Correlation between PET/CT parameters and KRAS expression in colorectal cancer. Clin Nucl Med. 2014;39:685-689. [PMID: 24978328 DOI: 10.1097/rlu.0000000000000481]

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.

Construction of prediction model for KRAS mutation status of colorectal cancer based on CT radiomics

BACKGROUND

In this study, we used computed tomography (CT)-based radiomics signatures to predict the mutation status of KRAS in patients with colorectal cancer (CRC) and to identify the phase of radiomics signature with the most robust and high performance from triphasic enhanced CT.

METHODS

This study involved 447 patients who underwent KRAS mutation testing and preoperative triphasic enhanced CT. They were categorized into training (n = 313) and validation cohorts (n = 134) in a 7:3 ratio. Radiomics features were extracted using triphasic enhanced CT imaging. The Boruta algorithm was used to retain the features closely associated with KRAS mutations. The Random Forest (RF) algorithm was used to develop radiomics, clinical, and combined clinical-radiomics models for KRAS mutations. The receiver operating characteristic curve, calibration curve, and decision curve were used to evaluate the predictive performance and clinical usefulness of each model.

RESULTS

Age, CEA level, and clinical T stage were independent predictors of KRAS mutation status. After rigorous feature screening, four arterial phase (AP), three venous phase (VP), and seven delayed phase (DP) radiomics features were retained as the final signatures for predicting KRAS mutations. The DP models showed superior predictive performance compared to AP or VP models. The clinical-radiomics fusion model showed excellent performance, with an AUC, sensitivity, and specificity of 0.772, 0.792, and 0.646 in the training cohort, and 0.755, 0.724, and 0.684 in the validation cohort, respectively. The decision curve showed that the clinical-radiomics fusion model had more clinical practicality than the single clinical or radiomics model in predicting KRAS mutation status.

CONCLUSION

The clinical-radiomics fusion model, which combines the clinical and DP radiomics model, has the best predictive performance for predicting the mutation status of KRAS in CRC, and the constructed model has been effectively verified by an internal validation cohort.

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.

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.

Rerouting the drug response: Overcoming metabolic adaptation in KRAS-mutant cancers

Mutations in guanosine triphosphatase KRAS are common in lung, colorectal, and pancreatic cancers. The constitutive activity of mutant KRAS and its downstream signaling pathways induces metabolic rewiring in tumor cells that can promote resistance to existing therapeutics. In this review, we discuss the metabolic pathways that are altered in response to treatment and those that can, in turn, alter treatment efficacy, as well as the role of metabolism in the tumor microenvironment (TME) in dictating the therapeutic response in KRAS-driven cancers. We highlight metabolic targets that may provide clinical opportunities to overcome therapeutic resistance and improve survival in patients with these aggressive cancers.

Predicting Kirsten Rat Sarcoma Virus Gene Mutation Status in Patients With Colorectal Cancer by Radiomics Models Based on Multiphasic CT

Objective

To develop and validate radiomics models based on multiphasic CT in predicting Kirsten rat sarcoma virus (KRAS) gene mutation status in patients with colorectal cancer (CRC).

Materials and Methods

A total of 231 patients with pathologically confirmed CRC were retrospectively enrolled and randomly divided into training(n=184) and test groups(n=47) in a ratio of 4:1. A total of 1316 quantitative radiomics features were extracted from non-contrast phase (NCP), arterial-phase (AP) and venous-phase (VP) CT for each patient. Four steps were applied for feature selection including Spearman correlation analysis, variance threshold, least absolute contraction and selection operator, and multivariate stepwise regression analysis. Clinical and pathological characteristics were also assessed. Subsequently, three classification methods, logistic regression (LR), support vector machine (SVM) and random tree (RT) algorithm, were applied to develop seven groups of prediction models (NCP, AP, VP, AP+VP, AP+VP+NCP, AP&amp;VP, AP&amp;VP&amp;NCP) for KRAS mutation prediction. The performance of these models was evaluated by receiver operating characteristics curve (ROC) analysis.

