Predicting local tumour progression after ablation for colorectal liver metastases: CT-based radiomics of the ablation zone

Prediction of Local Tumor Progression After Thermal Ablation of Colorectal Cancer Liver Metastases Based on Magnetic Resonance Imaging Δ-Radiomics

PURPOSE

This study aimed to enhance the predictability of local tumor progression (LTP) postthermal ablation in patients with colorectal cancer liver metastases (CRLMs). A sophisticated approach integrating magnetic resonance imaging (MRI) Δ-radiomics and clinical feature-based modeling was employed.

MATERIALS AND METHODS

In this retrospective study, 37 patients with CRLM were included, encompassing a total of 57 tumors. Radiomics features were derived by delineating the images of lesions pretreatment and images of the ablation zones posttreatment. The change in these features, termed Δ-radiomics, was calculated by subtracting preprocedure values from postprocedure values. Three models were developed using the least absolute shrinkage and selection operators (LASSO) and logistic regression: the preoperative lesion model, the postoperative ablation area model, and the Δ model. Additionally, a composite model incorporating identified clinical features predictive of early treatment success was created to assess its prognostic utility for LTP.

RESULTS

LTP was observed in 20 out of the 57 lesions (35%). The clinical model identified, tumor size (P = 0.010), and ΔCEA (P = 0.044) as factors significantly associated with increased LTP risk postsurgery. Among the three models, the Δ model demonstrated the highest AUC value (T2WI AUC in training, 0.856; Delay AUC, 0.909; T2WI AUC in testing, 0.812; Delay AUC, 0.875), whereas the combined model yielded optimal performance (T2WI AUC in training, 0.911; Delay AUC, 0.954; T2WI AUC in testing, 0.847; Delay AUC, 0.917). Despite its superior AUC values, no significant difference was noted when comparing the performance of the combined model across the two sequences (P = 0.6087).

CONCLUSIONS

Combined models incorporating clinical data and Δ-radiomics features serve as valuable indicators for predicting LTP following thermal ablation in patients with CRLM.

Artificial intelligence in predicting recurrence after first-line treatment of liver cancer: a systematic review and meta-analysis

BACKGROUND

The aim of this study was to conduct a systematic review and meta-analysis to comprehensively evaluate the performance and methodological quality of artificial intelligence (AI) in predicting recurrence after single first-line treatment for liver cancer.

METHODS

A rigorous and systematic evaluation was conducted on the AI studies related to recurrence after single first-line treatment for liver cancer, retrieved from the PubMed, Embase, Web of Science, Cochrane Library, and CNKI databases. The area under the curve (AUC), sensitivity (SENC), and specificity (SPEC) of each study were extracted for meta-analysis.

RESULTS

Six percutaneous ablation (PA) studies, 16 surgical resection (SR) studies, and 5 transarterial chemoembolization (TACE) studies were included in the meta-analysis for predicting recurrence after hepatocellular carcinoma (HCC) treatment, respectively. Four SR studies and 2 PA studies were included in the meta-analysis for recurrence after intrahepatic cholangiocarcinoma (ICC) and colorectal cancer liver metastasis (CRLM) treatment. The pooled SENC, SEPC, and AUC of AI in predicting recurrence after primary HCC treatment via PA, SR, and TACE were 0.78, 0.90, and 0.92; 0.81, 0.77, and 0.86; and 0.73, 0.79, and 0.79, respectively. The values for ICC treated with SR and CRLM treated with PA were 0.85, 0.71, 0.86 and 0.69, 0.63,0.74, respectively.

CONCLUSION

This systematic review and meta-analysis demonstrates the comprehensive application value of AI in predicting recurrence after a single first-line treatment of liver cancer, with satisfactory results, indicating the clinical translation potential of AI in predicting recurrence after liver cancer treatment.

A CT-based radiomics tumor quality and quantity model to predict early recurrence after radical surgery for colorectal liver metastases

PURPOSE

This study aimed to develop a tumor radiomics quality and quantity model (RQQM) based on preoperative enhanced CT to predict early recurrence after radical surgery for colorectal liver metastases (CRLM).

