(18)F-FDG PET/CT imaging in rectal cancer: relationship with the RAS mutational status

Image Analysis as tool for Predicting Colorectal Cancer Molecular Alterations: A Scoping Review

Objectives

Among the most important diagnostic indicators of colorectal cancer; however, measuring molecular alterations are invasive and expensive. This study aimed to investigate the application of image processing to predict molecular alterations in colorectal cancer.

Methods

In this scoping review, we searched for relevant literature by searching the Web of Science, Scopus, and PubMed databases. The method of selecting the articles and reporting the findings was according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses; moreover, the Strengthening the Reporting of Observational Studies in Epidemiology checklist was used to assess the quality of the studies.

Results

Sixty seven out of 2,223 articles, 67 were relevant to the aim of the study, and finally 41 studies with sufficient quality were reviewed. The prediction of Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS), Neuroblastoma RAS Viral (NRAS), B-Raf proto-oncogene, serine/threonine kinase (BRAF), Tumor Protein 53 (TP53), Adenomatous Polyposis Coli, and microsatellite instability (MSI) with the help of image analysis has received more attention than other molecular characteristics. The studies used computed tomography (CT), magnetic resonance imaging (MRI), and 18F-FDG positron emission tomography (PET)/CT with radionics and quantitative analysis to predict molecular alterations in colorectal cancer, analyzing features like texture, maximum standard uptake value, and MTV using various statistical methods. In 39 studies, there was a significant relationship between the features extracted from these images and molecular alterations. Different modalities were used to measure the area under the receiver operating characteristic curve for predicting the alterations in KRAS, MSI, BRAF, and TP53, with an average of 78, 81, 80 and 71%, respectively.

Conclusion

This scoping review underscores the potential of radiogenomics in predicting molecular alterations in colorectal cancer through non-invasive imaging modalities, like CT, MRI, and 18F-FDG PET/CT. The analysis of 41 studies showed the appropriate prediction of key alterations, such as KRAS, NRAS, BRAF, TP53, and MSI, highlighting the promise of radionics and texture features in enhancing predictive accuracy.

Using tumor habitat-derived radiomic analysis during pretreatment 18F-FDG PET for predicting KRAS/NRAS/BRAF mutations in colorectal cancer

BACKGROUND

To investigate the association between Kirsten rat sarcoma viral oncogene homolog (KRAS) / neuroblastoma rat sarcoma viral oncogene homolog (NRAS) /v-raf murine sarcoma viral oncogene homolog B (BRAF) mutations and the tumor habitat-derived radiomic features obtained during pretreatment 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in patients with colorectal cancer (CRC).

METHODS

We retrospectively enrolled 62 patients with CRC who had undergone 18F-FDG PET/computed tomography from January 2017 to July 2022 before the initiation of therapy. The patients were randomly split into training and validation cohorts with a ratio of 6:4. The whole tumor region radiomic features, habitat-derived radiomic features, and metabolic parameters were extracted from 18F-FDG PET images. After reducing the feature dimension and selecting meaningful features, we constructed a hierarchical model of KRAS/NRAS/BRAF mutations by using the support vector machine. The convergence of the model was evaluated by using learning curve, and its performance was assessed based on the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis. The SHapley Additive exPlanation was used to interpret the contributions of various features to predictions of the model.

RESULTS

The model constructed by using habitat-derived radiomic features had adequate predictive power with respect to KRAS/NRAS/BRAF mutations, with an AUC of 0.759 (95% CI: 0.585-0.909) on the training cohort and that of 0.701 (95% CI: 0.468-0.916) on the validation cohort. The model exhibited good convergence, suitable calibration, and clinical application value. The results of the SHapley Additive explanation showed that the peritumoral habitat and a high_metabolism habitat had the greatest impact on predictions of the model. No meaningful whole tumor region radiomic features or metabolic parameters were retained during feature selection.

CONCLUSION

The habitat-derived radiomic features were found to be helpful in stratifying the status of KRAS/NRAS/BRAF in CRC patients. The approach proposed here has significant implications for adjuvant treatment decisions in patients with CRC, and needs to be further validated on a larger prospective cohort.

The Role of Radiomics in Rectal Cancer

PURPOSE

Radiomics is a promising method for advancing imaging assessment in rectal cancer. This review aims to describe the emerging role of radiomics in the imaging assessment of rectal cancer, including various applications of radiomics based on CT, MRI, or PET/CT.

METHODS

We conducted a literature review to highlight the progress of radiomic research to date and the challenges that need to be addressed before radiomics can be implemented clinically.

RESULTS

The results suggest that radiomics has the potential to provide valuable information for clinical decision-making in rectal cancer. However, there are still challenges in terms of standardization of imaging protocols, feature extraction, and validation of radiomic models. Despite these challenges, radiomics holds great promise for personalized medicine in rectal cancer, with the potential to improve diagnosis, prognosis, and treatment planning. Further research is needed to validate the clinical utility of radiomics and to establish its role in routine clinical practice.

CONCLUSION

Overall, radiomics has emerged as a powerful tool for improving the imaging assessment of rectal cancer, and its potential benefits should not be underestimated.

Phenomic Imaging

Human phenomics is defined as the comprehensive collection of observable phenotypes and characteristics influenced by a complex interplay among factors at multiple scales. These factors include genes, epigenetics at the microscopic level, organs, microbiome at the mesoscopic level, and diet and environmental exposures at the macroscopic level. "Phenomic imaging" utilizes various imaging techniques to visualize and measure anatomical structures, biological functions, metabolic processes, and biochemical activities across different scales, both in vivo and ex vivo. Unlike conventional medical imaging focused on disease diagnosis, phenomic imaging captures both normal and abnormal traits, facilitating detailed correlations between macro- and micro-phenotypes. This approach plays a crucial role in deciphering phenomes. This review provides an overview of different phenomic imaging modalities and their applications in human phenomics. Additionally, it explores the associations between phenomic imaging and other omics disciplines, including genomics, transcriptomics, proteomics, immunomics, and metabolomics. By integrating phenomic imaging with other omics data, such as genomics and metabolomics, a comprehensive understanding of biological systems can be achieved. This integration paves the way for the development of new therapeutic approaches and diagnostic tools.

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.

Does high [18F]FDG uptake always mean poor prognosis? Colon cancer with high-level microsatellite instability is associated with high [18F]FDG uptake on PET/CT

OBJECTIVES

High-level microsatellite instability (MSI-high) is generally associated with higher F-18 fluorodeoxyglucose ([18F]FDG) uptake than stable microsatellite (MSI-stable) tumors. However, MSI-high tumors have better prognosis, which is in contrast with general understanding that high [18F]FDG uptake correlates with poor prognosis. This study evaluated metastasis incidence with MSI status and [18F]FDG uptake.

METHODS

We retrospectively reviewed 108 right-side colon cancer patients who underwent preoperative [18F]FDG PET/CT and postoperative MSI evaluations using a standard polymerase chain reaction at five Bethesda guidelines panel loci. The maximum standard uptake value (SUVmax), SUVmax tumor-to-liver ratio (TLR), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of the primary tumor were measured using SUV 2.5 cut-off threshold. Student's t-test or Mann-Whitney U test was performed for continuous variables, and χ2 test or Fisher's exact test was performed for categorical variables (p value of  < 0.05 for statistical significance). Medical records were reviewed for metastasis incidence.

RESULTS

Our study population had 66 MSI-stable and 42 MSI-high tumors. [18F]FDG uptake was higher in MSI-high tumors than MSI-stable tumors (TLR, median (Q1, Q3): 7.95 (6.06, 10.54) vs. 6.08 (4.09, 8.82), p = 0.021). Multivariable subgroup analysis demonstrated that higher [18F]FDG uptake was associated with higher risks of distant metastasis in MSI-stable tumors (SUVmax: p = 0.025, MTV: p = 0.008, TLG: p = 0.019) but not in MSI-high tumors.

CONCLUSION

MSI-high colon cancer is associated with high [18F]FDG uptake, but unlike MSI-stable tumors, the degree of [18F]FDG uptake does not correlate with the rate of distant metastasis.

CLINICAL RELEVANCE STATEMENT

MSI status should be considered during PET/CT assessment of colon cancer patients, as the degree of [18F]FDG uptake might not reflect metastatic potential in MSI-high tumors.

KEY POINTS

• High-level microsatellite instability (MSI-high) tumor is a prognostic factor for distant metastasis. • MSI-high colon cancers had a tendency of demonstrating higher [18F]FDG uptake compared to MSI-stable tumors. • Although higher [18F]FDG uptake is known to represent higher risks of distant metastasis, the degree of [18F]FDG uptake in MSI-high tumors did not correlate with the rate at which distant metastasis occurred.