Results

Among the three groups of single-phase models, the AP model, developed by LR algorithm, showed the best prediction performance with an AUC value of 0.811 (95% CI:0.685–0.938) in the test cohort. Compared with the single-phase models, the dual-phase (AP+VP) model with the LR algorithm showed better prediction performance (AUC=0.826, 95% CI:0.700-0.952). The performance of multiphasic (AP+VP+NCP) model with the LR algorithm (AUC=0.811, 95%CI: 0.679-0.944) is comparable to the model with the SVM algorithm (AUC=0.811, 95%CI: 0.695-0.918) in the test cohort, but the sensitivity, specificity, and accuracy of the multiphasic (AP+VP+NCP) model with the LR algorithm were 0.810, 0.808, 0.809 respectively, which were highest among these seven groups of prediction models in the test cohort.

Conclusion

The CT radiomics models have the potential to predict KRAS mutation in patients with CRC; different phases may affect the predictive efficacy of radiomics model, of which arterial-phase CT is more informative. The combination of multiphasic CT images can further improve the performance of radiomics model.

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.

Imaging features associated with survival outcomes among colorectal cancer patients with and without KRAS mutation

Background

Mutations in Kirsten rat sarcoma proto-oncogene (KRAS) have been shown to be associated with advanced-stage colorectal cancer (CRC), negative disease outcomes, and poor response to treatment. The purpose of this study was to investigate which CT features are biomarkers for KRAS gene mutation and impact the survival outcomes of colorectal cancer patients.

Results

Of the 113 CRC patients included in the study, 46 had KRAS mutations (40.71%) and 67 had no mutations (59.29%). Regional lymph node necrosis was the only imaging feature significantly associated with KRAS mutation (P = 0.011). Higher T staging and liver, lung, and distant metastasis were prognostic factors for CRC (P = 0.014, P < 0.001, P = 0.022, P < 0.001, respectively). There were no significant differences in overall survival between patients with KRAS mutations and those without (P = 0.159). However, in patients with no KRAS mutation, those with CRC on the left side had a significantly higher rate of survival than those with CRC on the right (P = 0.005).

Conclusion

Regional lymph node necrosis may be an imaging biomarker of CRC with KRAS mutation, possibly indicating poor prognosis.

Prediction of the differences in tumor mutation burden between primary and metastatic lesions by radiogenomics

Tumor mutational burden (TMB) is gaining attention as a biomarker for responses to immune checkpoint inhibitors in cancer patients. In this study, we evaluated the status of TMB in primary and liver metastatic lesions in patients with colorectal cancer (CRC). In addition, the status of TMB in primary and liver metastatic lesions was inferred by radiogenomics on the basis of computed tomography (CT) images. The study population included 24 CRC patients with liver metastases. DNA was extracted from primary and liver metastatic lesions obtained from the patients and TMB values were evaluated by next‐generation sequencing. The TMB value was considered high when it equaled to or exceeded 10/100 Mb. Radiogenomic analysis of TMB was performed by machine learning using CT images and the construction of prediction models. In 7 out of 24 patients (29.2%), the TMB status differed between the primary and liver metastatic lesions. Radiogenomic analysis was performed to predict whether TMB status was high or low. The maximum values for the area under the receiver operating characteristic curve were 0.732 and 0.812 for primary CRC and CRC with liver metastasis, respectively. The sensitivity, specificity, and accuracy of the constructed models for TMB status discordance were 0.857, 0.600, and 0.682, respectively. Our results suggested that accurate inference of the TMB status is possible using radiogenomics. Therefore, radiogenomics could facilitate the diagnosis, treatment, and prognosis of patients with CRC in the clinical setting.

Radiogenomics of gastroenterological cancer: The dawn of personalized medicine with artificial intelligence-based image analysis

Radiogenomics is a new field of medical science that integrates two omics, radiomics and genomics, and may bring a major paradigm shift in traditional personalized medicine strategies that require tumor tissue samples. In addition, the acquisition of the data does not require special imaging equipment or special imaging conditions, and it is possible to use image information from computed tomography, magnetic resonance imaging, positron emission tomography-computed tomography in clinical practice, so the versatility and cost-effectiveness of radiogenomics are expected. So far, the field of radiogenomics has developed, especially in the fields of brain tumors and breast cancer, but recently, reports of radiogenomic research on gastroenterological cancer are increasing. This review provides an overview of radiogenomic research methods and summarizes the current radiogenomic research in gastroenterological cancer. In addition, the application of artificial intelligence is considered to be indispensable for the integrated analysis of enormous omics information in the future, and the future direction of this research, including the latest technologies, will be discussed.

