Aflibercept or bevacizumab in combination with FOLFIRI as second-line treatment of mRAS metastatic colorectal cancer patients: the ARBITRATION study protocol

Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction

PURPOSE

To assess the efficacy of machine learning and radiomics analysis by computed tomography (CT) in presurgical setting, to predict RAS mutational status in colorectal liver metastases.

METHODS

Patient selection in a retrospective study was carried out from January 2018 to May 2021 considering the following inclusion criteria: patients subjected to surgical resection for liver metastases; proven pathological liver metastases; patients subjected to enhanced CT examination in the presurgical setting with a good quality of images; and RAS assessment as standard reference. A total of 851 radiomics features were extracted using the PyRadiomics Python package from the Slicer 3D image computing platform after slice-by-slice segmentation on CT portal phase by two expert radiologists of each individual liver metastasis performed first independently by the individual reader and then in consensus. Balancing technique was performed, and inter- and intraclass correlation coefficients were calculated to assess the between-observer and within-observer reproducibility of features. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers were considered. Moreover, features selection was performed before and after a normalized procedure using two different methods (3-sigma and z-score).

RESULTS

Seventy-seven liver metastases in 28 patients with a mean age of 60 years (range 40-80 years) were analyzed. The best predictors, at univariate analysis for both normalized procedures, were original_shape_Maximum2DDiameter and wavelet_HLL_glcm_InverseVariance that reached an accuracy of 80%, an AUC ≥ 0.75, a sensitivity ≥ 80% and a specificity ≥ 70% (p value <  < 0.01). However, a multivariate analysis significantly increased the accuracy in RAS prediction when a linear regression model (LRM) was used. The best performance was obtained using a LRM combining linearly 12 robust features after a z-score normalization procedure: AUC of 0.953, accuracy 98%, sensitivity 96%, specificity of 100%, PPV 100% and NPV 96% (p value <  < 0.01). No statistically significant increase was obtained considering the tested machine learning both without normalization and with normalization methods.

CONCLUSIONS

Normalized approach in CT radiomics analysis allows to predict RAS mutational status in colorectal liver metastases patients.

Machine learning-based radiomics analysis in predicting RAS mutational status using magnetic resonance imaging

PURPOSE

To assess the efficacy of radiomics features, obtained by magnetic resonance imaging (MRI) with hepatospecific contrast agent, in pre-surgical setting, to predict RAS mutational status in liver metastases.

METHODS

Patients with MRI in pre-surgical setting were enrolled in a retrospective study. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. The features were extracted considering the agreement with the Imaging Biomarker Standardization Initiative (IBSI). Balancing was performed through synthesis of samples for the underrepresented classes using the self-adaptive synthetic oversampling (SASYNO) approach. Inter- and intraclass correlation coefficients (ICC) were calculated to assess the between-observer and within-observer reproducibility of all radiomics characteristics. For continuous variables, nonparametric Wilcoxon-Mann-Whitney test was utilized. Benjamini and Hochberg's false discovery rate (FDR) adjustment for multiple testing was used. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. Moreover, features selection were performed before and after a normalized procedure using two different methods (3-sigma and z-score). McNemar test was used to assess differences statistically significant between dichotomic tables. All statistical procedures were done using MATLAB R2021b Statistics and Machine Toolbox (MathWorks, Natick, MA, USA).

RESULTS

Seven normalized radiomics features, extracted from arterial phase, 11 normalized radiomics features, from portal phase, 12 normalized radiomics features from hepatobiliary phase and 12 normalized features from T2-W SPACE sequence were robust predictors of RAS mutational status. The multivariate analysis increased significantly the accuracy in RAS prediction when a LRM was used, combining 12 robust normalized features extracted by VIBE hepatobiliary phase reaching an accuracy of 99%, a sensitivity 97%, a specificity of 100%, a PPV of 100% and a NPV of 98%. No statistically significant increase was obtained, considering the tested classifiers DT, KNN and SVM, both without normalization and with normalization methods.

CONCLUSIONS

Normalized approach in MRI radiomics analysis allows to predict RAS mutational status.

Update on Targeted Therapy and Immunotherapy for Metastatic Colorectal Cancer

Metastatic colorectal cancer remains a deadly malignancy and is the third leading cause of cancer-related death. The mainstay of treatment for metastatic colorectal cancer is chemotherapy, but unfortunately, even with recent progress, overall survival is still poor. Colorectal cancer is a heterogeneous disease, and the underlying genetic differences among tumors can define the behavior and prognosis of the disease. Given the limitations of cytotoxic chemotherapy, research has focused on developing targeted therapy based on molecular subtyping. Since the early 2000s, multiple targeted therapies have demonstrated efficacy in treating metastatic colorectal cancer and have received FDA approval. The epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF), and DNA mismatch repair pathways have demonstrated promising results for targeted therapies. As new gene mutations and proteins involved in the oncogenesis of metastatic colorectal cancer are identified, new targets will continue to emerge. We herein provide a summary of the updated literature regarding targeted therapies for patients with mCRC.

