancer liver metastases.

METHODS

We retrospectively analyzed a prospectively maintained registry of 241 patients treated with systemic chemotherapy and surgery for colorectal cancer liver metastases. Radiomics features were extracted from baseline, pre-treatment, contrast-enhanced CT images. Multiple aggregation strategies were investigated for cases with multiple metastases. Radiomics signatures were derived using feature selection methods. Random survival forests (RSF) and neural network survival models (DeepSurv) based on radiomics features, alone or combined with CRS, were developed to predict TTR and DSS. Leveraging survival models predictions, classification models were trained to predict TTR within 18 months and DSS within 3 years. Classification performance was assessed with area under the receiver operating characteristic curve (AUC) on the test set.

RESULTS

For TTR prediction, the concordance index (95% confidence interval) was 0.57 (0.57-0.57) for CRS, 0.61 (0.60-0.61) for RSF in combination with CRS, and 0.70 (0.68-0.73) for DeepSurv in combination with CRS. For DSS prediction, the concordance index was 0.59 (0.59-0.59) for CRS, 0.57 (0.56-0.57) for RSF in combination with CRS, and 0.60 (0.58-0.61) for DeepSurv in combination with CRS. For TTR classification, the AUC was 0.33 (0.33-0.33) for CRS, 0.77 (0.75-0.78) for radiomics signature alone, and 0.58 (0.57-0.59) for DeepSurv score alone. For DSS classification, the AUC was 0.61 (0.61-0.61) for CRS, 0.57 (0.56-0.57) for radiomics signature, and 0.75 (0.74-0.76) for DeepSurv score alone.

CONCLUSION

Radiomics-based survival models outperformed CRS for TTR prediction. More accurate, noninvasive, and early prediction of patient outcome may help reduce exposure to ineffective yet toxic chemotherapy or high-risk major hepatectomies.

Prognostic value of the tumor-to-liver density ratio in patients with metastatic colorectal cancer treated with bevacizumab-based chemotherapy. A post-hoc study of the STIC-AVASTIN trial

BACKGROUND

The Response Evaluation Criteria in Solid Tumors (RECIST) are often inadequate for the early assessment of the response to cancer therapy, particularly bevacizumab-based chemotherapy. In a first cohort of patients with colorectal cancer liver metastases (CRLM), we showed that variations of the tumor-to-liver density (TTLD) ratio and modified size-based criteria determined using computed tomography (CT) data at the first restaging were better prognostic criteria than the RECIST. The aims of this study were to confirm the relevance of these radiological biomarkers as early predictors of the long-term clinical outcome and to assess their correlation with contrast-enhanced ultrasound (CEUS) parameters in a new patient cohort.

METHODS

In this post-hoc study of the multicenter STIC-AVASTIN trial, we retrospectively reviewed CT data of patients with CRLM treated with bevacizumab-based regimens. We determined the size, density and TTLD ratio of target liver lesions at baseline and at the first restaging and also performed a morphologic evaluation according to the MD Anderson criteria. We assessed the correlation of these parameters with progression-free survival (PFS) and overall survival (OS) using the log-rank test and a Cox proportional hazard model. We also examined the association between TTLD ratio and quantitative CEUS parameters.

RESULTS

This analysis concerned 79 of the 137 patients included in the STIC-AVASTIN trial. PFS and OS were significantly longer in patients with tumor size reduction > 15% at first restaging, but were not correlated with TTLD ratio variations. However, PFS was longer in patients with TTLD ratio > 0.6 at baseline and first restaging than in those who did not reach this threshold. In the multivariate analysis, only baseline TTLD ratio > 0.6 was a significant survival predictor. TTLD ratio > 0.6 was associated with improved perfusion parameters.

CONCLUSIONS

Although TTLD ratio variations did not correlate with the long-term clinical outcomes, TTLD absolute values remained a good predictor of survival at baseline and first restaging, and may reflect tumor microvascular features that might influence bevacizumab-based treatment efficiency.

TRIAL REGISTRATION

NCT00489697, registration number of the STIC-AVASTIN trial.