METHODS

A retrospective analysis was conducted on 282 cases from 3 centers. Clinical risk factors were examined using univariate and multivariate logistic regression (LR) to construct the clinical model. Radiomics features were extracted using the least absolute shrinkage and selection operator (LASSO) for dimensionality reduction. The LR learning algorithm was employed to construct the radiomics model, RQQM (radiomics-TBS), combined model (radiomics-clinical), clinical risk score (CRS) model and tumor burden score (TBS) model. Inter-model comparisons were made using area under the curve (AUC), decision curve analysis (DCA) and calibration curve. Log-rank tests assessed differences in disease-free survival (DFS) and overall survival (OS).

RESULTS

Clinical features screening identified CRS, KRAS/NRAS/BRAF and liver lobe distribution as risk factors. Radiomics model, RQQM, combined model demonstrated higher AUC values compared to CRS and TBS model in training, internal and external validation cohorts (Delong-test P < 0.05). RQQM outperformed the radiomics model, but was slightly inferior to the combined model. Survival curves revealed statistically significant differences in 1-year DFS and 3-year OS for the RQQM (P < 0.001).

CONCLUSIONS

RQQM integrates both "quality" (radiomics) and "quantity" (TBS). The radiomics model is superior to the TBS model and has a greater impact on patient prognosis. In the absence of clinical data, RQQM, relying solely on imaging data, shows an advantage in predicting early recurrence after radical surgery for CRLM.

Independent validation of CT radiomics models in colorectal liver metastases: predicting local tumour progression after ablation

OBJECTIVES

Independent internal and external validation of three previously published CT-based radiomics models to predict local tumor progression (LTP) after thermal ablation of colorectal liver metastases (CRLM).

MATERIALS AND METHODS

Patients with CRLM treated with thermal ablation were collected from two institutions to collect a new independent internal and external validation cohort. Ablation zones (AZ) were delineated on portal venous phase CT 2-8 weeks post-ablation. Radiomics features were extracted from the AZ and a 10 mm peri-ablational rim (PAR) of liver parenchyma around the AZ. Three previously published prediction models (clinical, radiomics, combined) were tested without retraining. LTP was defined as new tumor foci appearing next to the AZ up to 24 months post-ablation.

RESULTS

The internal cohort included 39 patients with 68 CRLM and the external cohort 52 patients with 78 CRLM. 34/146 CRLM developed LTP after a median follow-up of 24 months (range 5-139). The median time to LTP was 8 months (range 2-22). The combined clinical-radiomics model yielded a c-statistic of 0.47 (95%CI 0.30-0.64) in the internal cohort and 0.50 (95%CI 0.38-0.62) in the external cohort, compared to 0.78 (95%CI 0.65-0.87) in the previously published original cohort. The radiomics model yielded c-statistics of 0.46 (95%CI 0.29-0.63) and 0.39 (95%CI 0.28-0.52), and the clinical model 0.51 (95%CI 0.34-0.68) and 0.51 (95%CI 0.39-0.63) in the internal and external cohort, respectively.

CONCLUSION

The previously published results for prediction of LTP after thermal ablation of CRLM using clinical and radiomics models were not reproducible in independent internal and external validation.

CLINICAL RELEVANCE STATEMENT

Local tumour progression after thermal ablation of CRLM cannot yet be predicted with the use of CT radiomics of the ablation zone and peri-ablational rim. These results underline the importance of validation of radiomics results to test for reproducibility in independent cohorts.

KEY POINTS

• Previous research suggests CT radiomics models have the potential to predict local tumour progression after thermal ablation in colorectal liver metastases, but independent validation is lacking. • In internal and external validation, the previously published models were not able to predict local tumour progression after ablation. • Radiomics prediction models should be investigated in independent validation cohorts to check for reproducibility.

Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies

Liver tumors, whether primary or metastatic, have emerged as a growing concern with substantial global health implications. Timely identification and characterization of liver tumors are pivotal factors in order to provide optimum treatment. Imaging is a crucial part of the detection of liver tumors; however, conventional imaging has shortcomings in the proper characterization of these tumors which leads to the need for tissue biopsy. Artificial intelligence (AI) and radiomics have recently emerged as investigational opportunities with the potential to enhance the detection and characterization of liver lesions. These advancements offer opportunities for better diagnostic accuracy, prognostication, and thereby improving patient care. In particular, these techniques have the potential to predict the histopathology, genotype, and immunophenotype of tumors based on imaging data, hence providing guidance for personalized treatment of such tumors. In this review, we outline the progression and potential of AI in the field of liver oncology imaging, specifically emphasizing manual radiomic techniques and deep learning-based representations. We discuss how these tools can aid in clinical decision-making challenges. These challenges encompass a broad range of tasks, from prognosticating patient outcomes, differentiating benign treatment-related factors and actual disease progression, recognizing uncommon response patterns, and even predicting the genetic and molecular characteristics of the tumors. Lastly, we discuss the pitfalls, technical limitations and future direction of these AI-based techniques.