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.

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.

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.

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.

Radiomics-Based Preoperative Prediction of Lymph Node Status Following Neoadjuvant Therapy in Locally Advanced Rectal Cancer

Background and Purpose: Lymph node status is a key factor for the recommendation of organ preservation for patients with locally advanced rectal cancer (LARC) following neoadjuvant therapy but generally confirmed post-operation. This study aimed to preoperatively predict the lymph node status following neoadjuvant therapy using multiparametric magnetic resonance imaging (MRI)-based radiomic signature.

Materials and Methods: A total of 391 patients with LARC who underwent neoadjuvant therapy and TME were included, of which 261 and 130 patients were allocated to the primary cohort and the validation cohort, respectively. The tumor area, as determined by preoperative MRI, underwent radiomics analysis to build a radiomic signature related to lymph node status. Two radiologists reassessed the lymph node status on MRI. The radiomic signature and restaging results were included in a multivariate analysis to build a combined model for predicting the lymph node status. Stratified analyses were performed to test the predictive ability of the combined model in patients with post-therapeutic MRI T1-2 or T3-4 tumors, respectively.

Results: The combined model was built in the primary cohort, and predicted lymph node metastasis (LNM+) with an area under the curve of 0.818 and a negative predictive value (NPV) of 93.7% were considered in the validation cohort. Stratified analyses indicated that the combined model could predict LNM+ with a NPV of 100 and 87.8% in the post-therapeutic MRI T1-2 and T3-4 subgroups, respectively.

Conclusion: This study reveals the potential of radiomics as a predictor of lymph node status for patients with LARC following neoadjuvant therapy, especially for those with post-therapeutic MRI T1-2 tumors.

The Impact of Primary Tumor and Locoregional Metastatic Lymph Node SUVmax on Predicting Survival in Patients with Rectal Cancer

Objectives:

The aim of this study was to evaluate the impact of maximum standard uptake value (SUV_max) of the primary tumor and locoregional metastatic lymph node in predicting survival in patients with the preoperative rectal adenocarcinoma.

Methods:

One hundred and fifteen patients [mean age ± standard deviation (SD): 58.7±11.4 years] with biopsy-proven rectal adenocarcinoma underwent ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) imaging for the staging were included in this study. All patients were followed-up for a minimum of 12 months (mean ± SD: 29.7±13.5 months). Tumor-node-metastasis 2017 clinical staging, SUV_max of the primary rectal tumor and locoregional lymph nodes on the PET/CT studies were evaluated.

Results:

All patients had increased FDG activity of the primary tumor. The mean ± SD SUV_max of the primary tumor and locoregional metastatic lymph node were 21.0±9.1 and 4.6±2.8, respectively. Primary tumor SUV_max did not have an effect on predicting survival (p=0.525) however locoregional metastatic lymph node SUV_max had an effect (p<0.05) on predicting survival. Clinical stage of the disease was a factor predicting survival (p<0.001).

Conclusion:

¹⁸F-FDG PET/CT is an effective imaging modality for detecting primary tumors and metastases in rectal adenocarcinoma and clinical stage assessment with PET/CT had an effect on predicting survival. Furthermore, in our study locoregional lymph node SUV_maks was defined as a factor in predicting survival.

Decoding the Genomic Report for Radiologists

OBJECTIVE. The purpose of this review is to provide a guide for radiologists that explains the language and format of modern genomic reports and summarizes the relevance of this information for modern oncologic imaging. CONCLUSION. Genomic testing plays a critical role in guiding oncologic therapies in the age of targeted treatments. Understanding and interpreting genomic reports is a valuable skill for radiologists involved with oncologic imaging interpretation.