Extracellular volume fraction can predict the treatment response and survival outcome of colorectal cancer liver metastases

OBJECTIVE

To assess the prognostic value of pre- and post-therapeutic changes in extracellular volume (ECV) fraction of liver metastases (LMs) for treatment response (TR) and survival outcomes in colorectal cancer liver metastases (CRLM).

METHODS

186 LMs were confirmed by pathology or follow-up (Training: 130; Test: 56). We analyzed the changes in ECV fraction of LMs before and after 2 cycles of chemotherapy combined with bevacizumab. After 12 cycles, we evaluated the TR on LMs based on the RECIST v1.1. Relative changes in ECV fraction and Hounsfield Units (HU), defined as ΔECV and ΔHU, were associated with progression-free survival (PFS), overall survival (OS), and TR. We identified TR predictors with multivariate logistic regression and PFS, OS risk factors with COX analysis.

RESULTS

186 LMs were classified as TR lesions (TR+: 84) and non-TR lesions (TR-:102). ΔECV, ΔHUA-E, and texture could distinguish the TR of LMs in training and test set (P < 0.05). ΔECV [Odds ratio (OR): 1.03; 95% Confidence interval (CI): 1.02-1.05, P < 0.01] was an independent predictor of TR-. Area under the curve (AUC), sensitivity and specificity of TR model in training and test set were 0.87, 0.84, 90.14%, 90.32%, 72.88%, 64.00%, respectively. High CRD_score indicates that patients have shorter PFS [Hazard ratio (HR): 2.01; 95%CI: 1.02-3.98, P = 0.045)] and OS (HR: 1.89, 95%CI: 1.04-3.42, P = 0.038).

CONCLUSION

ΔECV can be used as an independent predictor of TR of CRLM chemotherapy combined with bevacizumab.

Imaging-based characterization of tumoral heterogeneity for personalized cancer treatment

With personalized tumor therapy, understanding and addressing the heterogeneity of malignant tumors is becoming increasingly important. Heterogeneity can be found within one lesion (intralesional) and between several tumor lesions emerging from one primary tumor (interlesional). The heterogeneous tumor cells may show a different response to treatment due to their biology, which in turn influences the outcome of the affected patients and the choice of therapeutic agents. Therefore, both intra- and interlesional heterogeneity should be addressed at the diagnostic stage. While genetic and biological heterogeneity are important parameters in molecular tumor characterization and in histopathology, they are not yet addressed routinely in medical imaging. This article summarizes the recently established markers for tumor heterogeneity in imaging as well as heterogeneous/mixed response to therapy. Furthermore, a look at emerging markers is given. The ultimate goal of this overview is to provide comprehensive understanding of tumor heterogeneity and its implications for radiology and for communication with interdisciplinary teams in oncology. KEY POINTS::   · Tumor heterogeneity can be described within one lesion (intralesional) or between several lesions (interlesional).. · The heterogeneous biology of tumor cells can lead to a mixed therapeutic response and should be addressed in diagnostics and the therapeutic regime.. · Quantitative image diagnostics can be enhanced using AI, improved histopathological methods, and liquid profiling in the future..

Tumor classification of gastrointestinal liver metastases using CT-based radiomics and deep learning

OBJECTIVES

The goal of this study is to demonstrate the performance of radiomics and CNN-based classifiers in determining the primary origin of gastrointestinal liver metastases for visually indistinguishable lesions.

METHODS

In this retrospective, IRB-approved study, 31 pancreatic cancer patients with 861 lesions (median age [IQR]: 65.39 [56.87, 75.08], 48.4% male) and 47 colorectal cancer patients with 435 lesions (median age [IQR]: 65.79 [56.99, 74.62], 63.8% male) were enrolled. A pretrained nnU-Net performed automated segmentation of 1296 liver lesions. Radiomics features for each lesion were extracted using pyradiomics. The performance of several radiomics-based machine-learning classifiers was investigated for the lesions and compared to an image-based deep-learning approach using a DenseNet-121. The performance was evaluated by AUC/ROC analysis.

RESULTS

The radiomics-based K-nearest neighbor classifier showed the best performance on an independent test set with AUC values of 0.87 and an accuracy of 0.67. In comparison, the image-based DenseNet-121-classifier reached an AUC of 0.80 and an accuracy of 0.83.