Imaging prediction of KRAS mutation in patients with rectal cancer through deep metric learning using pretreatment [18F]Fluorodeoxyglucose positron emission tomography/computed tomography

OBJECTIVES

To predict KRAS mutation in rectal cancer (RC) through computer vision of [18F]fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) by using metric learning (ML).

METHODS

This study included 160 patients with RC who had undergone preoperative PET/CT. KRAS mutation was identified through polymerase chain reaction analysis. This model combined ML with the deep-learning framework to analyze PET data with or without CT images. The Batch Balance Wrapper framework and K-fold cross-validation were employed during the learning process. A receiver operating characteristic (ROC) curve analysis was performed to assess the model's predictive performance.

RESULTS

Genetic alterations in KRAS were identified in 82 (51%) tumors. Both PET and CT images were used, and the proposed model had an area under the ROC curve of 0.836 for its ability to predict a mutation status. The sensitivity, specificity, and accuracy were 75.3%, 79.3%, and 77.5%, respectively. When PET images alone were used, the area under the curve was 0.817, whereas the sensitivity, specificity, and accuracy were 73.2%, 79.6%, and 76.2%, respectively.

CONCLUSIONS

The ML model presented herein revealed that baseline 18F-FDG PET/CT images could provide supplemental information to determine KRAS mutation in RC. Additional studies are required to maximize the predictive accuracy.

ADVANCES IN KNOWLEDGE

The results of the ML model presented herein indicate that baseline 18F-FDG PET/CT images could provide supplemental information for determining KRAS mutation in RC.The predictive accuracy of the model was 77.5% when both image types were used and 76.2% when PET images alone were used. Additional studies are required to maximize the predictive accuracy.

Preoperative prediction of lymphovascular invasion of colorectal cancer by radiomics based on 18F-FDG PET-CT and clinical factors

Purpose

The purpose of this study was to investigate the value of a clinical radiomics model based on Positron emission tomography-computed tomography (PET-CT) radiomics features combined with clinical predictors of Lymphovascular invasion (LVI) in predicting preoperative LVI in patients with colorectal cancer (CRC).

Methods

A total of 95 CRC patients who underwent preoperative 18F-fluorodeoxyglucose (FDG) PET-CT examination were retrospectively enrolled. Univariate and multivariate logistic regression analyses were used to analyse clinical factors and PET metabolic data in the LVI-positive and LVI-negative groups to identify independent predictors of LVI. We constructed four prediction models based on radiomics features and clinical data to predict LVI status. The predictive efficacy of different models was evaluated according to the receiver operating characteristic curve. Then, the nomogram of the best model was constructed, and its performance was evaluated using calibration and clinical decision curves.

Results

Mean standardized uptake value (SUVmean), maximum tumour diameter and lymph node metastasis were independent predictors of LVI in CRC patients (P < 0.05). The clinical radiomics model obtained the best prediction performance, with an Area Under Curve (AUC) of 0.922 (95%CI 0.820-0.977) and 0.918 (95%CI 0.782-0.982) in the training and validation cohorts, respectively. A nomogram based on the clinical radiomics model was constructed, and the calibration curve fitted well (P > 0.05).

Conclusion

The clinical radiomics prediction model constructed in this study has high value in the preoperative individualized prediction of LVI in CRC patients.

Ability of 18F-FDG Positron Emission Tomography Radiomics and Machine Learning in Predicting KRAS Mutation Status in Therapy-Naive Lung Adenocarcinoma

OBJECTIVE

Considering the essential role of KRAS mutation in NSCLC and the limited experience of PET radiomic features in KRAS mutation, a prediction model was built in our current analysis. Our model aims to evaluate the status of KRAS mutants in lung adenocarcinoma by combining PET radiomics and machine learning.

METHOD

Patients were retrospectively selected from our database and screened from the NSCLC radiogenomic dataset from TCIA. The dataset was randomly divided into three subgroups. Two open-source software programs, 3D Slicer and Python, were used to segment lung tumours and extract radiomic features from ¹⁸F-FDG-PET images. Feature selection was performed by the Mann-Whitney U test, Spearman's rank correlation coefficient, and RFE. Logistic regression was used to build the prediction models. AUCs from ROCs were used to compare the predictive abilities of the models. Calibration plots were obtained to examine the agreements of observed and predictive values in the validation and testing groups. DCA curves were performed to check the clinical impact of the best model. Finally, a nomogram was obtained to present the selected model.

RESULTS

One hundred and nineteen patients with lung adenocarcinoma were included in our study. The whole group was divided into three datasets: a training set (n = 96), a validation set (n = 11), and a testing set (n = 12). In total, 1781 radiomic features were extracted from PET images. One hundred sixty-three predictive models were established according to each original feature group and their combinations. After model comparison and selection, one model, including wHLH_fo_IR, wHLH_glrlm_SRHGLE, wHLH_glszm_SAHGLE, and smoking habits, was validated with the highest predictive value. The model obtained AUCs of 0.731 (95% CI: 0.619~0.843), 0.750 (95% CI: 0.248~1.000), and 0.750 (95% CI: 0.448~1.000) in the training set, the validation set and the testing set, respectively. Results from calibration plots in validation and testing groups indicated that there was no departure between observed and predictive values in the two datasets (p = 0.377 and 0.861, respectively).

CONCLUSIONS

Our model combining ¹⁸F-FDG-PET radiomics and machine learning indicated a good predictive ability of KRAS status in lung adenocarcinoma. It may be a helpful non-invasive method to screen the KRAS mutation status of heterogenous lung adenocarcinoma before selected biopsy sampling.

Radiomics in colorectal cancer patients

The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.

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.

Current status and quality of radiomic studies for predicting KRAS mutations in colorectal cancer patients: A systematic review and meta‑analysis

PURPOSE

The purpose of this study was to evaluate the methodological quality of radiomics-based studies for noninvasive, preoperative prediction of Kirsten rat sarcoma (KRAS) mutations in patients with colorectal cancer; furthermore, we systematically evaluate the diagnostic accuracy of predicting models.

METHODS

We systematically searched PubMed, Embase, Cochrane Library and Web of Science databases up to 20 April 2022 for eligible studies. The methodological quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools. A meta-analysis of studies on the prediction of KRAS status in colorectal cancer patients was performed.

RESULT

Twenty-nine studies were identified in the systematic review, including three studies on the prediction of KRAS status in colorectal cancer liver metastases. All studies had an average RQS score of 9.55 (26.5% of the total score), ranging from 3 to 17. Most studies demonstrated a low or unclear risk of bias in the domains of QUADAS-2. Nineteen studies were included in the meta-analysis, mostly imaged with magnetic resonance imaging (MRI), followed by computed tomography (CT), positron emission tomography-CT (PET/CT). With pooled sensitivity, specificity and area under the curve (AUC) of the training cohorts were 0.80(95% confidence interval(CI), 0.75-0.84), 0.80(95% CI, 0.74-0.85) and 0.87(95% CI, 0.84-0.90),respectively. The pooled sensitivity, specificity, and AUC for the validation cohorts (13 studies) were 0.78(95% CI, 0.71-0.84), 0.84(95% CI, 0.74-0.90), and 0.86(95% CI, 0.83-0.89), respectively.

CONCLUSION

Radiomics is a potential noninvasive technology that has a moderate preoperative diagnosis and prediction effect on KRAS mutations. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the methodological quality of the study and further externally validate the model using multicenter datasets.

A Deep Learning Model Based on MRI and Clinical Factors Facilitates Noninvasive Evaluation of KRAS Mutation in Rectal Cancer

BACKGROUND

Recent studies showed the potential of MRI-based deep learning (DL) for assessing treatment response in rectal cancer, but the role of MRI-based DL in evaluating Kirsten rat sarcoma viral oncogene homologue (KRAS) mutation remains unclear.

PURPOSE

To develop a DL method based on T2-weighted imaging (T2WI) and clinical factors for noninvasively evaluating KRAS mutation in rectal cancer.

STUDY TYPE

Retrospective.

SUBJECTS

A total of 376 patients (108 women [28.7%]) with histopathology-confirmed rectal adenocarcinoma and KRAS mutation status.

FIELD STRENGTH/SEQUENCE

A 3 T, turbo spin echo T2WI and single-shot echo-planar diffusion-weighted imaging (b = 0, 1000 sec/mm² ).

ASSESSMENT

A clinical model was constructed with clinical factors (age, gender, carcinoembryonic antigen level, and carbohydrate antigen 199 level) and MRI features (tumor length, tumor location, tumor stage, lymph node stage, and extramural vascular invasion), and two DL models based on modified MobileNetV2 architecture were evaluated for diagnosing KRAS mutation based on T2WI alone (image model) or both T2WI and clinical factors (combined model). The clinical usefulness of these models was evaluated through calibration analysis and decision curve analysis (DCA).

