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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.

ABSTRACT: 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.

SUBMITTER: Shi R

PROVIDER: S-EPMC7783758 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Purpose: This study aimed to compare the performance of radiomics and deep learning in predicting EGFR mutation status in patients with lung cancer based on PET/CT images, and tried to explore a model with excellent prediction performance to accurately predict EGFR mutation status in patients with non-small cell lung cancer (NSCLC). Method: PET/CT images of 194 NSCLC patients from Xijing Hospital were collected and divided into a training set and a validation set according to the ratio of 7:3. Statistics were made on patients' clinical characteristics, and a large number of features were extracted based on their PET/CT images (4306 radiomics features and 2048 deep learning features per person) with the pyradiomics toolkit and 3D convolutional neural network. Then a radiomics model (RM), a deep learning model (DLM), and a hybrid model (HM) were established. The performance of the three models was compared by receiver operating characteristic (ROC) curves, sensitivity, specificity, accuracy, calibration curves, and decision curves. In addition, a nomogram based on a deep learning score (DS) and the most significant clinical characteristic was plotted. Result: In the training set composed of 138 patients (64 with EGFR mutation and 74 without EGFR mutation), the area under the ROC curve (AUC) of HM (0.91, 95% CI: 0.86-0.96) was higher than that of RM (0.82, 95% CI: 0.75-0.89) and DLM (0.90, 95% CI: 0.85-0.95). In the validation set composed of 57 patients (32 with EGFR mutation and 25 without EGFR mutation), the AUC of HM (0.85, 95% CI: 0.77-0.93) was also higher than that of RM (0.68, 95% CI: 0.52-0.84) and DLM (0.79, 95% CI: 0.67-0.91). In all, HM achieved better diagnostic performance in predicting EGFR mutation status in NSCLC patients than two other models. Conclusion: Our study showed that the deep learning model based on PET/CT images had better performance than radiomics model in diagnosing EGFR mutation status of NSCLC patients based on PET/CT images. Combined with the most statistically significant clinical characteristic (smoking) and deep learning features, our hybrid model had better performance in predicting EGFR mutation types of patients than two other models, which could enable NSCLC patients to choose more personalized treatment schemes.

Project description:BackgroundAnti-EGFR antibody-based treatment is an important therapeutic strategy for advanced colorectal cancer (CRC); despite this, several mutations--including KRAS, BRAF, and PIK3CA mutations, and HER2 amplification--are associated with the mechanisms underlying the development of resistance to anti-EGFR therapy. The aim of our study was to investigate the frequencies and clinical implications of these genetic alterations in advanced CRC.MethodsKRAS, BRAF, and PIK3CA mutations were determined by Cobas real-time polymerase chain reaction (PCR) in 191 advanced CRC patients with distant metastasis. Microsatellite instability (MSI) status was determined by a fragmentation assay and HER2 amplification was assessed by silver in situ hybridization. In addition, KRAS mutations were investigated by the Sanger sequencing method in 97 of 191 CRC cases.ResultsMutations in KRAS, BRAF, and PIK3CA were found in 104 (54.5%), 6 (3.1%), and 25 (13.1%) cases of advanced CRC, respectively. MSI-high status and HER2 amplification were observed in 3 (1.6%) and 16 (8.4%) cases, respectively. PIK3CA mutations were more frequently found in KRAS mutant type (18.3%) than KRAS wild type (6.9%) (P = 0.020). In contrast, HER2 amplifications and BRAF mutations were associated with KRAS wild type with borderline significance (P = 0.052 and 0.094, respectively). In combined analyses with KRAS, BRAF and HER2 status, BRAF mutations or HER2 amplifications were associated with the worst prognosis in the wild type KRAS group (P = 0.004). When comparing the efficacy of detection methods, the results of real time PCR analysis revealed 56 of 97 (57.7%) CRC cases with KRAS mutations, whereas Sanger sequencing revealed 49 cases (50.5%).ConclusionsKRAS mutations were found in 54.5% of advanced CRC patients. Our results support that subgrouping using PIK3CA and BRAF mutation or HER2 amplification status, in addition to KRAS mutation status, is helpful for managing advanced CRC patients.

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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.

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