CONCLUSIONS

CT-based radiomics and deep learning can distinguish the etiology of liver metastases from gastrointestinal primary tumors. Compared to deep learning, radiomics based models showed a varying generalizability in distinguishing liver metastases from colorectal cancer and pancreatic adenocarcinoma.

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.

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.

Interobserver Variability in CT-based Morphologic Tumor Response Assessment of Colorectal Liver Metastases

Purpose To evaluate interobserver variability in the morphologic tumor response assessment of colorectal liver metastases (CRLM) managed with systemic therapy and to assess the relation of morphologic response with gene mutation status, targeted therapy, and Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 measurements. Materials and Methods Participants with initially unresectable CRLM receiving different systemic therapy regimens from the randomized, controlled CAIRO5 trial (NCT02162563) were included in this prospective imaging study. Three radiologists independently assessed morphologic tumor response on baseline and first follow-up CT scans according to previously published criteria. Two additional radiologists evaluated disagreement cases. Interobserver agreement was calculated by using Fleiss κ. On the basis of the majority of individual radiologic assessments, the final morphologic tumor response was determined. Finally, the relation of morphologic tumor response and clinical prognostic parameters was assessed. Results In total, 153 participants (median age, 63 years [IQR, 56-71]; 101 men) with 306 CT scans comprising 2192 CRLM were included. Morphologic assessment performed by the three radiologists yielded 86 (56%) agreement cases and 67 (44%) disagreement cases (including four major disagreement cases). Overall interobserver agreement between the panel radiologists on morphology groups and morphologic response categories was moderate (κ = 0.53, 95% CI: 0.48, 0.58 and κ = 0.54, 95% CI: 0.47, 0.60). Optimal morphologic response was particularly observed in patients treated with bevacizumab (P = .001) and in patients with RAS/BRAF mutation (P = .04). No evidence of a relationship between RECIST 1.1 and morphologic response was found (P = .61). Conclusion Morphologic tumor response assessment following systemic therapy in participants with CRLM demonstrated considerable interobserver variability. Keywords: Tumor Response, Observer Performance, CT, Liver, Metastases, Oncology, Abdomen/Gastrointestinal Clinical trial registration no. NCT02162563 Supplemental material is available for this article. © RSNA, 2022.

Radiomics diagnosed histopathological growth pattern in prediction of response and 1-year progression free survival for colorectal liver metastases patients treated with bevacizumab containing chemotherapy

OBJECTIVE

To investigate the capability of a radiomics model, which was designed to identify histopathologic growth pattern (HGP) of colorectal liver metastases (CRLMs) based on contrast-enhanced multidetector computed tomography (ceMDCT), to predict early response and 1-year progression free survival (PFS) in patients treated with bevacizumab-containing chemotherapy.

METHODS

Patients with unresectable CRLMs who were treated with bevacizumab-containing chemotherapy were included in this multicenter retrospective study. For each target lesion, the radiomics-diagnosed HGP (RAD_HGP) of desmoplastic (D) pattern or replacement (R) pattern was determined. Logistic regression and receiver operating characteristic (ROC) curves were used to assess lesion- and patient-based responses according to morphologic response criteria. One-year PFS was calculated using Kaplan-Meier curves. Hazard ratios for 1-year PFS were obtained through Cox proportional hazard regression analysis.

RESULTS

Among 119 study patients, 206 D pattern and 140 R pattern lesions were identified. In patients with multiple lesions, 52 had D pattern, 31 had R pattern, and 36 had mixed (D + R) pattern. The area under the curve value for RAD_HGP in predicting early response was 0.707 for lesion-based analysis and 0.720 for patient-based analysis. Patients with D pattern had a significantly longer PFS than patients with R pattern or mixed pattern (P < 0.001). RAD_HGP was the only independent predictor of 1-year PFS.

CONCLUSIONS

HGP diagnosed using a radiomics model could be used as an effective predictor of PFS for patients with CRLMs treated with bevacizumab-containing chemotherapy.