Three-Dimensional Margin as a Predictor of Local Tumor Progression after Microwave Ablation: Intraprocedural versus 4-8-Week Postablation Assessment

PURPOSE

To evaluate the prognostic accuracy of intraprocedural and 4-8-week (current standard) post-microwave ablation zone (AZ) and margin assessments for prediction of local tumor progression (LTP) using 3-dimensional (3D) software.

MATERIALS AND METHODS

Data regarding 100 colorectal liver metastases (CLMs) in 75 patients were collected from 2 prospective fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT)-guided microwave ablation (MWA) trials. The target CLMs and theoretical 5- and 10-mm margins were segmented and registered intraprocedurally and at 4-8 weeks after MWA contrast-enhanced CT (or magnetic resonance [MR] imaging) using the same methodology and 3D software. Tumor and 5- and 10-mm minimal margin (MM) volumes not covered by the AZ were defined as volumes of insufficient coverage (VICs). The intraprocedural and 4-8-week post-MWA VICs were compared as predictors of LTP using receiver operating characteristic curve analysis.

RESULTS

The median follow-up time was 19.6 months (interquartile range, 7.97-36.5 months). VICs for 5- and 10-mm MMs were predictive of LTP at both time assessments. The highest accuracy for the prediction of LTP was documented with the intra-ablation 5-mm VIC (area under the curve [AUC], 0.78; 95% confidence interval, 0.66-0.89). LTP for a VIC of 6-10-mm margin category was 11.4% compared with 4.3% for >10-mm margin category (P < .001).

CONCLUSIONS

A 3D 5-mm MM is a critical endpoint of thermal ablation, whereas optimal local tumor control is noted with a 10-mm MM. Higher AUCs for prediction of LTP were achieved for intraprocedural evaluation than for the 4-8-week postablation 3D evaluation of the AZ.

Habitat-based radiomics analysis for evaluating immediate response in colorectal cancer lung metastases treated by radiofrequency ablation

PURPOSE

To create radiomics signatures based on habitat to assess the instant response in lung metastases of colorectal cancer (CRC) after radiofrequency ablation (RFA).

METHODS

Between August 2016 and June 2019, we retrospectively included 515 lung metastases in 233 CRC patients who received RFA (412 in the training group and 103 in the test group). Multivariable analysis was performed to identify independent risk factors for developing the clinical model. Tumor and ablation regions of interest (ROI) were split into three spatial habitats through K-means clustering and dilated with 5 mm and 10 mm thicknesses. Radiomics signatures of intratumor, peritumor, and habitat were developed using the features extracted from intraoperative CT data. The performance of these signatures was primarily evaluated using the area under the receiver operating characteristics curve (AUC) via the DeLong test, calibration curves through the Hosmer-Lemeshow test, and decision curve analysis.

RESULTS

A total of 412 out of 515 metastases (80%) achieved complete response. Four clinical variables (cancer antigen 19-9, simultaneous systemic treatment, site of lung metastases, and electrode type) were utilized to construct the clinical model. The Habitat signature was combined with the Peri-5 signature, which achieved a higher AUC than the Peri-10 signature in the test set (0.825 vs. 0.816). The Habitat+Peri-5 signature notably surpassed the clinical and intratumor radiomics signatures (AUC: 0.870 in the test set; both, p < 0.05), displaying improved calibration and clinical practicality.

CONCLUSIONS

The habitat-based radiomics signature can offer precise predictions and valuable assistance to physicians in developing personalized treatment strategies.