STATISTICAL TESTS

Mann-Whitney U test, Chi-squared test, Fisher's exact test, logistic regression analysis, receiver operating characteristic curve (ROC), Delong's test, Hosmer-Lemeshow test, interclass correlation coefficients, and Fleiss kappa coefficients (P < 0.05 was considered statistically significant).

RESULTS

All the nine clinical-MRI characteristics were included for clinical model development. The clinical model, image model, and combined model in the testing cohort demonstrated good calibration and achieved areas under the curve (AUCs) of 0.668, 0.765, and 0.841, respectively. The combined model showed improved performance compared to the clinical model and image model in two cohorts. DCA confirmed the higher net benefit of the combined model than the other two models when the threshold probability is between 0.05 and 0.85.

DATA CONCLUSION

The proposed combined DL model incorporating T2WI and clinical factors may show good diagnostic performance. Thus, it could potentially serve as a supplementary approach for noninvasively evaluating KRAS mutation in rectal cancer.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Contrast-Enhanced CT Texture Analysis in Colon Cancer: Correlation with Genetic Markers

Background: The purpose of the study was to determine whether contrast-enhanced CT texture features relate to, and can predict, the presence of specific genetic mutations involved in CRC carcinogenesis. Materials and methods: This retrospective study analyzed the pre-operative CT in the venous phase of patients with CRC, who underwent testing for mutations in the KRAS, NRAS, BRAF, and MSI genes. Using a specific software based on CT images of each patient, for each slice including the tumor a region of interest was manually drawn along the margin, obtaining the volume of interest. A total of 56 texture parameters were extracted that were compared between the wild-type gene group and the mutated gene group. A p-value of <0.05 was considered statistically significant. Results: The study included 47 patients with stage III-IV CRC. Statistically significant differences between the MSS group and the MSI group were found in four parameters: GLRLM RLNU (area under the curve (AUC) 0.72, sensitivity (SE) 77.8%, specificity (SP) 65.8%), GLZLM SZHGE (AUC 0.79, SE 88.9%, SP 65.8%), GLZLM GLNU (AUC 0.74, SE 88.9%, SP 60.5%), and GLZLM ZLNU (AUC 0.77, SE 88.9%, SP 65.8%). Conclusions: The findings support the potential role of the CT texture analysis in detecting MSI in CRC based on pre-treatment CT scans.

Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers

The objective of this review was to summarize published radiomics studies dealing with infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers, and assess their quality. PubMed database was searched from January 1990 to February 2022 for articles performing radiomics on PET imaging of at least 1 specified tumor type. Exclusion criteria includd: non-oncological studies; supradiaphragmatic tumors; reviews, comments, cases reports; phantom or animal studies; technical articles without a clinically oriented question; studies including <30 patients in the training cohort. The review database contained PMID, first author, year of publication, cancer type, number of patients, study design, independent validation cohort and objective. This database was completed twice by the same person; discrepant results were resolved by a third reading of the articles. A total of 162 studies met inclusion criteria; 61 (37.7%) studies included >100 patients, 13 (8.0%) were prospective and 61 (37.7%) used an independent validation set. The most represented cancers were esophagus, lymphoma, and cervical cancer (n = 24, n = 24 and n = 19 articles, respectively). Most studies focused on 18F-FDG, and prognostic and response to treatment objectives. Although radiomics and artificial intelligence are technically challenging, new contributions and guidelines help improving research quality over the years and pave the way toward personalized medicine.

Correlation between 18F-FDG PET/CT intra-tumor metabolic heterogeneity parameters and KRAS mutation in colorectal cancer

PURPOSE

The study aimed to evaluate the relationship between intra-tumor metabolic heterogeneity parameters of ¹⁸F-FDG and KRAS mutation status in colorectal cancer (CRC) patients and which threshold heterogeneity parameters could better reflect the heterogeneity characteristics of colorectal cancer.

METHODS

Medical data of 101 CRC patients who underwent ¹⁸F-FDG PET/CT and KRAS mutation analysis were selected. On PET scans, ¹⁸F-FDG traditional indices maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneity parameters coefficient of variation with a threshold of 2.5 (CV_2.5), CV_40%, heterogeneity index-1 (HI-1), and HI-2 of the primary lesions were obtained. We inferred correlations between these ¹⁸F-FDG parameters and KRAS mutation status.

RESULTS

41 patients (40.6%) had KRAS gene mutation. Assessment of FDG parameters showed that SUVmax (19.00 vs. 13.16, p < 0.001), MTV (11.64 vs. 8.83, p = 0.001), and TLG (102.85 vs. 69.76, p < 0.001), CV_2.5 (0.55 vs. 0.46, p = 0.006), and HI-2 (14.03 vs. 7.59, p < 0.001) of KRAS mutation were higher compared to wild-type (WT) KRAS. CV_40% (0.22 vs. 0.24, p = 0.001) was lower in the KRAS mutation group, while HI-1 had no significant difference between the two groups. Multivariate analysis showed that MTV (OR = 4.97, 1.04-23.83, p = 0.045) was the only significant predictor in KRAS mutation, using a cut-off of 7.62 (AUC = 0.695), and MTV showed a sensitivity of 90.2% and specificity of 45.0%. However, the PET parameters were not independent predictors in KRAS mutation.

CONCLUSION

KRAS gene mutant CRC patients had more ¹⁸F-FDG uptake (SUVmax, MTV, TLG) and heterogeneity (CV_2.5, HI-2) than WT KRAS. MTV was the only independent predictor of KRAS gene mutation in colorectal cancer patients.

Clinical Significance and Prognostic Value of the Maximum Standardized Uptake Value of 18F-Flurodeoxyglucose Positron Emission Tomography-Computed Tomography in Colorectal Cancer

Background

The role of ¹⁸F-flurodeoxyglucose (¹⁸F-FDG) positron emission tomography–computed tomography (PET/CT) in colorectal cancer (CRC) remains unclear. This study aimed to explore the association of the maximum standardized uptake value (SUVmax), a parameter of ¹⁸F-FDG PET/CT, with KRAS mutation, the Ki-67 index, and survival in patients with CRC.

Methods

Data of 66 patients with CRC who underwent ¹⁸F-FDG PET/CT was retrospectively collected in our center. The clinical significance of the SUVmax in CRC and the association of the SUVmax with KRAS mutation and the Ki-67 index were determined. A meta-analysis was conducted by a systematic search of PubMed, Web of Science, and CNKI databases, and the data from published articles were combined with that of our study. The association of the SUVmax with KRAS mutation and the Ki-67 index was determined using the odds ratio to estimate the pooled results. The hazard ratio was used to quantitatively evaluate the prognosis of the SUVmax in CRC.

Results

By analyzing the data of 66 patients with CRC, the SUVmax was found not to be related to the tumor-node-metastasis stage, clinical stage, sex, and KRAS mutation but was related to the tumor location and nerve invasion. The SUVmax had no significant correlation with the tumor biomarkers and the Ki-67 index. Data of 17 studies indicated that the SUVmax was significantly increased in the mutated type compared with the wild type of KRAS in CRC; four studies showed that there was no remarkable difference between patients with a high and low Ki-67 index score regarding the SUVmax. Twelve studies revealed that the SUVmax had no significant association with overall survival and disease-free survival in CRC patients.

Conclusions

Based on the combined data, this study demonstrated that the SUVmax of ¹⁸F-FDG PET/CT was different between colon and rectal cancers and associated with KRAS mutation but not the Ki-67 index; there was no significant association between the SUVmax and survival of patients with CRC.

Overview of serum and tissue markers in colorectal cancer: a primer for radiologists

Serum and tissue tumor markers provide crucial information in the diagnosis, treatment, and follow-up of colorectal cancers. Tissue tumor markers are increasingly used for determination of targeted chemotherapy planning based on genotyping of tumor cells. Recently, plasma-based technique of liquid biopsy is being evaluated for providing tumor biomarkers in the management of colorectal cancer. Tumor markers are commonly used in conjunction with imaging during initial staging, treatment determination, response assessment, and determination of recurrence or metastatic disease. Knowledge of tumor markers and their association with radiological findings is thus crucial for radiologists. Additionally, various novel imaging techniques are being evaluated as potential noninvasive imaging biomarkers to predict tumor genotypes, features, and tumor response. We review and discuss the potential role of these newer imaging techniques.