Quantitative CT assessment by histogram and volume ratio in pyrrolizidines alkaloids-induced hepatic sinusoidal obstruction syndrome

OBJECTIVE

To quantitatively assess hypoattenuation volume ratio and hepatic parenchymal hypoattenuation on contrast enhanced computed tomography (CECT) in patients with pyrrolizidines alkaloids (PAs)-induced hepatic sinusoidal obstruction syndrome (HSOS), and evaluate the correlations of the CT-based quantitative values with clinical factors.

METHODS

Thirty-five patients with PAs-induced HSOS who underwent CECT were retrospectively enrolled. The ratio of hypoattenuation volume to total liver volume, and changes in damaged area-to-normal liver density ratio (ΔDR) derived from histogram on portal venous phase were quantitatively measured. Heterogeneous hypoattenuation (CT score) scored by hypoattenuation volume ratio and ΔDR were calculated. The correlation between imaging findings and clinical factors was analyzed using Pearson correlation test.

RESULTS

Liver function tests were abnormal in most patients, the mean Hounsfield unit (HU) of damaged area (58.68 ± 17.3) was significantly lower (P < 0.001) than the corresponding normal liver (82.27 ± 23.97). Heterogeneous hypoattenuation were mild in 13 patients (37 %), moderate in 16 patients (46 %), and severe in 6 patients (17 %). ΔDR derived from histogram was positively correlated (weakly to moderately) with total bilirubin (r = 0.341, P = 0.045), direct bilirubin (r = 0.385, P = 0.022), and alkaline phosphatase (r = 0.491, P = 0.003), while such correlation was not observed in hypoattenuation volume ratio. The severity of heterogeneous hypoattenuation scored by hypoattenuation volume ratio and ΔDR was positively correlated (weakly) with prothrombin time (r = 0.357, P = 0.035), international normalized ratio (r = 0.363, P = 0.032), alkaline phosphatase (r = 0.359, P = 0.034), and model for end-stage liver disease (MELD) score (r = 0.347, P = 0.041).

CONCLUSION

Heterogeneous hypoattenuation scored by volume ratio and ΔDR on CECT provides a non-invasive approach in evaluating the severity of PAs-induced HSOS.

Enhanced Rim on MDCT of Colorectal Liver Metastases: Assessment of Ability to Predict Progression-Free Survival and Response to Bevacizumab-Based Chemotherapy

OBJECTIVE. The purpose of this article is to evaluate the enhanced rim on the portal venous phase (PVP) on MDCT as a predictor of 1-year progression-free survival (PFS) and response to bevacizumab-based chemotherapy in patients with colorectal liver metastases (CRLM). MATERIALS AND METHODS. We retrospectively identified 111 patients with primary unresectable CRLM treated with bevacizumab-based chemotherapy at two institutions between 2012 and 2018. Pretreatment contrast-enhanced MDCT images were reviewed and data on clinical characteristics were collected from the electronic medical records. Univariable and multivariable analyses were conducted to assess several imaging features and clinical characteristics as potential predictors of 1-year PFS and objective response rate (ORR). RESULTS. After 1 year of follow-up, liver metastatic tumor progression was detected in 52 patients (46.8%) after bevacizumab-based chemotherapy. A log-rank test showed that enhanced rim on PVP (chi-square test, 5.862; p = 0.015) and the occurrence of liver resection surgery (chi-square test, 7.836; p = 0.005) were significant predictors of 1-year PFS. Multivariable analysis showed that enhanced rim on PVP images was an independent predictor of 1-year PFS (hazard ratio, 0.510; 95% CI, 0.282-0.926; p = 0.027) and ORR (odds ratio, 4.694; p < 0.001). CONCLUSION. The presence of an enhanced rim on PVP MDCT is an independent predictor of survival and response to bevacizumab-based chemotherapy among patients with CRLM.