Lung metastasectomy after colorectal cancer: prognostic impact of resection margin on long term survival, a retrospective cohort study

BACKGROUND

Pulmonary metastasectomy is considered a potentially curative treatment for selected patients with metastatic colorectal cancer (CRC). Several prognostic factors have been analysed, but to date, it is still not well defined which is the optimal resection margin during lung metastasectomy (LM). This study analyses the long-term results and prognostic factors after LM in CRC patients with particular attention to the resection margins. Primary endpoint of this study is to assess the correlation between resection margins and long-term outcomes.

METHODS

Observational cohort study on all proven cases of CRC lung metastases (2000-2016) resected with curative intent in a single centre.

RESULTS

The series included 210 consecutive patients (M/F 133/77) with a mean age of 65.4 (± 9.96) years, 75% (159/210) of them with a solitary metastasis. Mean size of metastasis was 2.57 cm (± 1.45). One hundred sixty-eight patients underwent wedge resections (80%) and lymphadenectomy was carried out in 90 cases (42.9%). With a mean follow-up of 56 months (range 5-192), we observed a 1-, 3- and 5-year overall survival (OS) of 95%, 74% and 54%, respectively. The patients were divided into three groups according to the resection margin distance from the tumour: (a) ≥ 2 cm (145 cases); (b) < 2, ≥ 1 cm (37 cases); and (c) < 1 cm (12 cases). The OS was significantly different between the three groups (p = 0,020); univariate and multivariate analyses showed that a narrow resection margin was an independent prognostic factor of worse survival (p = 0.006 and HR 3.4 p = 0.009).

CONCLUSIONS

Long-term survival of patients after LM is strongly associated with a greater distance between the lesion and the resection margin.

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.

A chirality-dependent action of vitamin C in suppressing Kirsten rat sarcoma mutant tumor growth by the oxidative combination: Rationale for cancer therapeutics

Kirsten rat sarcoma (KRAS) mutant cancers, which constitute the vast majority of pancreatic tumors, are characterized by their resistance to established therapies and high mortality rates. Here, we developed a novel and extremely effective combinational therapeutic approach to target KRAS mutant tumors through the generation of a cytotoxic oxidative stress. At high concentrations, vitamin C (VC) is known to provoke oxidative stress and selectively kill KRAS mutant cancer cells, although its effects are limited when it is given as monotherapy. We found that the combination of VC and the oxidizing drug arsenic trioxide (ATO) is an effective therapeutic treatment modality. Remarkably, its efficiency is dependent on chirality of VC as its enantiomer d-optical isomer of VC (d-VC) is significantly more potent than the natural l-optical isomer of VC. Thus, our results demonstrate that the oxidizing combination of ATO and d-VC is a promising approach for the treatment of KRAS mutant human cancers.

Colorectal cancer: Parametric evaluation of morphological, functional and molecular tomographic imaging

Colorectal cancer (CRC) represents one of the leading causes of tumor-related deaths worldwide. Among the various tools at physicians’ disposal for the diagnostic management of the disease, tomographic imaging (e.g., CT, MRI, and hybrid PET imaging) is considered essential. The qualitative and subjective evaluation of tomographic images is the main approach used to obtain valuable clinical information, although this strategy suffers from both intrinsic and operator-dependent limitations. More recently, advanced imaging techniques have been developed with the aim of overcoming these issues. Such techniques, such as diffusion-weighted MRI and perfusion imaging, were designed for the “in vivo” evaluation of specific biological tissue features in order to describe them in terms of quantitative parameters, which could answer questions difficult to address with conventional imaging alone (e.g., questions related to tissue characterization and prognosis). Furthermore, it has been observed that a large amount of numerical and statistical information is buried inside tomographic images, resulting in their invisibility during conventional assessment. This information can be extracted and represented in terms of quantitative parameters through different processes (e.g., texture analysis). Numerous researchers have focused their work on the significance of these quantitative imaging parameters for the management of CRC patients. In this review, we aimed to focus on evidence reported in the academic literature regarding the application of parametric imaging to the diagnosis, staging and prognosis of CRC while discussing future perspectives and present limitations. While the transition from purely anatomical to quantitative tomographic imaging appears achievable for CRC diagnostics, some essential milestones, such as scanning and analysis standardization and the definition of robust cut-off values, must be achieved before quantitative tomographic imaging can be incorporated into daily clinical practice.