The Role of Artificial Intelligence and Texture Analysis in Interventional Radiological Treatments of Liver Masses: A Narrative Review

Liver lesions, including both benign and malignant tumors, pose significant challenges in interventional radiological treatment planning and prognostication. The emerging field of artificial intelligence (AI) and its integration with texture analysis techniques have shown promising potential in predicting treatment outcomes, enhancing precision, and aiding clinical decision-making. This comprehensive review aims to summarize the current state-of-the-art research on the application of AI and texture analysis in determining treatment response, recurrence rates, and overall survival outcomes for patients undergoing interventional radiological treatment for liver lesions. Furthermore, the review addresses the challenges associated with the implementation of AI and texture analysis in clinical practice, including data acquisition, standardization of imaging protocols, and model validation. Future directions and potential advancements in this field are discussed. Integration of multi-modal imaging data, incorporation of genomics and clinical data, and the development of predictive models with enhanced interpretability are proposed as potential avenues for further research. In conclusion, the application of AI and texture analysis in predicting outcomes of interventional radiological treatment for liver lesions shows great promise in augmenting clinical decision-making and improving patient care. By leveraging these technologies, clinicians can potentially enhance treatment planning, optimize intervention strategies, and ultimately improve patient outcomes in the management of liver lesions.

CT-based radiomics analysis to predict local progression of recurrent colorectal liver metastases after microwave ablation

The objective of this study is to establish and validate a radiomics nomogram for prediction of local tumor progression (LTP) after microwave ablation (MWA) for recurrent colorectal liver metastases (CRLM) after hepatic resection. We included 318 consecutive recurrent CRLM patients (216 of training while 102 of validation cohort) with contrast-enhanced computerized tomography images treated with MWA between January 2014 and October 2018. Support vector machine-generated radiomics signature was incorporated together with clinical information to establish a radiomics nomogram. Our constructed radiomics signature including 15 features (first-order intensity statistics features, shape and size-based features, gray level size zone/dependence matrix features) performed well in assessing LTP for both cohorts. With regard to its predictive performance, its C-index was 0.912, compared to the clinical or radiomics models only (c-statistic 0.89 and 0.75, respectively) in the training cohort. In the validation cohort, the radiomics nomogram had better performance (area under the curve = 0.89) compared to the radiomics and clinical models (0.85 and 0.69). According to decision curve analysis, our as-constructed radiomics nomogram showed high clinical utility. As revealed by survival analysis, LTP showed worse progression-free survival (3-year progression-free survival 42.6% vs 78.4%, P < .01). High-risk patients identified using this radiomics signature exhibited worse LTP compared with low-risk patients (3-year LTP 80.2% vs 48.6%, P < .01). A radiomics-based nomogram of pre-ablation computerized tomography imaging may be the precious biomarker model for predicting LTP and personalized risk stratification for recurrent CRLM after hepatic resection treated by MWA.

Imaging in interventional oncology, the better you see, the better you treat

Imaging and image processing is the fundamental pillar of interventional oncology in which diagnostic, procedure planning, treatment and follow-up are sustained. Knowing all the possibilities that the different image modalities can offer is capital to select the most appropriate and accurate guidance for interventional procedures. Despite there is a wide variability in physicians preferences and availability of the different image modalities to guide interventional procedures, it is important to recognize the advantages and limitations for each of them. In this review, we aim to provide an overview of the most frequently used image guidance modalities for interventional procedures and its typical and future applications including angiography, computed tomography (CT) and spectral CT, magnetic resonance imaging, Ultrasound and the use of hybrid systems. Finally, we resume the possible role of artificial intelligence related to image in patient selection, treatment and follow-up.

Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment

OBJECTIVE

The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated.

METHODS

The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed.

RESULTS

The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence.

CONCLUSIONS

The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.

A CT-based radiomics nomogram for predicting histopathologic growth patterns of colorectal liver metastases

PURPOSE

To develop a computed tomography (CT)-based radiomics nomogram for pre-treatment prediction of histopathologic growth patterns (HGPs) in colorectal liver metastases (CRLM) and to validate its accuracy and clinical value.

MATERIALS AND METHODS

This retrospective study included a total of 197 CRLM from 92 patients. Lesions from CRLM were randomly divided into the training study (n = 137) and the validation study (n = 60) with the ratio of 3:1 for model construction and internal validation. The least absolute shrinkage and selection operator (LASSO) was used to screen features. Radiomics score (rad-score) was calculated to generate radiomics features. A predictive radiomics nomogram based on rad-score and clinical features was developed using random forest (RF). The performances of clinical model, radiomic model and radiomics nomogram were thoroughly evaluated by the DeLong test, decision curve analysis (DCA) and clinical impact curve (CIC) allowing for generation of an optimal predictive model.

