Radiomics in colorectal cancer patients

Effectiveness of magnetic resonance imaging and spiral computed tomography in the staging and treatment prognosis of colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a prevalent cancer type in clinical settings; its early signs can be difficult to detect, which often results in late-stage diagnoses in many patients. The early detection and diagnosis of CRC are crucial for improving treatment success and patient survival rates. Recently, imaging techniques have been hypothesized to be essential in managing CRC, with magnetic resonance imaging (MRI) and spiral computed tomography (SCT) playing a significant role in enhancing diagnostic and treatment approaches.

AIM

To explore the effectiveness of MRI and SCT in the preoperative staging of CRC and the prognosis of laparoscopic treatment.

METHODS

Ninety-five individuals admitted to Zhongshan Hospital Xiamen University underwent MRI and SCT and were diagnosed with CRC. The precision of MRI and SCT for the presurgical classification of CRC was assessed, and pathological staging was used as a reference. Receiver operating characteristic curves were used to evaluate the diagnostic efficacy of blood volume, blood flow, time to peak, permeability surface, blood reflux constant, volume transfer constant, and extracellular extravascular space volume fraction on the prognosis of patients with CRC.

RESULTS

Pathological biopsies confirmed the following CRC stages: 23, 23, 32, and 17 at T1, T2, T3, and T4, respectively. There were 39 cases at the N0 stage, 22 at N1, 34 at N2, 44 at M0 stage, and 51 at M1. Using pathological findings as the benchmark, the combined use of MRI and SCT for preoperative TNM staging in patients with CRC demonstrated superior sensitivity, specificity, and accuracy compared with either modality alone, with a statistically significant difference in accuracy (P < 0.05). Receiver operating characteristic curve analysis revealed the predictive values for laparoscopic treatment prognosis, as indicated by the areas under the curve for blood volume, blood flow, time to peak, and permeability surface, blood reflux constant, volume transfer constant, and extracellular extravascular space volume fraction were 0.750, 0.683, 0.772, 0.761, 0.709, 0.719, and 0.910, respectively. The corresponding sensitivity and specificity values were also obtained (P < 0.05).

CONCLUSION

MRI with SCT is effective in the clinical diagnosis of patients with CRC and is worthy of clinical promotion.

OCTA-based research on changes of retinal microcirculation in digestive tract malignancy

PURPOSE

This cross-sectional study measured retinal vessel density (VD) in patients with digestive tract malignancy by optical coherence tomography angiography (OCTA), and compared them with healthy controls to explore the retinal microcirculation changes in patients with digestive tract malignancy.

METHODS

106 eligible participants were divided into three groups: gastric cancer (GC) group (36 individuals), colorectal cancer (CRC) group (34 individuals), and healthy control group (36 individuals). Angio 6*6 512*512 R4 and ONH Angio 6*6 512*512 R4 modes were performed to collect retinal vessel density data centered on fovea and papillary, respectively. The retina was automatically segmented into different layers (superficial vascular plexus (SVP), the inner retinal layer, radial peripapillary capillary plexus (RPCP), deep vascular plexus (DVP)) and areas to analyze.

RESULTS

At the optic nerve head (ONH) region, the VD of the inner retinal layer increased in both GC and CRC groups in all quadrants and areas. In the papillary area, VD in the inner retinal layer, SVP, and RPCP increased in the GC and CRC groups. In the parapapillary area, VD in the inner retinal layer increased in the GC and the CRC groups. Significant increase in the global VD were found in the GC group of the RPCP and SVP. Regarding the macular region, no statistical differences were observed in each layer.

CONCLUSIONS

The study suggested that retinal vessel density changed in patients with digestive tract malignancy, especially in the inner retinal layer of the ONH region, revealing the potential relevance of the relation between gastrointestinal cancer and retinal microcirculation.

Radiomics analysis of dual-layer spectral-detector CT-derived iodine maps for predicting tumor deposits in colorectal cancer

OBJECTIVE

To investigate the value of radiomics analysis of dual-layer spectral-detector computed tomography (DLSCT)-derived iodine maps for predicting tumor deposits (TDs) preoperatively in patients with colorectal cancer (CRC).

MATERIALS AND METHODS

A total of 264 pathologically confirmed CRC patients (TDs + (n = 80); TDs - (n = 184)) who underwent preoperative DLSCT from two hospitals were retrospectively enrolled, and divided into training (n = 124), testing (n = 54), and external validation cohort (n = 86). Conventional CT features and iodine concentration (IC) were analyzed and measured. Radiomics features were derived from venous phase iodine maps from DLSCT. The least absolute shrinkage and selection operator (LASSO) was performed for feature selection. Finally, a support vector machine (SVM) algorithm was employed to develop clinical, radiomics, and combined models based on the most valuable clinical parameters and radiomics features. Area under receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis were used to evaluate the model's efficacy.