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.

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.

SUVmax and metabolic tumor volume: surrogate image biomarkers of KRAS mutation status in colorectal cancer

Purpose

The objective of this study was to explore the association between KRAS mutation status and PET/CT metabolic parameters in colorectal cancer (CRC) patients.

Materials and methods

One hundred and sixty-four CRC patients were enrolled in this study and received PET/CT examination before operation, then KRAS mutation status was analyzed through pathologically confirmed CRC samples. The association between tumor clinical characteristics and PET/CT metabolic parameters, including maximum standardized uptake value (SUVmax), SUVmean, and metabolic tumor volume (MTV), and KRAS mutation status was analyzed using chi-squared tests, Mann-Whitney U tests, and logistic regression analysis.

Results

The KRAS mutation type patients exhibited high MTV and high SUVmax using a threshold of 17.8 cm³ and 8.7 respectively and the predictive accuracy was 0.772 and 0.603 respectively. High MTV (P=0.001; 95% CI: 1.119-1.296) and high SUVmax (P=0.048; 95% CI: 0.564-0.985) were independent predictors for KRAS mutation status.

Conclusion

MTV and SUVmax were associated with KRAS mutation type in CRC patients. PET/CT metabolic parameters can be used for supplementing KRAS mutation status prediction in CRC patients.

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.

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.

KRAS/BRAF基因与结肠癌糖代谢研究现状

正电子发射断层成像术(positron emission tomography, PET)/计算机断层扫描(computed tomography, CT)显像可用于结肠癌的诊断、监测疗效和预后评估. ¹⁸F标记葡萄糖(2-fluorine-18-fluoro-2-deeoxy-D-glucose, ¹⁸F-FDG)是PET/CT常用显像剂, 可以反映结肠癌活体组织葡萄糖代谢. KRAS/BRAF基因检测常用于结肠癌靶向治疗方案的选择及评估其治疗效果. 文献报道¹⁸F-FDG-PET/CT显像可预测结肠癌KRAS/BRAF基因状态, 能以无创的方式预测结肠癌抗表皮生长因子受体靶向治疗效果. 目前国内有关KRAS/BRAF基因与结肠癌糖代谢的研究相对较少, 本文结合近期的相关文献进行概述.

Targeting metabolic reprogramming in KRAS-driven cancers

Mutations of KRAS are found in a variety of human malignancies, including in pancreatic cancer, colorectal cancer, and non-small cell lung cancer at high frequency. To date, no effective treatments that target mutant variants of KRAS have been introduced into clinical practice. In recent years, a number of studies have shown that the oncogene KRAS plays a critical role in controlling cancer metabolism by orchestrating multiple metabolic changes. One of the metabolic hallmarks of malignant tumor cells is their dependency on aerobic glycolysis, known as the Warburg effect. The role of KRAS signaling in the regulation of aerobic glycolysis has been reported in several types of cancer. KRAS-driven cancers are characterized by altered metabolic pathways involving enhanced nutrients uptake, enhanced glycolysis, enhanced glutaminolysis, and elevated synthesis of fatty acids and nucleotides. However, Just how mutated KRAS can coordinate the metabolic shift to promote tumor growth and whether specific metabolic pathways are essential for the tumorigenesis of KRAS-driven cancers are questions which remain to be answered. In this context, the aim of this review is to summarize current data on KRAS-related metabolic alterations in cancer cells. Given that cancer cells rely on changes in metabolism to support their growth and survival, the targeting of metabolic processes may be a potential strategy for treating KRAS-driven cancers.

Correlation between KRAS mutation and 18F-FDG uptake in stage IV colorectal cancer

PURPOSE

The purpose of this study is to evaluate the correlation between KRAS mutation, ¹⁸F-FDG uptake, and metastatic pattern in advanced stage colorectal cancer (CRC) patients.

METHODS

Medical records of stage IV CRC patients who underwent ¹⁸F-FDG PET/CT for staging and KRAS mutation analysis were selected. On PET scans, a volume of interest (VOI) was drawn on the primary lesion. ¹⁸F-FDG indices (SUVmax, SUVmean, MTV, TLG) of the primary lesions were obtained and correlated with KRAS mutation of the primary lesion. Also, metastatic sites were recorded. Association between metastatic pattern and KRAS expression and FDG indices were analyzed.