RESULTS

The radiological nomogram model consists of three independent predictors, including rad-score, T-stage, and enhancement rim on PVP. Training and validation results demonstrated the high-performance level of the model of area under curve (AUC) of 0.86 and 0.84, respectively. The radiomic nomogram model can achieve better diagnostic performance than the clinical model, yielding greater net clinical benefit compared to the clinical model alone.

CONCLUSIONS

A CT-based radiomics nomogram can be used to predict HGPs in CRLM. Preoperative non-invasive identification of HGPs could further facilitate clinical treatment and provide personalized treatment plans for patients with liver metastases from colorectal cancer.

Tumor and Ablation Margin Visibility during Cryoablation of Musculoskeletal Tumors: Comparing Intraprocedural PET/CT Images with CT-Only Images

PURPOSE

To compare tumor and ice-ball margin visibility on intraprocedural positron emission tomography (PET)/computed tomography (CT) and CT-only images and report technical success, local tumor progression, and adverse event rates for PET/CT-guided cryoablation procedures for musculoskeletal tumors.

MATERIALS AND METHODS

This Health Insurance Portability and Accountability Act (HIPAA)-compliant and institutional review board-approved retrospective study evaluated 20 PET/CT-guided cryoablation procedures performed with palliative and/or curative intent to treat 15 musculoskeletal tumors in 15 patients from 2012 to 2021. Cryoablation was performed using general anesthesia and PET/CT guidance. Procedural images were reviewed to determine the following: (a) whether the tumor borders could be fully assessed on PET/CT or CT-only images; and (b) whether tumor ice-ball margins could be fully assessed on PET/CT or CT-only images. The ability to visualize tumor borders and ice-ball margins on PET/CT images was compared with that on CT-only images.

RESULTS

Tumor borders were fully assessable for 100% (20 of 20; 95% CI, 0.83-1) of procedures on PET/CT versus 20% (4 of 20; 95 CI, 0.057-0.44) of procedures on CT only (P < .001). The tumor ice-ball margin was fully assessable in 80% (16 of 20; 95% CI, 0.56-0.94) of procedures using PET/CT versus 5% (1 of 20; 95% CI, 0.0013-0.25) of procedures using CT only (P < .001). Primary technical success was achieved in 75% (15 of 20; 95% CI, 0.51-0.91) of procedures. There was local tumor progression in 23% (3/13; 95% CI, 0.050-0.54) of the treated tumors with at least 6 months of follow-up. There were 3 adverse events (1 Grade 3, 1 Grade 2, and 1 Grade 1).

CONCLUSIONS

PET/CT-guided cryoablation of musculoskeletal tumors can provide superior intraprocedural visualization of the tumor and ice-ball margins compared with that provided by CT alone. Further studies are warranted to confirm the long-term efficacy and safety of this approach.

A prospective multicentre trial on survival after Microwave Ablation VErsus Resection for Resectable Colorectal liver metastases (MAVERRIC)

AIM

This multi-centre prospective cohort study aimed to investigate non-inferiority in patients' overall survival when treating potentially resectable colorectal cancer liver metastasis (CRLM) with stereotactic microwave ablation (SMWA) as opposed to hepatic resection (HR).

METHODS

Patients with no more than 5 CRLM no larger than 30 mm, deemed eligible for both SMWA and hepatic resection at the local multidisciplinary team meetings, were deliberately treated with SMWA (study group). The contemporary control group consisted of patients with no more than 5 CRLM, none larger than 30 mm, treated with HR, extracted from a prospectively maintained nationwide Swedish database. After propensity-score matching, 3-year overall survival (OS) was compared as the primary outcome using Kaplan-Meier and Cox regression analyses.

RESULTS

All patients in the study group (n = 98) were matched to 158 patients from the control group (mean standardised difference in baseline covariates = 0.077). OS rates at 3 years were 78% (Confidence interval [CI] 68-85%) after SMWA versus 76% (CI 69-82%) after HR (stratified Log-rank test p = 0.861). Estimated 5-year OS rates were 56% (CI 45-66%) versus 58% (CI 50-66%). The adjusted hazard ratio for treatment type was 1.020 (CI 0.689-1.510). Overall and major complications were lower after SMWA (percentage decrease 67% and 80%, p < 0.01). Hepatic retreatments were more frequent after SMWA (percentage increase 78%, p < 0.01).