Pretreatment 18F-FDG PET/CT Imaging Predicts the KRAS/NRAS/BRAF Gene Mutational Status in Colorectal Cancer

Objective

To investigate the association between KRAS/NRAS/BRAF mutations and metabolic parameters of pretreatment ¹⁸F-FDG PET/CT in colorectal cancer (CRC).

Methods

A total of 85 patients with CRC were included in the study. PET/CT was performed in all the patients before surgery. The histopathological examination and analysis of the gene mutational status of the primary tumor were conducted. The associations among clinical features, PET metabolic parameters, and the gene mutational status were investigated. Moreover, receiver operating characteristic (ROC) curves for maximum standard uptake value (SUVmax) of the primary tumor were generated along with analysis of the target tissue to nontarget tissue ratio (T/NT) for predicting the efficacy of KRAS/NRAS/BRAF mutations in CRC. Finally, the corresponding area under the curve, the optimal cutoff value, and the corresponding sensitivity and specificity were obtained.

Results

The mutation rate of KRAS/NRAS/BRAF was 54.12% (46/85). In addition, both SUVmax and T/NT were significantly higher in the KRAS/NRAS/BRAF-mutation groups compared to the wild-type group (15.88 ± 6.71 vs. 12.59 ± 5.79, 8.04 ± 3.03 vs. 6.38 ± 2.80; P=0.012 and 0.004, respectively). Results from the ROC curve also showed that the cutoff values for T/NT and SUVmax were 5.14 and 12.40, respectively, while the predictive accuracy was 0.682 and 0.647, respectively. On the other hand, the sensitivity was 91.30% and 65.22% while the specificity was 43.59% and 64.10%, respectively. Moreover, univariate analysis showed that the KRAS/NRAS/BRAF mutation was not significantly associated with gender, age, lesion location, tumor length, pathological type, tissue differentiation, and UICC staging (all P > 0.05).

Conclusion

T/NT ratio and SUVmax could be the potential surrogate imaging indicators to predict the KRAS/NRAS/BRAF mutational status in CRC patients.

MRI Radiomics Signature as a Potential Biomarker for Predicting KRAS Status in Locally Advanced Rectal Cancer Patients

Background and Purpose

Locally advanced rectal cancer (LARC) is a heterogeneous disease with little information about KRAS status and image features. The purpose of this study was to analyze the association between T2 magnetic resonance imaging (MRI) radiomics features and KRAS status in LARC patients.

Material and Methods

Eighty-three patients with KRAS status information and T2 MRI images between 2012.05 and 2019.09 were included. Least absolute shrinkage and selection operator (LASSO) regression was performed to assess the associations between features and gene status. The patients were divided 7:3 into training and validation sets. The C-index and the average area under the receiver operator characteristic curve (AUC) were used for performance evaluation.

Results

The clinical characteristics of 83 patients in the KRAS mutant and wild-type cohorts were balanced. Forty-two (50.6%) patients had KRAS mutations, and 41 (49.4%) patients had wild-type KRAS. A total of 253 radiomics features were extracted from the T2-MRI images of LARC patients. One radiomic feature named X.LL_scaled_std, a standard deviation value of scaled wavelet-transformed low-pass channel filter, was selected from 253 features (P=0.019). The radiomics-based C-index values were 0.801 (95% CI: 0.772-0.830) and 0.703 (95% CI: 0.620-0.786) in the training and validation sets, respectively.

Conclusion

Radiomics features could differentiate KRAS status in LARC patients based on T2-MRI images. Further validation in a larger dataset is necessary in the future.

Preoperative prediction of regional lymph node metastasis of colorectal cancer based on 18F-FDG PET/CT and machine learning

PURPOSE

To establish and validate a regional lymph node (LN) metastasis prediction model of colorectal cancer (CRC) based on 18F-FDG PET/CT and radiomic features using machine-learning methods.

METHODS

A total of 199 colorectal cancer patients underwent pre-therapy diagnostic 18F-FDG PET/CT scans and CRC radical surgery. The Chang-Gung Image Texture Analysis toolbox (CGITA) was used to extract 70 PET radiomic features reflecting 18F-FDG uptake heterogeneity of tumors. The least absolute shrinkage and selection operator (LASSO) algorithm was used to select radiomic features and develop a radiomic signature score (Rad-score). The training set was used to establish five machine-learning prediction models and the test set was used to test the efficacy of the models. The effectiveness of the models was compared by ROC analysis.

RESULTS

The CRC patients were divided into a training set (n = 144) and a test set (n = 55). Two radiomic features were selected to build the Rad-score. Five machine-learning algorithms including logistic regression, support vector machine (SVM), random forest, neural network and eXtreme gradient boosting (XGBoost) were used to established models. Among the five machine-learning models, logistic regression (AUC 0.866, 95% CI 0.808-0.925) and XGBoost (AUC 0.903, 95% CI 0.855-0.951) models performed the best. In the training set, the AUC of these two models were significantly higher than that of the LN metastasis status reported by 18F-FDG PET/CT for differentiating positive and negative regional LN metastases in CRC (all p < 0.05). Good efficacy of the above two models was also achieved in the test set. We created a nomogram based on the logistic regression model that visualized the results and provided an easy-to-use method for predicting regional LN metastasis in patients with CRC.

CONCLUSION

In this study, five machine-learning models for preoperative prediction of regional LN metastasis of CRC based on 18F-FDG PET/CT and PET-based radiomic features were successfully developed and validated. Among them, the logistic regression and XGBoost models performed the best, with higher efficacy than 18F-FDG PET/CT in both the training and test sets.

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

Feasibility of MRI Radiomics for Predicting KRAS Mutation in Rectal Cancer

The mutation status of KRAS is a significant biomarker in the prognosis of rectal cancer. This study investigated the feasibility of MRI-based radiomics in predicting the mutation status of KRAS with a composite index which could be an important criterion for KRAS mutation in clinical practice. In this retrospective study, a total of 127 patients with rectal cancer were enrolled. The 3D Slicer was used to extract the radiomics features from the MRI images, and sparse support vector machine (SVM) with linear kernel was applied for feature reduction. The radiomics classifier for predicting the KRAS status was then constructed by Linear Discriminant Analysis (LDA) and its performance was evaluated. The composite index was determined with LDA model. Out of 127 rectal cancer subjects, there were 44 KRAS mutation cases and 83 wild cases. A total of 104 radiomics features were extracted, 54 features were filtered by linear SVM with L1-norm regularization and 6 features that had no significant correlations within them were finally selected. The radiomics classifier constructed using the 6 features featured an AUC value of 0.669 (specificity: 0.506; sensitivity: 0.773) with LDA. Furthermore, the composite index (Radscore) had statistically significant difference between the KRAS mutation and wild groups. It is suggested that the MRI-based radiomics has the potential in predicting the KRAS status in patients with rectal cancer, which may enhance the diagnostic value of MRI in rectal cancer.

Prediction of KRAS, NRAS and BRAF status in colorectal cancer patients with liver metastasis using a deep artificial neural network based on radiomics and semantic features

There is a critical need for development of improved methods capable of accurately predicting the RAS (KRAS and NRAS) and BRAF gene mutation status in patients with advanced colorectal cancer (CRC). The purpose of this study was to investigate whether radiomics and/or semantic features could improve the detection accuracy of RAS/BRAF gene mutation status in patients with colorectal liver metastasis (CRLM). In this retrospective study, 159 patients who had been diagnosed with CRLM in two hospitals were enrolled. All patients received lung and abdominal contrast-enhanced CT (CECT) scans prior to radiation therapy and chemotherapy. Semantic features were independently assessed by two radiologists. Radiomics features were extracted from the portal venous phase (PVP) of the CT scan for each patient. Seven machine learning algorithms were used to establish three scores based on the semantic, radiomics and the combination of both features. Two semantic and 851 radiomics features were used to predict the mutation status of RAS and BRAF using an artificial neural network method (ANN). This approach performed best out of the seven tested algorithms. We constructed three scores which were based on radiomics, semantic features and the combined scores. The combined score could distinguish between wild-type and mutant patients with an AUC of 0.95 in the primary cohort and 0.79 in the validation cohort. This study proved that the application of radiomics together with semantic features can improve non-invasive assessment of the gene mutation status of RAS (KRAS and NRAS) and BRAF in CRLM.

KRAS mutation effects on the 2-[18F]FDG PET uptake of colorectal adenocarcinoma metastases in the liver

Background

Deriving individual tumor genomic characteristics from patient imaging analysis is desirable. We explore the predictive value of 2-[18F]FDG uptake with regard to the KRAS mutational status of colorectal adenocarcinoma liver metastases (CLM).