RESULTS

KRAS mutation was positive in 40 (43%) patients. Evaluation of FDG indices showed that higher SUVmax (14.0 vs. 11.2, p = 0.004), higher SUVmean (5.3 vs. 4.7, p = 0.005), and higher TLG (301.4 vs. 205.5, p = 0.023) were predictive of KRAS mutation compared to wild-type (WT) KRAS. Lung metastasis was more frequently involved in patients with KRAS mutation (50.0% vs. 22.6%, p = 0.006), and liver metastasis was more frequently involved in patients with WT KRAS (81.1% vs. 55.0%, p = 0.007). Multivariate analysis showed that  primary tumor location (OR 3.92, p = 0.07) and KRAS mutation (OR 2.45, p = 0.09) were significant factors in lung metastasis model.

CONCLUSION

KRAS mutation patients had more frequent lung metastasis and had higher ¹⁸F-FDG uptake compared to WT KRAS in stage IV CRC.

Metabolic Tumor Volume and Total Lesion Glycolysis in PET/CT Correlate With the Pathological Findings of Colorectal Cancer and Allow Its Accurate Staging

INTRODUCTION

PET/CT plays an important role in cancer diagnosis. Recently, novel metabolic parameters in PET/CT such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG) have been reported to be diagnostic and prognostic biomarkers of various cancers. We evaluated the diagnostic value of these metabolic parameters in colorectal cancer (CRC).

METHODS

The study included 138 patients who underwent surgical resection of CRCs between August 2012 and March 2014. The MTVs and TLGs of tumors were measured using various SUV thresholds. The diagnostic abilities of the metabolic parameters were analyzed using ROC curves and classification and regression trees.

RESULTS

The AUCs of the MTVs and TLGs for predicting T stage (0.881-0.892) were significantly higher than the AUC of the SUVmax (0.824). In the M stage, the AUCs of MTVs and TLGs (0.688-0.723) were significantly higher than that of the SUVmax (0.606). Recursive partitioning applying classification and regression trees demonstrated that the optimal cutoff values of the most important variables for discriminating T, N, and M stages are MTV2.5 = 9.35 and 63.33 mL, TLG50% = 328.1, and TLG50% = 94.81, respectively.

CONCLUSION

Metabolic tumor volumes and TLGs in PET/CT are reliable diagnostic biomarkers. Using these parameters, more accurate preoperative diagnoses for CRC can be made.

Mechanisms underlying 18F-fluorodeoxyglucose accumulation in colorectal cancer

Positron emission tomography (PET) with ¹⁸F-fluorodeoxyglucose (FDG) is a diagnostic tool to evaluate metabolic activity by measuring accumulation of FDG, an analogue of glucose, and has been widely used for detecting small tumors, monitoring treatment response and predicting patients’ prognosis in a variety of cancers. However, the molecular mechanism of FDG accumulation into tumors remains to be investigated. It is well-known that most cancers are metabolically active with elevated glucose metabolism, a phenomenon known as the Warburg effect. The underlying mechanisms for elevated glucose metabolism in cancer tissues are complex. Recent reports have indicated the potential of FDG-PET/CT scans in predicting mutational status (e.g., KRAS gene mutation) of colorectal cancer (CRC), which suggests that FDG-PET/CT scans may play a key role in determining therapeutic strategies by non-invasively predicting treatment response to anti-epidermal growth factor receptor (EGFR) therapy. In this review, we summarize the current findings investigating the molecular mechanism of ¹⁸F-FDG accumulation in CRC.

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

OBJECTIVE

Treating metastatic colorectal cancer with anti-EGFR monoclonal antibodies is recommended only for patients whose tumour does not harbour mutations of KRAS or NRAS. The aim of this study was to investigate the biology of rectal cancers and specifically to evaluate the relationship between fluorine-18 fludeoxyglucose ((18)F-FDG) positron emission tomography (PET) intensity and heterogeneity parameters and their mutational status.