CONCLUSION

SMWA is a valid curative-intent treatment alternative to surgical resection for small resectable CRLM. It represents an attractive option in terms of treatment-related morbidity with potentially wider options regarding hepatic retreatments over the future course of disease.

MRI-based radiomics model and nomogram for predicting the outcome of locoregional treatment in patients with hepatocellular carcinoma

BACKGROUND

Prediction of locoregional treatment response is important for further therapeutic strategy in patients with hepatocellular carcinoma. This study aimed to investigate the role of MRI-based radiomics and nomogram for predicting the outcome of locoregional treatment in patients with hepatocellular carcinoma.

METHODS

The initial postoperative MRI after locoregional treatment in 100 patients with hepatocellular carcinoma was retrospectively analysed. The outcome was evaluated according to mRECIST at 6 months. We delineated the tumour volume of interest on arterial phase, portal venous phase and T2WI. The radiomics features were selected by using the independent sample t test or nonparametric Mann‒Whitney U test and the least absolute shrinkage and selection operator. The clinical variables were selected by using univariate analysis and multivariate analysis. The radiomics model and combined model were constructed via multivariate logistic regression analysis. A nomogram was constructed that incorporated the Rad score and selected clinical variables.

RESULTS

Fifty patients had an objective response, and fifty patients had a nonresponse. Nine radiomics features in the arterial phase were selected, but none of the portal venous phase or T2WI radiomics features were predictive of the treatment response. The best radiomics model showed an AUC of 0.833. Two clinical variables (hCRP and therapy method) were selected. The AUC of the combined model was 0.867. There was no significant difference in the AUC between the combined model and the best radiomics model (P = 0.573). Decision curve analysis demonstrated the nomogram has satisfactory predictive value.

CONCLUSIONS

MRI-based radiomics analysis may serve as a promising and noninvasive tool to predict outcome of locoregional treatment in HCC patients, which will facilitate the individualized follow-up and further therapeutic strategies guidance.

Prediction of Local Tumor Progression After Microwave Ablation in Colorectal Carcinoma Liver Metastases Patients by MRI Radiomics and Clinical Characteristics-Based Combined Model: Preliminary Results

PURPOSE

To investigate the predictability of local tumor progression (LTP) after microwave ablation (MWA) in colorectal carcinoma liver metastases (CRLM) patients by magnetic resonance imaging (MRI) radiomics and clinical characteristics-based combined model.

MATERIALS AND METHODS

Forty-two consecutive CRLM patients (67 tumors) with post-MWA complete response at 1st month MRI were included in this retrospective study. One hundred and eleven radiomics features were extracted for each tumor and for each phase by manual segmentation from pre-treatment MRI T2 fat-suppressed (Phase 2) and early arterial phase T1 fat-suppressed sequences (Phase 1). A clinical model was constructed using clinical data, two combined models were created with feature reduction and machine learning by combining clinical data and Phase 2 and Phase 1 radiomics features. The predicting performance for LTP development was investigated.

RESULTS

LTP developed in 7 patients (16.6%) and 11 tumors (16.4%). In the clinical model, the presence of extrahepatic metastases before MWA was associated with a high probability of LTP (p < 0.001). The pre-treatment levels of carbohydrate antigen 19-9 and carcinoembryonic antigen were higher in the LTP group (p = 0.010, p = 0.020, respectively). Patients with LTP had statistically significantly higher radiomics scores in both phases (p < 0.001 for Phase 2 and p = 0.001 for Phase 1). The classification performance of the combined model 2, created by using clinical data and Phase 2-based radiomics features, achieved the highest discriminative performance in predicting LTP (p = 0,014; the area under curve (AUC) value 0.981 (95% CI 0.948-0.990). The combined model 1, created using clinical data and Phase 1-based radiomics features (AUC value 0,927 (95% CI 0.860-0.993, p < 0.001)) and the clinical model alone [AUC value of 0.887 (95% CI 0.807-0.967, p < 0.001)] had similar performance.

CONCLUSION

Combined models based on clinical data and radiomics features obtained from T2 fat-suppressed and early arterial-phase T1 fat-suppressed MRI are valuable markers in predicting LTP after MWA in CRLM patients. Large-scale studies with internal and external validations are needed to come to a firm conclusion on the predictability of radiomics models in CRLM patients.

Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics

In this narrative review, we reported un up-to-date on the role of radiomics to assess prognostic features, which can impact on the liver metastases patient treatment choice. In the liver metastases patients, the possibility to assess mutational status (RAS or MSI), the tumor growth pattern and the histological subtype (NOS or mucinous) allows a better treatment selection to avoid unnecessary therapies. However, today, the detection of these features require an invasive approach. Recently, radiomics analysis application has improved rapidly, with a consequent growing interest in the oncological field. Radiomics analysis allows the textural characteristics assessment, which are correlated to biological data. This approach is captivating since it should allow to extract biological data from the radiological images, without invasive approach, so that to reduce costs and time, avoiding any risk for the patients. Several studies showed the ability of Radiomics to identify mutational status, tumor growth pattern and histological type in colorectal liver metastases. Although, radiomics analysis in a non-invasive and repeatable way, however features as the poor standardization and generalization of clinical studies results limit the translation of this analysis into clinical practice. Clear limits are data-quality control, reproducibility, repeatability, generalizability of results, and issues related to model overfitting.

A CT-based radiomics approach to predict immediate response of radiofrequency ablation in colorectal cancer lung metastases

Objectives

To objectively and accurately assess the immediate efficacy of radiofrequency ablation (RFA) on colorectal cancer (CRC) lung metastases, the novel multimodal data fusion model based on radiomics features and clinical variables was developed.

Methods

This case-control single-center retrospective study included 479 lung metastases treated with RFA in 198 CRC patients. Clinical and radiological data before and intraoperative computed tomography (CT) scans were retrieved. The relative radiomics features were extracted from pre- and immediate post-RFA CT scans by maximum relevance and minimum redundancy algorithm (MRMRA). The Gaussian mixture model (GMM) was used to divide the data of the training dataset and testing dataset. In the process of modeling in the training set, radiomics model, clinical model and fusion model were built based on a random forest classifier. Finally, verification was carried out on an independent test dataset. The receiver operating characteristic curves (ROC) were drawn based on the obtained predicted scores, and the corresponding area under ROC curve (AUC), accuracy, sensitivity, and specificity were calculated and compared.

Results

Among the 479 pulmonary metastases, 379 had complete response (CR) ablation and 100 had incomplete response ablation. Three hundred eighty-six lesions were selected to construct a training dataset and 93 lesions to construct a testing dataset. The multivariate logistic regression analysis revealed cancer antigen 19-9 (CA19-9, p<0.001) and the location of the metastases (p< 0.05) as independent risk factors. Significant correlations were observed between complete ablation and 9 radiomics features. The best prediction performance was achieved with the proposed multimodal data fusion model integrating radiomic features and clinical variables with the highest accuracy (82.6%), AUC value (0.921), sensitivity (80.3%), and specificity (81.4%).

Conclusion

This novel multimodal data fusion model was demonstrated efficient for immediate efficacy evaluation after RFA for CRC lung metastases, which could benefit necessary complementary treatment.

Immunofluorescence Assay of Ablated Colorectal Liver Metastases: The Frozen Section of Image-Guided Tumor Ablation?

PURPOSE

To validate an immunofluorescence assay (IFA) detecting residual viable tumor (VT) as intraprocedural thermal ablation (TA) zone assessment and demonstrate its prognostic value for local tumor progression (LTP) after colorectal liver metastasis (CLM) TA.

MATERIALS AND METHODS

This prospective study, approved by the institutional review board, included 99 patients with 155 CLMs ablated between November 2009 and January 2019. Tissue samples from the ablation zone (AZ) center and minimal margin underwent immunofluorescent microscopic examination interrogating cellular morphology and mitochondrial viability (IFA) within 30 minutes after ablation. The same tissue samples were subsequently evaluated with standard morphologic and immunohistochemical methods. The sensitivity, specificity, and overall accuracy of IFA versus standard morphologic and immunohistochemical examination were calculated. The LTP-free survival rates were evaluated for the 12-month follow-up period.

RESULTS

Of the 311 tissue samples stained, 304 (98%) were deemed evaluable. Of these specimens, 27% (81/304) were considered positive for the presence of VT. The accuracy of IFA was 94% (286/304). The sensitivity and specificity were 100% (63/63) and 93% (223/241), respectively. The 18 false-positive IFA assessments corresponded to samples that included viable cholangiocytes. The 12-month LTP-free survival was 59% versus 78% for IFA positive versus negative for VT AZs, respectively (P < .001). There was no difference in LTP between margin positive only and central AZ-positive tumors (25% vs 31%, P = 1).