Methods

2-[18F]FDG PET/CT images, surgical pathology and molecular diagnostic reports of 37 patients who underwent PET/CT-guided biopsy of CLM were reviewed under an IRB-approved retrospective research protocol. Sixty CLM in 39 interventional PET scans of the 37 patients were segmented using two different auto-segmentation tools implemented in different commercially available software packages. PET standard uptake values (SUV) were corrected for: (1) partial volume effect (PVE) using cold wall-corrected contrast recovery coefficients derived from phantom spheres with variable diameter and (2) variability of arterial tracer supply and variability of uptake time after injection until start of PET scan derived from the tumor-to-blood standard uptake ratio (SUR) approach. The correlations between the KRAS mutational status and the mean, peak and maximum SUV were investigated using Student’s t test, Wilcoxon rank sum test with continuity correction, logistic regression and receiver operation characteristic (ROC) analysis. These correlation analyses were also performed for the ratios of the mean, peak and maximum tumor uptake to the mean blood activity concentration at the time of scan: SUR_MEAN, SUR_PEAK and SUR_MAX, respectively.

Results

Fifteen patients harbored KRAS missense mutations (KRAS+), while another 3 harbored KRAS gene amplification. For 31 lesions, the mutational status was derived from the PET/CT-guided biopsy. The Student’s t test p values for separating KRAS mutant cases decreased after applying PVE correction to all uptake metrics of each lesion and when applying correction for uptake time variability to the SUR metrics. The observed correlations were strongest when both corrections were applied to SUR_MAX and when the patients harboring gene amplification were grouped with the wild type: p ≤ 0.001; ROC area under the curve = 0.77 and 0.75 for the two different segmentations, respectively, with a mean specificity of 0.69 and sensitivity of 0.85.

Conclusion

The correlations observed after applying the described corrections show potential for assigning probabilities for the KRAS missense mutation status in CLM using 2-[18F]FDG PET images.

Deep Learning Features Improve the Performance of a Radiomics Signature for Predicting KRAS Status in Patients with Colorectal Cancer

RATIONALE AND OBJECTIVES

We assess the performance of a model combining a deep convolutional neural network and a hand-crafted radiomics signature for predicting KRAS status in patients with colorectal cancer (CRC).

MATERIALS AND METHODS

The primary cohort consisted of 279 patients with clinicopathologically confirmed CRC between April 2011 and April 2015. Portal venous phase computed tomographic images were analyzed to extract traditional hand-crafted radiomics features as well as deep learning features. A Wilcoxon rank sum test, the minimum redundancy maximum relevance algorithm, and multivariable logistic regression analysis were used to select features and build a radiomics signature. A combined model was then developed using multivariable logistic regression analysis. An independent validation cohort of 119 patients from May 2015 to April 2016 was used to confirm the combined model's predictive performance.

RESULTS

The C-index of hand-crafted radiomics signature's discriminative ability was 0.719 (95% confidence interval, CI: 0.658-0.776) for the primary cohort and 0.720 (95% CI: 0.625-0.813) for the validation cohort. The C-index of the deep radiomics signature's discriminative ability was 0.754 (95% CI: 0.696-0.813) for the primary cohort and 0.786 (95% CI: 0.702-0.863) for the validation cohort. The combined model, which merged the hand-crafted radiomics features and deep radiomics features, achieve a C-index of 0.815 (95% CI: 0.766-0.868) for the primary cohort and 0.832 (95% CI: 0.762-0.905) for the validation cohort.

CONCLUSION

This study presents a model that incorporates the hand-crafted and deep radiomics signature, which can be used for individualized preoperative prediction of KRAS mutations in patients with CRC.

Diameter of Superior Rectal Vein - CT Predictor of KRAS Mutation in Rectal Carcinoma

Background

The purpose of this study was to investigate the feasibility of CT parameters to predict the presence of KRAS mutations in rectal cancer patients. The relationship between the presence of a KRAS mutation and pathological findings was evaluated simultaneously.

Methods

Eighty-nine patients (29 females, 60 males, age 27–90, mean 59.7±12 years) with pathologically proven rectal cancer were enrolled. A KRAS mutation test was completed following surgery. Parameters evaluated on CT included the tumor location, the diameter of the superior rectal vein (SRV) and inferior mesenteric vein (IMV), the presence of calcification, ulceration, lymph node enlargement (LNE), distant metastasis, tumor shape (intraluminal polypoid mass, infiltrative mass, or bulky), circumferential extent (C0–C1/4, C1/4–C1/2, C1/2–C3/4, or C3/4–C1), enhanced pattern (homogeneous or heterogeneous), CT ratio, and the length of the tumor (LOT). Pathological findings included lymphovascular emboli, signet ring cell, peripheral fat interval infiltration, focal ulcer, lymph node metastasis, tumor pathological type, and differentiation extent. The correlations between KRAS status and CT parameters, and KRAS status and pathological findings were investigated. The accuracy of CT characteristics for predicting KRAS mutation was evaluated.

Results

A KRAS mutation was detected in 42 cases. On CT image, the diameter of the SRV was significantly increased in the KRAS mutation group compared to in the KRAS wild-type group (4.6±0.9 mm vs 4.2±0.9 mm, p=0.02), and LNE was more likely to occur in the KRAS mutation group (73.3% vs 26.7%, p=0.03). There was no significant difference between the KRAS mutation group and the KRAS wild-type group on the other CT parameters (location, IMV, calcification, ulcer, distant metastasis, tumor shape, enhanced pattern, circumferential extent, CT ratio, and LOT). In the pathological findings, a KRAS mutation was more likely to occur in the middle differentiation group (p=0.03). No significant difference was found between the KRAS mutation group and the KRAS wild-type group in the presence of lymphovascular emboli, signet ring cell, peripheral fat interval infiltration, focal ulcer, lymph node metastasis, and tumor pathological type. With the best cut-off value of 4.07 mm, the AUC of the SRV to predict a KRAS mutation was 0.63 with a sensitivity of 76.2% and a specificity of 48.9%.

Conclusion

It was feasible to use the diameter of the SRV to predict a KRAS mutation in rectal cancer patients, and LNE also can be regarded as an important clue on preoperative CT images.

Metabolic Parameters as Predictors for Progression Free and Overall Survival of Patients with Metastatic Colorectal Cancer

We tested the prognostic relevance of metabolic parameters and their relative changes in patients with metastatic colorectal cancer (mCRC) treated with monoclonal antibody and chemotherapy. SUVmax (standardized uptake volume), SAM (standardized added metabolic activity) and TLG (total lesion glycolysis) are assessed with 18F-fluorodeoxyglucosepositron emission tomography and computed tomography (FDG-PET/CT) to evaluate total metabolic activity of malignant processes. Our purpose was to investigate the change of glucose metabolism in relation to PFS (progression free survival) and OS (overall survival). Fifty-three patients with mCRC with at least one measurable liver metastasis were included in this prospective, multi-center, early exploratory study. All patients were treated with first-line chemotherapy and targeted therapy. Metabolic parameters, like SUVmax, SAM, normalized SAM (NSAM) and TLG were assessed by FDG-PET/CT, carried out at baseline (scan-1) and after two therapeutic cycle (scan-2). Our results suggested neither SUVmax nor TLG have such prognostic value as NSAM in liver metastases of colorectal cancer. The parameters after the two cycles of chemotherapy proved to be better predictors of the clinical outcome. NSAM after two cycles of treatment has a statistically significant predictive value on OS, while SAM was predictive to the PFS. The follow up normalized SAM after 2 cycles of first line oncotherapy was demonstrated to be useful as prognostic biomarkers for OS in metastatic colorectal cancer. We should introduce this measurement in metastatic colorectal cancer if there is at least one metastasis in the liver.

Clinical application and research advancement of positron emission tomography/computed tomography in colorectal cancer

Colorectal cancer is one of the most common malignant tumors of the digestive system. Early diagnosis and accurate staging and restaging of tumors are the preconditions for standardized treatment of colorectal cancer, which is conducive to the selection of treatment options and the evaluation of prognosis, as well as the improvement of patients' quality of life. With the popularization of fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT), its value in the diagnosis, staging and restaging, treatment decision-making, and efficacy and prognosis assessment of colorectal cancer is becoming increasingly important. This review briefly introduces the application and advancement of PET/CT in the diagnosis and treatment of colorectal cancer, in the hope that clinicians can have a more comprehensive understanding of the significance of PET/CT in the diagnosis and treatment of colorectal cancer.

Diffusion-Weighted MRI and Diffusion Kurtosis Imaging to Detect RAS Mutation in Colorectal Liver Metastasis

Simple Summary

Imaging derived parameters can provide data on tumor phenotype as well as cancer microenvironment. Radiomics has recently shown potential in realizing personalized medicine. The aim of the manuscript is to detect RAS mutation in colorectal liver metastasis by Diffusion-Weighted Magnetic Resonance Imaging (DWI-MRI) - and Diffusion Kurtosis imaging (DKI)-derived parameters. We demonstrated that DKI derived parameters allows to detect RAS mutation in liver metastasis.