Impact of Size and Location of Metastases on Early Tumor Shrinkage and Depth of Response in Patients With Metastatic Colorectal Cancer: Subgroup Findings of the Randomized, Open-Label Phase 3 Trial FIRE-3/AIO KRK-0306

BACKGROUND

The Response Evaluation Criteria in Solid Tumors (RECIST) are used to define degrees of response to chemotherapy. For accelerated response evaluation, early tumor shrinkage (ETS) of ≥ 20% has been suggested as a predictor for outcome in metastatic colorectal cancer (mCRC). Together with depth of response (DpR), new alternative metrics have been provided, yielding promising outcome parameters. In this analysis, we aimed to further characterize ETS and DpR.

PATIENTS AND METHODS

This analysis was based on FIRE-3, a randomized phase 3 trial comparing first-line FOLFIRI plus either cetuximab or bevacizumab in KRAS exon 2 wild-type mCRC. ETS and DpR were determined on the basis of RECIST 1.1 in a blinded radiologic review. ETS was evaluated as a categorized (≥ 20% shrinkage) and continuous parameter. The impact of baseline location and size of metastases on ETS and DpR were evaluated by univariate and multivariate analyses.

RESULTS

Of 592 patients, 395 (66.7%) had data available for radiologic review. Median continuous ETS for lung, liver, and suspected lymph node metastases was 20%, 23%, and 30%, respectively. The median DpR was -32%, -44%, and -50%, respectively (all P < .01). In multivariate analysis, lung metastases were significantly associated with inferior DpR (P = .021), whereas hepatic metastases led to higher DpR (P = .024). Large metastases were associated with favorable ETS, whereas small metastases were correlated with higher DpR (P < .001).

CONCLUSION

ETS and DpR depend on the location and size of metastases in mCRC. These associations may establish the basis for further research to optimize the predictive accuracy of both parameters. This may help basing treatment decisions on ETS and DpR.

The relevance of CT-based geometric and radiomics analysis of whole liver tumor burden to predict survival of patients with metastatic colorectal cancer

OBJECTIVES

To investigate the prediction of 1-year survival (1-YS) in patients with metastatic colorectal cancer with use of a systematic comparative analysis of quantitative imaging biomarkers (QIBs) based on the geometric and radiomics analysis of whole liver tumor burden (WLTB) in comparison to predictions based on the tumor burden score (TBS), WLTB volume alone, and a clinical model.

METHODS

A total of 103 patients (mean age: 61.0 ± 11.2 years) with colorectal liver metastases were analyzed in this retrospective study. Automatic segmentations of WLTB from baseline contrast-enhanced CT images were used. Established biomarkers as well as a standard radiomics model building were used to derive 3 prognostic models. The benefits of a geometric metastatic spread (GMS) model, the Aerts radiomics prior model of the WLTB, and the performance of TBS and WLTB volume alone were assessed. All models were analyzed in both statistical and predictive machine learning settings in terms of AUC.

RESULTS

TBS showed the best discriminative performance in a statistical setting to discriminate 1-YS (AUC = 0.70, CI: [0.56, 0.90]). For the machine learning-based prediction for unseen patients, both a model of the GMS of WLTB (0.73, CI: [0.60, 0.84]) and the Aerts radiomics prior model (0.76, CI: [0.65, 0.86]) applied on the WLTB showed a numerically higher predictive performance than TBS (0.68, CI: [0.54, 0.79]), radiomics (0.65, CI: [0.55, 0.78]), WLTB volume alone (0.53, CI: [0.40. 0.66]), or the clinical model (0.56, CI: [0.43, 0.67]).

CONCLUSIONS

The imaging-based GMS model may be a first step towards a more fine-grained machine learning extension of the TBS concept for risk stratification in mCRC patients without the vulnerability to technical variance of radiomics.

KEY POINTS

• CT-based geometric distribution and radiomics analysis of whole liver tumor burden in metastatic colorectal cancer patients yield prognostic information. • Differences in survival are possibly attributable to the spatial distribution of metastatic lesions and the geometric metastatic spread analysis of all liver metastases may serve as robust imaging biomarker invariant to technical variation. • Imaging-based prediction models outperform clinical models for 1-year survival prediction in metastatic colorectal cancer patients with liver metastases.