CONCLUSIONS

The IFA assessment of the AZ can be completed intraprocedurally and serve as a valid real-time biomarker of complete tumor eradication or detect residual VT after TA. This method could improve tumor control by TA.

Biopsy and Margins Optimize Outcomes after Thermal Ablation of Colorectal Liver Metastases

Simple Summary

Thermal ablation (TA) is a non-surgical treatment of cancer that has been used with success in the treatment of colorectal liver metastases (CLM). TA consists of burning the cancer and a rim of surrounding tissue (margin) with a special needle placed in the tumor under image guidance. Despite the technological evolution of TA, tumor progression/recurrence rates remain higher than expected. We present a method that combines tissue and imaging tests performed immediately after ablation to determine whether there is complete tumor destruction or remaining live cancer cells that can cause tumor progression/recurrence. This information can provide guidance for additional treatments for patients with evidence of residual cancer, i.e.,: additional TA at the same or subsequent sitting, or additional chemotherapy and short-interval imaging follow-up to detect recurrence. The presented method proposes a clinical practice paradigm change that can improve clinical outcomes in a large population of patients with CLM treated by TA.

Abstract

Background: Thermal ablation is a definitive local treatment for selected colorectal liver metastases (CLM) that can be ablated with adequate margins. A critical limitation has been local tumor progression (LTP). Methods: This prospective, single-group, phase 2 study enrolled patients with CLM < 5 cm in maximum diameter, at a tertiary cancer center between November 2009 and February 2019. Biopsy of the ablation zone center and margin was performed immediately after ablation. Viable tumor in tissue biopsy and ablation margins < 5 mm were assessed as predictors of 12-month LTP. Results: We enrolled 107 patients with 182 CLMs. Mean tumor size was 2.0 (range, 0.6–4.6) cm. Microwave ablation was used in 51% and radiofrequency ablation in 49% of tumors. The 12- and 24-month cumulative incidence of LTP was 22% (95% confidence interval [CI]: 17, 29) and 29% (95% CI: 23, 36), respectively. LTP at 12 months was 7% (95% CI: 3, 14) for the biopsy tumor-negative ablation zone with margins ≥ 5 mm vs. 63% (95% CI: 35, 85) for the biopsy-positive ablation zone with margins < 5 mm (p < 0.001). Conclusions: Biopsy-proven complete tumor ablation with margins of at least 5 mm achieves optimal local tumor control for CLM, regardless of the ablation modality used.

Radiogenomics: Hunting Down Liver Metastasis in Colorectal Cancer Patients

Simple Summary

Colorectal cancer (CRC) is the third leading cause of cancer and the second most deadly tumor type in the world. The liver is the most common site of metastasis in CRC patients. The conversion of new imaging biomarkers into diagnostic, prognostic and predictive signatures, by the development of radiomics and radiogenomics, is an important potential new tool for the clinical management of cancer patients. In this review, we assess the knowledge gained from radiomics and radiogenomics studies in liver metastatic colorectal cancer patients and their possible use for early diagnosis, response assessment and treatment decisions.

Abstract

Radiomics is a developing new discipline that analyzes conventional medical images to extract quantifiable data that can be mined for new biomarkers that show the biology of pathological processes at microscopic levels. These data can be converted into image-based signatures to improve diagnostic, prognostic and predictive accuracy in cancer patients. The combination of radiomics and molecular data, called radiogenomics, has clear implications for cancer patients’ management. Though some studies have focused on radiogenomics signatures in hepatocellular carcinoma patients, only a few have examined colorectal cancer metastatic lesions in the liver. Moreover, the need to differentiate between liver lesions is fundamental for accurate diagnosis and treatment. In this review, we summarize the knowledge gained from radiomics and radiogenomics studies in hepatic metastatic colorectal cancer patients and their use in early diagnosis, response assessment and treatment decisions. We also investigate their value as possible prognostic biomarkers. In addition, the great potential of image mining to provide a comprehensive view of liver niche formation is examined thoroughly. Finally, new challenges and current limitations for the early detection of the liver premetastatic niche, based on radiomics and radiogenomics, are also discussed.