Abstract

Objectives: To detect RAS mutation in colorectal liver metastasis by Diffusion-Weighted Magnetic Resonance Imaging (DWI-MRI) - and Diffusion Kurtosis imaging (DKI)-derived parameters. Methods: In total, 106 liver metastasis (60 metastases with RAS mutation) in 52 patients were included in this retrospective study. Diffusion and perfusion parameters were derived by DWI (apparent diffusion coefficient (ADC), basal signal (S0), pseudo-diffusion coefficient (DP), perfusion fraction (FP) and tissue diffusivity (DT)) and DKI data (mean of diffusion coefficient (MD) and mean of diffusional Kurtosis (MK)). Wilcoxon–Mann–Whitney U tests for non-parametric variables and receiver operating characteristic (ROC) analyses were calculated with area under ROC curve (AUC). Moreover, pattern recognition approaches (linear classifier, support vector machine, k-nearest neighbours, decision tree), with features selection methods and a leave-one-out cross validation approach, were considered. Results: A significant discrimination between the group with RAS mutation and the group without RAS mutation was obtained by the standard deviation value of MK (MK STD), by the mean value of MD, and by that of FP. The best results were reached by MK STD with an AUC of 0.80 (sensitivity of 72%, specificity of 85%, accuracy of 79%) using a cut-off of 203.90 × 10⁻³, and by the mean value of MD with AUC of 0.80 (sensitivity of 84%, specificity of 73%, accuracy of 77%) using a cut-off of 1694.30 mm²/s × 10⁻⁶. Considering all extracted features or the predictors obtained by the features selection method (the mean value of S0, the standard deviation value of MK, FP and of DT), the tested pattern recognition approaches did not determine an increase in diagnostic accuracy to detect RAS mutation (AUC of 0.73 and 0.69, respectively). Conclusions: Diffusion-Weighted imaging and Diffusion Kurtosis imaging could be used to detect the RAS mutation in liver metastasis. The standard deviation value of MK and the mean value of MD were the more accurate parameters in the RAS mutation detection, with an AUC of 0.80.

Characterization of FDG PET Images Using Texture Analysis in Tumors of the Gastro-Intestinal Tract: A Review

Radiomics or textural feature extraction obtained from positron emission tomography (PET) images through complex mathematical models of the spatial relationship between multiple image voxels is currently emerging as a new tool for assessing intra-tumoral heterogeneity in medical imaging. In this paper, available literature on texture analysis using FDG PET imaging in patients suffering from tumors of the gastro-intestinal tract is reviewed. While texture analysis of FDG PET images appears clinically promising, due to the lack of technical specifications, a large variability in the implemented methodology used for texture analysis and lack of statistical robustness, at present, no firm conclusions can be drawn regarding the predictive or prognostic value of FDG PET texture analysis derived indices in patients suffering from gastro-enterologic tumors. In order to move forward in this field, a harmonized image acquisition and processing protocol as well as a harmonized protocol for texture analysis of tumor volumes, allowing multi-center studies excluding statistical biases should be considered. Furthermore, the complementary and additional value of CT-imaging, as part of the PET/CT imaging technique, warrants exploration.

Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on CT imaging

Background

The detection of Kirsten rat sarcoma viral oncogene homolog (KRAS) gene mutations in colorectal cancer (CRC) is key to the optimal design of individualized therapeutic strategies. The noninvasive prediction of the KRAS status in CRC is challenging. Deep learning (DL) in medical imaging has shown its high performance in diagnosis, classification, and prediction in recent years. In this paper, we investigated predictive performance by using a DL method with a residual neural network (ResNet) to estimate the KRAS mutation status in CRC patients based on pre-treatment contrast-enhanced CT imaging.

Methods

We have collected a dataset consisting of 157 patients with pathology-confirmed CRC who were divided into a training cohort (n = 117) and a testing cohort (n = 40). We developed an ResNet model that used portal venous phase CT images to estimate KRAS mutations in the axial, coronal, and sagittal directions of the training cohort and evaluated the model in the testing cohort. Several groups of expended region of interest (ROI) patches were generated for the ResNet model, to explore whether tissues around the tumor can contribute to cancer assessment. We also explored a radiomics model with the random forest classifier (RFC) to predict KRAS mutations and compared it with the DL model.

Results

The ResNet model in the axial direction achieved the higher area under the curve (AUC) value (0.90) in the testing cohort and peaked at 0.93 with an input of ’ROI and 20-pixel’ surrounding area. AUC of radiomics model in testing cohorts were 0.818. In comparison, the ResNet model showed better predictive ability.

Conclusions

Our experiments reveal that the computerized assessment of the pre-treatment CT images of CRC patients using a DL model has the potential to precisely predict KRAS mutations. This new model has the potential to assist in noninvasive KRAS mutation estimation.

Radiogenomics Based on PET Imaging

Radiogenomics or imaging genomics is a novel omics strategy of associating imaging data with genetic information, which has the potential to advance personalized medicine. Imaging features extracted from PET or PET/CT enable assessment of in vivo functional and physiological activity and provide comprehensive tumor information non-invasively. However, PET features are considered secondary to features on conventional imaging, and there has not yet been a review of the radiogenomic approach using PET features. This review article summarizes the current state of PET-based radiogenomic research for cancer, which discusses some of its limitations and directions for future study.

A study of radiomics parameters from dual-energy computed tomography images for lymph node metastasis evaluation in colorectal mucinous adenocarcinoma

Lymph nodes (LN) metastasis differentiation from computed tomography (CT) images is a challenging problem. This study aims to investigate the association between radiomics image parameters and LN metastasis in colorectal mucinous adenocarcinoma (MAC).Clinical records and CT images of 15 patients were included in this study. Among them, 1 patient was confirmed with all metastatic LNs, the other 14 were confirmed with all non-metastatic LNs. The regions of the LNs were manually labeled on each slice by experienced radiologists. A total of 1054 LN regions were obtained. Among them, 164 were from metastatic LNs. One hundred nine image parameters were computed and analyzed using 2-sample t test method and logistic regression classifier.Based on 2 sample t test, image parameters between the metastatic group and the non-metastatic group were compared. A total of 73 parameters were found to be significant (P < .01). The selected shape parameters demonstrate that non-metastatic LNs tend to have smaller sizes and more circle-like shapes than metastatic LNs, which validates the common agreement of LN diagnosis using computational method. Besides, several high order parameters were selected as well, which indicates that the textures vary between non-metastatic LNs and metastatic LNs. The selected parameters of significance were further used to train logistic regression classifier with L1 penalty. Based on receiver operating characteristic (ROC) analysis, large area under curve (AUC) values were achieved over 5-fold cross validation (0.88 ± 0.06). Moreover, high accuracy, specificity, and sensitivity values were observed as well.The results of the study demonstrate that some quantitative image parameters are of significance in differentiating LN metastasis. Logistic regression classifiers showed that the parameters are with predictive values in LN metastasis, which may be used to assist preoperative diagnosis.

Methylglyoxal Scavengers Resensitize KRAS-Mutated Colorectal Tumors to Cetuximab

The use of cetuximab anti-epidermal growth factor receptor (anti-EGFR) antibodies has opened the era of targeted and personalized therapy in colorectal cancer (CRC). Poor response rates have been unequivocally shown in mutant KRAS and are even observed in a majority of wild-type KRAS tumors. Therefore, patient selection based on mutational profiling remains problematic. We previously identified methylglyoxal (MGO), a by-product of glycolysis, as a metabolite promoting tumor growth and metastasis. Mutant KRAS cells under MGO stress show AKT-dependent survival when compared with wild-type KRAS isogenic CRC cells. MGO induces AKT activation through phosphatidylinositol 3-kinase (PI3K)/mammalian target of rapamycin 2 (mTORC2) and Hsp27 regulation. Importantly, the sole induction of MGO stress in sensitive wild-type KRAS cells renders them resistant to cetuximab. MGO scavengers inhibit AKT and resensitize KRAS-mutated CRC cells to cetuximab in vivo. This study establishes a link between MGO and AKT activation and pinpoints this oncometabolite as a potential target to tackle EGFR-targeted therapy resistance in CRC.

What can artificial intelligence teach us about the molecular mechanisms underlying disease?