Current state of the art imaging approaches for colorectal liver metastasis

One of the most common cancers worldwide, colorectal cancer (CRC) has been associated with significant morbidity and mortality and therefore represents an enormous burden to the health care system. Recent advances in CRC treatments have provided patients with primary and metastatic CRC a better long-term prognosis. The presence of synchronous or metachronous metastasis has been associated, however, with worse survival. The most common site of metastatic disease is the liver. A variety of treatment modalities aimed at targeting colorectal liver metastases (CRLM) has been demonstrated to improve the prognosis of these patients. Loco-regional approaches such as surgical resection and tumor ablation (operative and percutaneous) can provide patients with a chance at long-term disease control and even cure in select populations. Patient selection is important in defining the most suitable treatment option for CRLM in order to provide the best possible survival benefit while avoiding unnecessary interventions and adverse events. Medical imaging plays a crucial role in evaluating the characteristics of CRLMs and disease resectability. Size of tumors, proximity to adjacent anatomical structures, and volume of the unaffected liver are among the most important imaging parameters to determine the suitability of patients for surgical management or other appropriate treatment approaches. We herein provide a comprehensive overview of current-state-of-the-art imaging in the management of CRLM, including staging, treatment planning, response and survival assessment, and post-treatment surveillance. Computed tomography (CT) scan and magnetic resonance imaging (MRI) are two most commonly used techniques, which can be used solely or in combination with functional imaging modalities such as positron emission tomography (PET) and diffusion weighted imaging (DWI). Providing up-to-date evidence on advantages and disadvantages of imaging modalities and tumor assessment criteria, the current review offers a practice guide to assist providers in choosing the most suitable imaging approach for patients with CRLM.

Early evaluation using a radiomic signature of unresectable hepatic metastases to predict outcome in patients with colorectal cancer treated with FOLFIRI and bevacizumab

PURPOSE

The objective of this study was to build and validate a radiomic signature to predict early a poor outcome using baseline and 2-month evaluation CT and to compare it to the RECIST1·1 and morphological criteria defined by changes in homogeneity and borders.

METHODS

This study is an ancillary study from the PRODIGE-9 multicentre prospective study for which 491 patients with metastatic colorectal cancer (mCRC) treated by 5-fluorouracil, leucovorin and irinotecan (FOLFIRI) and bevacizumab had been analysed. In 230 patients, computed texture analysis was performed on the dominant liver lesion (DLL) at baseline and 2 months after chemotherapy. RECIST1·1 evaluation was performed at 6 months. A radiomic signature (Survival PrEdiction in patients treated by FOLFIRI and bevacizumab for mCRC using contrast-enhanced CT TextuRe Analysis (SPECTRA) Score) combining the significant predictive features was built using multivariable Cox analysis in 120 patients, then locked, and validated in 110 patients. Overall survival (OS) was estimated with the Kaplan-Meier method and compared between groups with the logrank test. An external validation was performed in another cohort of 40 patients from the PRODIGE 20 Trial.

RESULTS

In the training cohort, the significant predictive features for OS were: decrease in sum of the target liver lesions (STL), (adjusted hasard-ratio(aHR)=13·7, p=1·93×10^-7), decrease in kurtosis (ssf=4) (aHR=1·08, p=0·001) and high baseline density of DLL, (aHR=0·98, p<0·001). Patients with a SPECTRA Score >0·02 had a lower OS in the training cohort (p<0·0001), in the validation cohort (p<0·0008) and in the external validation cohort (p=0·0027). SPECTRA Score at 2 months had the same prognostic value as RECIST at 6 months, while non-response according to RECIST1·1 at 2 months was not associated with a lower OS in the validation cohort (p=0·238). Morphological response was not associated with OS (p=0·41).

CONCLUSION

A radiomic signature (combining decrease in STL, density and computed texture analysis of the DLL) at baseline and 2-month CT was able to predict OS, and identify good responders better than RECIST1.1 criteria in patients with mCRC treated by FOLFIRI and bevacizumab as a first-line treatment. This tool should now be validated by further prospective studies.

TRIAL REGISTRATION

Clinicaltrial.gov identifier of the PRODIGE 9 study: NCT00952029.Clinicaltrial.gov identifier of the PRODIGE 20 study: NCT01900717.