METHODS

151 patients with newly diagnosed rectal cancer were included in this retrospective study. All patients underwent a baseline (18)F-FDG PET/CT within a median time interval of 27 days of tumour tissue sampling, which was performed before any treatment. Standardized uptake values (SUVs), volume-based parameters and texture analysis were studied. We retrospectively performed KRAS genotyping on codons 12, 13, 61, 117 and 146, NRAS genotyping on codons 12, 13 and 61 and BRAF on codon 600. Associations between PET/CT parameters and the mutational status were assessed using univariate and multivariate analysis.

RESULTS

83 (55%) patients had an RAS mutation: 74 KRAS and 9 NRAS, while 68 patients had no mutation (wild-type tumours). No patient had BRAF mutation. First-order features based on intensity histogram analysis were significantly associated with RAS mutations: maximum SUV (SUVmax) (p-value = 0.002), mean SUV (p-value = 0.006), skewness (p-value = 0.049), SUV standard deviation (p-value = 0.001) and SUV coefficient of variation (SUVcov) (p-value = 0.001). Both SUVcov and SUVmax showed an area under the curve of 0.65 with sensitivity of 56% and 69%, respectively, and specificity of 64% and 52%, respectively. None of the volume-based (metabolic tumour volume and total lesion glycolysis), nor local or regional textural features were associated with the presence of RAS mutations.

CONCLUSION

Although rectal cancers with KRAS or NRAS mutations display a significantly higher glucose metabolism than wild-type cancers, the accuracy of the currently proposed quantitative metrics extracted from (18)F-FDG PET/CT is not sufficiently high for playing a meaningful clinical role.

ADVANCES IN KNOWLEDGE

RAS-mutated rectal cancers have a significantly higher glucose metabolism. However, the accuracy of (18)F-FDG PET/CT quantitative metrics is not as such as the technique could play a clinical role.

Relationship Between 18F-Fluorodeoxyglucose Uptake and V-Ki-Ras2 Kirsten Rat Sarcoma Viral Oncogene Homolog Mutation in Colorectal Cancer Patients: Variability Depending on C-Reactive Protein Level

To evaluate clinical values of clinicopathologic and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT)-related parameters for prediction of v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation in colorectal cancer (CRC) and to investigate their variability depending on C-reactive protein (CRP) levels. In total, 179 CRC patients who underwent PET/CT scans before curative resection and KRAS mutation evaluation following surgery were enrolled. Maximum standardized uptake value (SUV max), peak standardized uptake value (SUV peak), metabolic tumor volume, and total lesion glycolysis were determined semiquantitatively. Associations between clinicopathologic and PET/CT-related parameters and KRAS expression were analyzed. Elevated CRP (> 6.0 mg/L; n = 47) was associated with higher primary tumor size, higher SUV max, SUV peak, metabolic tumor volume, and total lesion glycolysis, compared with those for the group with a CRP lower than that the cutoff value (< 6.0 mg/L; n = 132). Interestingly, the CRC patients (having CRP < 6.0 mg/L) with KRAS mutations had significantly higher (P < 0.05) SUV max and SUV peak values than the patients expressing wild-type KRAS mutations. Multivariate analysis revealed SUV max and SUV peak to be significantly associated with KRAS mutations (odds ratio = 3.3, P = 0.005, and odds ratio = 3.9, P = 0.004), together with histologic grade and lymph node metastasis. 18F-FDG uptake was significantly higher in CRC patients with KRAS mutations and with normal CRP levels. A severe local inflammation with raised CRP levels, however, might affect accurate 18F-FDG quantification in CRC tumors. Positron emission tomography/computed tomography-related parameters could supplement genomic analysis to determine KRAS expression in CRC; however, care should be exercised to guarantee proper patient selection.