While molecular imaging with positron emission tomography or single-photon emission computed tomography already reports on tumour molecular mechanisms on a macroscopic scale, there is increasing evidence that there are multiple additional features within medical images that can further improve tumour characterization, treatment prediction and prognostication. Early reports have already revealed the power of radiomics to personalize and improve patient management and outcomes. What remains unclear is how these additional metrics relate to underlying molecular mechanisms of disease. Furthermore, the ability to deal with increasingly large amounts of data from medical images and beyond in a rapid, reproducible and transparent manner is essential for future clinical practice. Here, artificial intelligence (AI) may have an impact. AI encompasses a broad range of 'intelligent' functions performed by computers, including language processing, knowledge representation, problem solving and planning. While rule-based algorithms, e.g. computer-aided diagnosis, have been in use for medical imaging since the 1990s, the resurgent interest in AI is related to improvements in computing power and advances in machine learning (ML). In this review we consider why molecular and cellular processes are of interest and which processes have already been exposed to AI and ML methods as reported in the literature. Non-small-cell lung cancer is used as an exemplar and the focus of this review as the most common tumour type in which AI and ML approaches have been tested and to illustrate some of the concepts.

Novel imaging techniques of rectal cancer: what do radiomics and radiogenomics have to offer? A literature review

INTRODUCTION

As computational capabilities have advanced, radiologists and their collaborators have looked for novel ways to analyze diagnostic images. This has resulted in the development of radiomics and radiogenomics as new fields in medical imaging. Radiomics and radiogenomics may change the practice of medicine, particularly for patients with colorectal cancer. Radiomics corresponds to the extraction and analysis of numerous quantitative imaging features from conventional imaging modalities in correlation with several endpoints, including the prediction of pathology, genomics, therapeutic response, and clinical outcome. In radiogenomics, qualitative and/or quantitative imaging features are extracted and correlated with genetic profiles of the imaged tissue. Thus far, several studies have evaluated the use of radiomics and radiogenomics in patients with colorectal cancer; however, there are challenges to be overcome before its routine implementation including challenges related to sample size, model design and interpretability, and the lack of robust multicenter validation set.

MATERIAL AND METHODS

In this article, we will review the concepts of radiomics and radiogenomics and their potential applications in rectal cancer.

CONCLUSION

Radiologists should be aware of the basic concepts, benefits, pitfalls, and limitations of new radiomic and radiogenomics techniques to achieve a balanced interpretation of the results.

Metabolic Biomarker-Based BRAFV600 Mutation Association and Prediction in Melanoma

The aim of this study was to associate and predict B-rapidly accelerated fibrosarcoma valine 600 (BRAFV600) mutation status with both conventional and radiomics ¹⁸F-FDG PET/CT features, while exploring several methods of feature selection in melanoma radiomics. Methods: Seventy unresectable stage III-IV melanoma patients who underwent a baseline ¹⁸F-FDG PET/CT scan were identified. Patients were assigned to the BRAFV600 group or BRAF wild-type group according to mutational status. ¹⁸F-FDG uptake quantification was performed by semiautomatic lesion delineation. Four hundred eighty radiomics features and 4 conventional PET features (SUV_max, SUV_mean, SUV_peak, and total lesion glycolysis) were extracted per lesion. Six different methods of feature selection were implemented, and 10-fold cross-validated predictive models were built for each. Model performances were evaluated with areas under the curve (AUCs) for the receiver operating characteristic curves. Results: Thirty-five BRAFV600 mutated patients (100 lesions) and 35 BRAF wild-type patients (79 lesions) were analyzed. AUCs predicting the BRAFV600 mutation varied from 0.54 to 0.62 and were susceptible to feature selection method. The best AUCs were achieved by feature selection based on literature, a penalized binary logistic regression model, and random forest model. No significant difference was found between the BRAFV600 and BRAF wild-type group in conventional PET features or predictive value. Conclusion: BRAFV600 mutation status is not associated with, nor can it be predicted with, conventional PET features, whereas radiomics features were of low predictive value (AUC = 0.62). We showed feature selection methods to influence predictive model performance, describing and evaluating 6 unique methods. Detecting BRAFV600 status in melanoma based on ¹⁸F-FDG PET/CT alone does not yet provide clinically relevant knowledge.

Metabolic Imaging Phenotype Using Radiomics of [18F]FDG PET/CT Associated with Genetic Alterations of Colorectal Cancer

PURPOSE

To understand the association between genetic mutations and radiomics of 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) positron emission tomography (PET)/x-ray computed tomography (CT) in patients with colorectal cancer (CRC).

PROCEDURES

This study included 74 CRC patients who had undergone preoperative [¹⁸F]FDG PET/CT. A total of 65 PET/CT-related features including intensity, volume-based, histogram, and textural features were calculated. High-resolution melting methods were used for genetic mutation analysis.

RESULTS

Genetic mutants were found in 21 KRAS tumors (28 %), 31 TP53 tumors (42 %), and 17 APC tumors (23 %). Tumors with a mutated KRAS had an increased value at the 25th percentile of maximal standardized uptake value (SUV_max) within their metabolic tumor volume (MTV) (P < .0001; odds ratio [OR] 1.99; 95 % confidence interval [CI] 1.37-2.90) and their contrast from the gray-level cooccurrence matrix (P = .005; OR 1.52; 95 % CI 1.14-2.04). A mutated TP53 was associated with an increased value of short-run low gray-level emphasis derived from the gray-level run length matrix (P = .001; OR 243006.0; 95 % CI 59.2-996,872,313). APC mutants exhibited lower low gray-level zone emphasis derived from the gray-level zone length matrix (P = .006; OR < .0001; 95 % CI 0.000-0.22).

CONCLUSION

PET/CT-derived radiomics can provide supplemental information to determine KRAS, TP53, and APC genetic alterations in CRC.

CT texture analysis for the prediction of KRAS mutation status in colorectal cancer via a machine learning approach

PURPOSE

This study aimed to investigate whether a machine learning-based computed tomography (CT) texture analysis could predict the mutation status of V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) in colorectal cancer.

METHOD

This retrospective study comprised 40 patients with pathologically confirmed colorectal cancer who underwent KRAS mutation testing, contrast-enhancement CT, and ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) before treatment. Of the 40 patients, 20 had mutated KRAS genes, whereas 20 had wild-type KRAS genes. Fourteen CT texture parameters were extracted from portal venous phase CT images of primary tumors, and the maximum standard uptake values (SUV_max) on ¹⁸F-FDG PET images were recorded. Univariate logistic regression was used to develop predictive models for each CT texture parameter and SUV_max, and a machine learning method (multivariate support vector machine) was used to develop a comprehensive set of CT texture parameters. The area under the receiver operating characteristic (ROC) curve (AUC) of each model was calculated using five-fold cross validation. In addition, the performance of the machine learning method with the CT texture parameters was compared with that of SUV_max.

RESULTS

In the univariate analyses, the AUC of each CT texture parameter ranged from 0.4 to 0.7, while the AUC of the SUV_max was 0.58. Comparatively, the multivariate support vector machine with comprehensive CT texture parameters yielded an AUC of 0.82, indicating a superior prediction performance when compared to the SUV_max.

CONCLUSIONS

A machine learning-based CT texture analysis was superior to the SUV_max for predicting the KRAS mutation status of a colorectal cancer.

Relationship between KRAS mutations and dual time point 18F-FDG PET/CT imaging in colorectal liver metastases

PURPOSE

To investigate the association between metabolic parameters of dual time point ¹⁸F-FDG PET/CT imaging and Kirsten rat sarcoma (KRAS) mutation status in colorectal liver metastases (CRLM).

METHODS

Forty-nine colorectal cancer patients with 87 liver metastatic lesions were included in this retrospective study. KRAS gene mutation tests were also performed for all the patients. The maximum standardized uptake value (SUV_max) was measured for each hepatic metastatic lesion on both early and delayed scans, and the change of SUV_max (ΔSUV_max) and retention index (RI) were calculated. Uni-variate and multi-variate analyses were employed to determine the relationship between any PET/CT parameters and KRAS mutation status.

RESULTS

Thirty-seven (42.5%) liver metastatic lesions harboring KRAS mutations were identified. The SUV_max of CRLM with KRAS mutation both on early and delayed scans was significantly higher than those with wild-type KRAS (10.7 ± 6.0 vs. 7.8 ± 3.3, P = 0.002; 15.5 ± 10.1 vs. 10.0 ± 4.2, P < 0.001, respectively). Compared with wild-type KRAS CRLM, ΔSUV_max and RI (%) of CRLM with KRAS mutation were also significantly higher than those with wild-type KRAS (4.8 ± 4.7 vs. 2.2 ± 2.0, P < 0.001; 45.3 ± 28.2 vs. 29.6 ± 24.7, P = 0.003, respectively). Multi-variate analyses showed that the SUV_max on both early and delayed scans, ΔSUV_max, and RI (%) were the 4 independent factors to predict CRLM patients harboring KRAS mutations.