RESULTS

The combined model incorporating the valuable clinical parameters and radiomics features demonstrated excellent performance in predicting TDs in CRC (AUCs of 0.926, 0.881, and 0.887 in the training, testing, and external validation cohorts, respectively), which outperformed the clinical model in the training cohort and external validation cohorts (AUC: 0.839 and 0.695; p: 0.003 and 0.014) and the radiomics model in two cohorts (AUC: 0.922 and 0.792; p: 0.014 and 0.035).

CONCLUSION

Radiomics analysis of DLSCT-derived iodine maps showed excellent predictive efficiency for preoperatively diagnosing TDs in CRC, and could guide clinicians in making individualized treatment strategies.

CLINICAL RELEVANCE STATEMENT

The radiomics model based on DLSCT iodine maps has the potential to aid in the accurate preoperative prediction of TDs in CRC patients, offering valuable guidance for clinical decision-making.

KEY POINTS

Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge. The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs. The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.

Can Pretreatment MRI and Planning CT Radiomics Improve Prediction of Complete Pathological Response in Locally Advanced Rectal Cancer Following Neoadjuvant Treatment?

OBJECTIVE(S)

The treatment response to neoadjuvant chemoradiation (nCRT) differs largely in individuals treated for rectal cancer. In this study, we investigated the role of radiomics to predict the pathological response in locally advanced rectal cancers at different treatment time points: (1) before the start of any treatment using baseline T2-weighted MRI (T2W-MR) and (2) at the start of radiation treatment using planning CT.

METHODS

Patients on nCRT followed by surgery between June 2017 to December 2019 were included in the study. Histopathological tumour response grading (TRG) was used for classification, and gross tumour volume was defined by the radiation oncologists. Following resampling, 100 and 103 pyradiomic features were extracted from T2W-MR and planning CT images, respectively. Synthetic minority oversampling technique (SMOTE) was used to address class imbalance. Four machine learning classifiers built clinical, radiomic, and merged models. Model performances were evaluated on a held-out test dataset following 3-fold cross-validation using area under the receiver operator characteristic curves (AUC) with bootstrap 95% confidence intervals.

RESULTS

One hundred and fifty patients were included; 58/150 with TRG 1 were classified as complete responders, and rest were incomplete responders (IR). Clinical models performed better (AUC = 0.68) compared to radiomics models (AUC = 0.62). Overall, the clinical + T2W-MR model showed best performance (AUC = 0.72) in predicting the pathological response prior to therapy. Clinical + Planning CT-merged models could only achieve the highest AUC of 0.66.

CONCLUSION

Merging clinical and baseline T2W-MR radiomics enhances predicting pathological response in rectal cancer. Validation in larger cohorts is warranted, especially for watch and wait strategies.

Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment

PURPOSE

We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in a pre-surgical setting, to predict tumor budding in liver metastases.

METHODS

Patients with MRI in a pre-surgical setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. Balancing was performed and inter- and intraclass correlation coefficients were calculated to assess the between observer and within observer reproducibility of all radiomics extracted features. A Wilcoxon-Mann-Whitney nonparametric test and receiver operating characteristics (ROC) analysis were carried out. Balancing and feature selection procedures were performed. Linear and non-logistic regression models (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered.

RESULTS

The internal training set included 49 patients and 119 liver metastases. The validation cohort consisted of a total of 28 single lesion patients. The best single predictor to classify tumor budding was original_glcm_Idn obtained in the T1-W VIBE sequence arterial phase with an accuracy of 84%; wavelet_LLH_firstorder_10Percentile was obtained in the T1-W VIBE sequence portal phase with an accuracy of 92%; wavelet_HHL_glcm_MaximumProbability was obtained in the T1-W VIBE sequence hepatobiliary excretion phase with an accuracy of 88%; and wavelet_LLH_glcm_Imc1 was obtained in T2-W SPACE sequences with an accuracy of 88%. Considering the linear regression analysis, a statistically significant increase in accuracy to 96% was obtained using a linear weighted combination of 13 radiomic features extracted from the T1-W VIBE sequence arterial phase. Moreover, the best classifier was a KNN trained with the 13 radiomic features extracted from the arterial phase of the T1-W VIBE sequence, obtaining an accuracy of 95% and an AUC of 0.96. The validation set reached an accuracy of 94%, a sensitivity of 86% and a specificity of 95%.

CONCLUSIONS

Machine learning and radiomics analysis are promising tools in predicting tumor budding. Considering the linear regression analysis, there was a statistically significant increase in accuracy to 96% using a weighted linear combination of 13 radiomics features extracted from the arterial phase compared to a single radiomics feature.