Vitamin C selectively kills KRAS and BRAF mutant colorectal cancer cells by targeting GAPDH

More than half of human colorectal cancers (CRCs) carry either KRAS or BRAF mutations and are often refractory to approved targeted therapies. We found that cultured human CRC cells harboring KRAS or BRAF mutations are selectively killed when exposed to high levels of vitamin C. This effect is due to increased uptake of the oxidized form of vitamin C, dehydroascorbate (DHA), via the GLUT1 glucose transporter. Increased DHA uptake causes oxidative stress as intracellular DHA is reduced to vitamin C, depleting glutathione. Thus, reactive oxygen species accumulate and inactivate glyceraldehyde 3-phosphate dehydrogenase (GAPDH). Inhibition of GAPDH in highly glycolytic KRAS or BRAF mutant cells leads to an energetic crisis and cell death not seen in KRAS and BRAF wild-type cells. High-dose vitamin C impairs tumor growth in Apc/Kras(G12D) mutant mice. These results provide a mechanistic rationale for exploring the therapeutic use of vitamin C for CRCs with KRAS or BRAF mutations.

Relationship Between 18F-FDG PET/CT Scans and KRAS Mutations in Metastatic Colorectal Cancer

Several studies have shown that KRAS mutations in colorectal cancer (CRC) result in the lack of response to anti-epidermal growth factor receptor-based therapy; thus, KRAS mutational testing has been incorporated into routine clinical practice. However, 1 limitation of this test is the heterogeneity of KRAS status, which can be either intratumoral heterogeneity within an individual primary CRC or discordant KRAS status between a primary CRC and its corresponding metastases. We previously reported that (18)F-FDG accumulation was significantly higher in primary CRCs with mutated KRAS than in those with wild-type KRAS. However, the clinical utility of the previous report has been limited because endoscopic biopsy for testing KRAS status is safe and feasible only in primary CRC. The purpose of this study was to investigate whether KRAS status is associated with (18)F-FDG accumulation in metastatic CRC and whether (18)F-FDG PET/CT scans can be used to predict the KRAS status of metastatic CRC.

METHODS

A retrospective analysis was performed on 55 metastatic CRC tumors that were identified by (18)F-FDG PET/CT before surgical resection. Maximum standardized uptake value (SUVmax) of the respective metastatic tumor was calculated from (18)F-FDG accumulation.

RESULTS

From the analysis with the 55 tumors, no significant correlation was found between SUVmax and KRAS status. We next analyzed only tumors larger than 10 mm to minimize the bias of partial-volume effect and found that SUVmax was significantly higher in the KRAS-mutated group than in the wild-type group (8.3 ± 4.1 vs. 5.7 ± 2.4, respectively; P = 0.03). Multivariate analysis indicated that SUVmax remained significantly associated with KRAS mutations (P = 0.04). KRAS status could be predicted with an accuracy of 71.4% when an SUVmax cutoff value of 6.0 was used.

CONCLUSION

(18)F-FDG accumulation into metastatic CRC was associated with KRAS status. (18)F-FDG PET/CT scans may be useful for predicting the KRAS status of metastatic CRC and help in determining the therapeutic strategies against metastatic CRC.

Correlation between long-term aspirin use and F-fluorodeoxyglucose uptake in colorectal cancer measured by PET/CT

PURPOSE

The aim of this study was to evaluate the relationship between long-term aspirin use with pretreatment 18 Fluorodeoxyglucose (FDG) uptake of primary lesions of Colorectal cancer (CRC) and evaluate their clinical significance.

MATERIALS AND METHODS

We enrolled 84 patients with CRC who underwent 18F-FDG PET/CT scanning before surgery between 1st July 2008 and 1st March 2013 and followed up until 1st March 2014. Maximum standardized uptake value (SUVmax) of the primary tumor was measured by 18F-FDG PET/CT. The history of aspirin taken and other clinicopathogical factors were also obtained and their relationships were examined by Mann-Whitney or χ2 tests. Progression-free survival (PFS) was determined by standard Kaplan-Meier survival analysis. Cox proportional hazards regression was performed to determine whether history of aspirin taken, pretreatment SUVmax, age, gender, TNM stage, tumor sizes and differentiation influenced outcomes.

RESULTS

CRC Patients with long-term history of aspirin use had lower SUVmax of primary lesions than control group (9.74±2.62 vs. 13.91±6.18) and showed a trend towards improved PFS after curative surgery. However, pretreatment of SUVmax showed no prognostic value in patients with CRC.

CONCLUSIONS

Long-term aspirin use is associated with lower pretreatment SUVmax of CRC and is a promising prognostic factor for predicting PFS in patients with CRC.