CONCLUSION

The SUV_max on both early and delayed scans, ΔSUV_max, and RI (%) may be the 4 independent factors to predict CRLM patients harboring KRAS mutations.

Radiogenomics: bridging imaging and genomics

From diagnostics to prognosis to response prediction, new applications for radiomics are rapidly being developed. One of the fastest evolving branches involves linking imaging phenotypes to the tumor genetic profile, a field commonly referred to as "radiogenomics." In this review, a general outline of radiogenomic literature concerning prominent mutations across different tumor sites will be provided. The field of radiogenomics originates from image processing techniques developed decades ago; however, many technical and clinical challenges still need to be addressed. Nevertheless, increasingly accurate and robust radiogenomic models are being presented and the future appears to be bright.

Diagnostic performance of F-18 FDG PET/CT for prediction of KRAS mutation in colorectal cancer patients: a systematic review and meta-analysis

OBJECTIVE

The purpose of the current study was to investigate the diagnostic performance of F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for the prediction of v-Ki-ras-2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation in colorectal cancer (CRC) patients through a systematic review and meta-analysis.

METHODS

The PubMed and EMBASE database, from the earliest available date of indexing through April 30, 2018, were searched for studies evaluating the diagnostic performance of F-18 FDG PET/CT for prediction of KRAS mutation in CRC patients.

RESULTS

Across 9 studies (804 patients), the pooled sensitivity for F-18 FDG PET/CT was 0.66 (95% CI 0.60-0.73) without heterogeneity (I² = 34.1, p = 0.14) and a pooled specificity of 0.67 (95% CI 0.62-0.72) without heterogeneity (I² = 1.63, p = 0.42). Likelihood ratio (LR) syntheses gave an overall positive likelihood ratio (LR+) of 2.0 (95% CI 1.7-2.4) and negative likelihood ratio (LR-) of 0.5 (95% CI 0.41-0.61). The pooled diagnostic odds ratio (DOR) was 4 (95% CI 3-6). Hierarchical summary receiver operating characteristic (ROC) curve indicates that the areas under the curve were 0.69 (95% CI 0.65-0.73).

CONCLUSION

The current meta-analysis showed the low sensitivity and specificity of F-18 FDG PET/CT for prediction of KRAS mutation in CRC patients. The DOR was very low and the likelihood ratio scatter-gram indicated that F-18 FDG PET/CT might not be useful for prediction of KRAS mutation and not for its exclusion. Therefore, cautious application and interpretation should be paid to the F-18 FDG PET/CT for prediction of KRAS mutation in CRC patients.

Association between oncogenic RAS mutation and radiologic-pathologic findings in patients with primary rectal cancer

Background

To evaluate the association between various radiologic-pathologic findings and oncogenic Kirsten-ras (KRAS) mutation in patients with primary rectal cancer.

Methods

Seventy-five patients with primary rectal cancer who had undergone rectal magnetic resonance imaging (MRI) were included. The rectal MRI consisted of T2-weighted images in three planes, pre- and post-contrast-enhanced T1-weighted images, and axial diffusion-weighted images (b factors, 0, 1,000 s/mm²). Two radiologists reviewed the MRI scans and measured the axial and longitudinal tumor lengths (LTLs), apparent diffusion coefficient (ADC), and relative contrast enhancement [signal intensity (SI) difference of tumor on pre- and post-contrast T1WI/SI of tumor on pre-contrast T1WI]. The associations among the qualitative data (tumor stage, node stage, lymphatic invasion, venous invasion, and perineural invasion), quantitative data (tumor length, ADC, relative contrast enhancement) and KRAS mutations were statistically analyzed by Fisher's exact test for the qualitative data and by the Mann-Whitney U test for the quantitative data. An area under receiver operating characteristic curve (AUC) was considered as the diagnostic performance for the prediction of KRAS mutation. Molecular-biologic results served as the reference standard.

Results

The ratio of axial to LTL in the KRAS-mutant group (n=41) was higher than that in the wild-type group (n=34) (0.29±0.15; 0.22±0.08, P=0.0117). The AUC was 0.640 (95% CI, 0.520 to 0.747, P=0.0292) with an estimated maximum accuracy of 64%. The mean ADC of the mutant group was not significantly different from that of the wild-type group [(0.95±0.17)×10^-3 mm²/s; (0.96±0.17)×10^-3 mm²/s, P=0.6505]. The relative contrast enhancement showed no significant difference between the two groups (1.66±0.93, 1.35±0.84, P=0.1581). The other qualitative findings also did not show any significant difference (P>0.05).

Conclusions

The ratio of axial to LTL showed a significant difference according to KRAS mutation in patients with primary rectal cancer. However, it showed a low accuracy of 64% for prediction of KRAS mutation.

Diffusion kurtosis imaging-derived histogram metrics for prediction of KRAS mutation in rectal adenocarcinoma: Preliminary findings

BACKGROUND

Although histological examination is the standard method for assessing genetic status, the development of a noninvasive method, which can display the heterogeneity of the whole tumor to supplement genotype analysis, might be important for personalized treatment strategies.

PURPOSE

To evaluate the potential role of diffusion kurtosis imaging (DKI)-derived parameters using histogram analysis derived from whole-tumor volumes for prediction of the status of KRAS mutations in patients with rectal adenocarcinoma.

STUDY TYPE

Retrospective.

SUBJECTS

In all, 148 consecutive patients with histologically confirmed rectal adenocarcinoma who were treated at our institution.

SEQUENCE

DKI was performed with a 3.0 T MRI system using a single-shot echo-planar imaging sequence with b values of 0, 700, 1400, and 2100 sec/mm² .

ASSESSMENT

D, K, and apparent diffusion coefficient (ADC) values were measured using whole-tumor volume histogram analysis and were compared between different KRAS mutations status.

STATISTICAL TESTS

Student's t-test or Mann-Whitney U-test, and receiver operating characteristic (ROC) curves were used for statistical analysis.

RESULTS

All the percentile metrics of ADC and D values were significantly lower in the mutated group than those in the wildtype group (all P < 0.05), except for the minimum value of ADC and D (both P > 0.05), while K-related percentile metrics were higher in the mutated group compared with those in the wildtype group (all P < 0.05). Regarding the comparison of the diagnostic performance of all the histogram metrics, K_75th showed the highest AUC value of 0.871, and the corresponding values for sensitivity, specificity, positive predictive value, and negative predictive value were 81.43%, 78.21%, 77.03%, and 82.43%, respectively.

DATA CONCLUSION

DKI metrics with whole-tumor volume histogram analysis is associated with KRAS mutations, and thus may be useful for predicting the KRAS status of rectal cancers for guiding targeted therapy.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:930-939.

Radiomics in Oncological PET/CT: Clinical Applications

18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is widely used for staging, evaluating treatment response, and predicting prognosis in malignant diseases. FDG uptake and volumetric PET parameters such as metabolic tumor volume have been used and are still used as conventional PET parameters to assess biological characteristics of tumors. However, in recent years, additional features derived from PET images by computational processing have been found to reflect intratumoral heterogeneity, which is related to biological tumor features, and to provide additional predictive and prognostic information, which leads to the concept of radiomics. In this review, we focus on recent clinical studies of malignant diseases that investigated intratumoral heterogeneity on PET/CT, and we discuss its clinical role in various cancers.

Advanced Imaging Techniques in Evaluation of Colorectal Cancer

Imaging techniques are clinical decision-making tools in the evaluation of patients with colorectal cancer (CRC). The aim of this article is to discuss the potential of recent advances in imaging for diagnosis, prognosis, therapy planning, and assessment of response to treatment of CRC. Recent developments and new clinical applications of conventional imaging techniques such as virtual colonoscopy, dual-energy spectral computed tomography, elastography, advanced computing techniques (including volumetric rendering techniques and machine learning), magnetic resonance (MR) imaging-based magnetization transfer, and new liver imaging techniques, which may offer additional clinical information in patients with CRC, are summarized. In addition, the clinical value of functional and molecular imaging techniques such as diffusion-weighted MR imaging, dynamic contrast material-enhanced imaging, blood oxygen level-dependent imaging, lymphography with contrast agents, positron emission tomography with different radiotracers, and MR spectroscopy is reviewed, and the advantages and disadvantages of these modalities are evaluated. Finally, the future role of imaging-based analysis of tumor heterogeneity and multiparametric imaging, the development of radiomics and radiogenomics, and future challenges for imaging of patients with CRC are discussed. Online supplemental material is available for this article. ^©RSNA, 2018.

Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis

With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.