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.

Colorectal cancer tumor stem cells mediate bevacizumab resistance through the signal IL-22-STAT3 signaling pathway

Bevacizumab is the standard treatment for colorectal cancer (CRC) in the advanced stage. However, poor diagnosis identified due to the bevacizumab resistance in many CRC patients. Previous studies have found that CRC stem cells (CCSCs) and interleukin 22 (IL-22) are involved in the resistance of bevacizumab, however, the mechanism of remains unclear. In this study, we established the bevacizumab drug-resistant cell line HCT-116-R by concentration gradient method, and the cell viability was detected by CCK-8 assay. The resistance of bevacizumab in CRC cell lines HCT-116-R was identified by characterizing epithelial-mesenchymal transition (EMT). Additionally, HCT-116-R cell lines were isolated from CCSCs and their tumorigenicity was validated in nude mice. We observed that that compared with the matched group, the expression of IL-22, IL-22R, STAT3, and GP130 in drug-resistant cells increased distinctly, with blocked IL-22 cells were successfully constructed by lentiviral interference. The level of proteins in stem cell landmarks (EpCAM, CD133), and stem cell landmarks (Oct4, Sox2) was identified by western blotting. Furthermore, the IL-22 role was evaluated by xenograft model. We found that short hairpin RNA (shRNA) suppression of IL-22 expression can restore the sensitivity of drug-resistant CCSCs to bevacizumab, Moreover, xenograft tumor models show that suppression of IL-22 can increase the anti-tumor influence of bevacizumab. In summary, we demonstrated that CCSCs play a major part in bevacizumab-resistant CRC. Inhibiting the signaling pathway of IL-22/STAT3 can improve the anti-tumor influence on bevacizumab in vitro and in vivo. Thus, IL-22 may represent a new anti-bevacizumab target in CRC.

A systemic review and meta-analysis of Aflibercept plus FOLFIRI regimen as a second-line treatment for metastatic colorectal cancer: A PRISMA compliant pooled analysis of randomized controlled trials and single arm studies to assess efficacy and safety

BACKGROUND AND OBJECTIVE

Aflibercept; a decoy receptor for vascular endothelial growth factors (VEGFs) and placental growth factor (PLGF), in combination with FOLFIRI (leucovorin calcium, fluorouracil, irinotecan hydrochloride) chemotherapy regime, was FDA approved in 2012 as second-line salvage chemotherapy for metastatic colorectal cancer (mCRC). This is the first systematic review, and meta-analysis-based evidence to determine the efficacy and safety of Aflibercept plus FOLFIRI regimen pooling randomized controlled trials and single-arm studies.

METHOD

PubMed, Cochrane library, Embase, and Clinical trial.gov were systematically searched for published randomized controlled trials, single-arm studies, and national patient programs on aflibercept plus FOLFIRI chemotherapy for the treatment of mCRC till 11/10/2022.

RESULT

Ten studies met the inclusion criteria comprising 1075 patients for efficacy studies and 2027 patients for safety studies. The pooled prevalences were 18% (95% CI, 5%-37%, p = 0.00) for 12m PFS and 61% (95% CI, 53% - 68%, p = 0.00) for 12m OS. The pooled prevalences were 69% (95% CI, 55% - 82%, p = 0.00) for any grade 3-4 toxicities, 10% (95% CI, 5% - 16%, p = 0.00) for grade 3-4 diarrhea, 13% (95% CI, 5% - 24%, p = 0.00) for grade 3-4 hypertension, 31% (95% CI, 22% - 40%, p = 0.00) for grade 3-4 neutropenia and 5% (95% CI, 2% - 7%, p = 0.00) for grade 3-4 venous thromboembolic event.

CONCLUSION

Our meta-analysis shows that the aflibercept plus FOLFIRI combination shows better survival efficacies however; it is also associated with more high-grade adverse events.

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.

Immunotherapy Assessment: A New Paradigm for Radiologists

Immunotherapy denotes an exemplar change in an oncological setting. Despite the effective application of these treatments across a broad range of tumors, only a minority of patients have beneficial effects. The efficacy of immunotherapy is affected by several factors, including human immunity, which is strongly correlated to genetic features, such as intra-tumor heterogeneity. Classic imaging assessment, based on computed tomography (CT) or magnetic resonance imaging (MRI), which is useful for conventional treatments, has a limited role in immunotherapy. The reason is due to different patterns of response and/or progression during this kind of treatment which differs from those seen during other treatments, such as the possibility to assess the wide spectrum of immunotherapy-correlated toxic effects (ir-AEs) as soon as possible. In addition, considering the unusual response patterns, the limits of conventional response criteria and the necessity of using related immune-response criteria are clear. Radiomics analysis is a recent field of great interest in a radiological setting and recently it has grown the idea that we could identify patients who will be fit for this treatment or who will develop ir-AEs.