MRI-based radiomics signature for tumor grading of rectal carcinoma using random forest model

A radiomics strategy based on CT intra-tumoral and peritumoral regions for preoperative prediction of neoadjuvant chemoradiotherapy for esophageal cancer

OBJECTIVE

Develop a method for selecting esophageal cancer patients achieving pathological complete response with pre-neoadjuvant therapy chest-enhanced CT scans.

METHODS

Two hundred and one patients from center 1 were enrolled, split into training and testing sets (7:3 ratio), with an external validation set of 30 patients from center 2. Radiomics features from intra-tumoral and peritumoral images were extracted and dimensionally reduced using Student's t-test and least absolute shrinkage and selection operator. Four machine learning classifiers were employed to build models, with the best-performing models selected based on accuracy and stability. ROC curves were utilized to determine the top prediction model, and its generalizability was evaluated on the external validation set.

RESULTS

Among 16 models, the integrated-XGBoost and integrated-random forest models performed the best, with average ROC AUCs of 0.906 and 0.918, respectively, and RSDs of 6.26 and 6.89 in the training set. In the testing set, AUCs were 0.845 and 0.871, showing no significant difference in ROC curves. External validation set AUCs for integrated-XGBoost and integrated-random forest models were 0.650 and 0.749.

CONCLUSION

Incorporating peritumoral radiomics features into the analysis enhances predictive performance for esophageal cancer patients undergoing neoadjuvant chemoradiotherapy, paving the way for improved treatment outcomes.

Research Progress of Artificial Intelligence in the Grading and Classification of Meningiomas

A meningioma is a common primary central nervous system tumor. The histological features of meningiomas vary significantly depending on the grade and subtype, leading to differences in treatment and prognosis. Therefore, early diagnosis, grading, and typing of meningiomas are crucial for developing comprehensive and individualized diagnosis and treatment plans. The advancement of artificial intelligence (AI) in medical imaging, particularly radiomics and deep learning (DL), has contributed to the increasing research on meningioma grading and classification. These techniques are fast and accurate, involve fully automated learning, are non-invasive and objective, enable the efficient and non-invasive prediction of meningioma grades and classifications, and provide valuable assistance in clinical treatment and prognosis. This article provides a summary and analysis of the research progress in radiomics and DL for meningioma grading and classification. It also highlights the existing research findings, limitations, and suggestions for future improvement, aiming to facilitate the future application of AI in the diagnosis and treatment of meningioma.

Development and Validation of a Radiomics Model Based on Lymph-Node Regression Grading After Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

PURPOSE

The response to neoadjuvant chemoradiotherapy (nCRT) varies among patients with locally advanced rectal cancer (LARC), and the treatment response of lymph nodes (LNs) to nCRT is critical in implementing a watch-and-wait strategy. A robust predictive model may help personalize treatment plans to increase the chance that patients achieve a complete response. This study investigated whether radiomics features based on prenCRT magnetic resonance imaging nodes could predict treatment response in preoperative LARC LNs.

METHODS AND MATERIALS

The study included 78 patients with clinical stage T3-T4, N1-2, and M0 rectal adenocarcinoma who received long-course neoadjuvant radiotherapy before surgery. Pathologists evaluated 243 LNs, of which 173 and 70 were assigned to training and validation cohorts, respectively. For each LN, 3641 radiomics features were extracted from the region of interest in high-resolution T2WI magnetic resonance imaging before nCRT. The least absolute shrinkage and selection operator regression model was used for feature selection and radiomics signature building. A prediction model based on multivariate logistic analysis, combining radiomics signature and selected LN morphologic characteristics, was developed and visualized by drawing a nomogram. The model's performance was assessed by receiver operating characteristic curve analysis and calibration curves.

RESULTS

The radiomics signature consists of 5 selected features that were effectively discriminated within the training cohort (area under the curve [AUC], 0.908; 95% CI, 0.857%-0.958%) and the validation cohort (AUC, 0.865; 95% CI, 0.757%-0.973%). The nomogram, which consisted of radiomics signature and LN morphologic characteristics (short-axis diameter and border contours), showed better calibration and discrimination in the training and validation cohorts (AUC, 0.925; 95% CI, 0.880%-0.969% and AUC, 0.918; 95% CI, 0.854%-0.983%, respectively). The decision curve analysis confirmed that the nomogram had the highest clinical utility.

CONCLUSIONS

The nodal-based radiomics model effectively predicts LNs treatment response in patients with LARC after nCRT, which could help personalize treatment plans and guide the implementation of the watch-and-wait approach in these patients.

Peritumoral and Intratumoral Texture Features Based on Multiparametric MRI and Multiple Machine Learning Methods to Preoperatively Evaluate the Pathological Outcomes of Pancreatic Cancer

BACKGROUND

Radiomics-based preoperative evaluation of lymph node metastasis (LNM) and histological grade (HG) might facilitate the decision-making for pancreatic cancer and further efforts are needed to develop effective models.

PURPOSE

To develop multiparametric MRI (MP-MRI)-based radiomics models to evaluate LNM and HG.

STUDY TYPE

Retrospective.

POPULATION

The pancreatic cancer patients from the main center (n = 126) were assigned to the training and validation sets at a 4:1 ratio. The patients from the other center (n = 40) served as external test sets.

FIELD STRENGTH/SEQUENCE

A 3.0 T and 1.5 T/T2-weighted imaging, diffusion-weighted imaging, and dynamic contrast enhancement T1-weighted imaging.

ASSESSMENT

A total of 10,686 peritumoral and intratumoral radiomics features were extracted which contained first-order, shape-based, and texture features. The following three-step method was applied to reduce the feature dimensionality: SelectKBest (a function from scikit-learn package), least absolute shrinkage and selection operator (LASSO), and recursive feature elimination based on random forest (RFE-RF). Six classifiers (random forest, logistic regression, support vector machine, K-nearest neighbor, decision tree, and XGBOOST) were trained and selected based on their performance to construct the clinical, radiomics, and combination models.

STATISTICAL TESTS

Delong's test was used to compare the models' performance. P value less than 0.05 was considered significant.

RESULTS

Twelve significant features for LNM and 11 features for HG were obtained. Random forest and logistic regression performed better than the other classifiers in evaluating LNM and HG, respectively, according to the surgical pathological results. The best performance was obtained with the models that combined peritumoral and intratumoral features with area under curve (AUC) values of 0.944 and 0.892 in the validation and external test sets for HG and 0.924 and 0.875 for LNM.

DATA CONCLUSION

Radiomics holds the potential to evaluate LNM and HG of pancreatic cancer. The combination of peritumoral and intratumoral features will make models more accurate.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

Stage 2.

Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review

Rectal cancer (RC) is one of the most common tumours worldwide in both males and females, with significant morbidity and mortality rates, and it accounts for approximately one-third of colorectal cancers (CRCs). Magnetic resonance imaging (MRI) has been demonstrated to be accurate in evaluating the tumour location and stage, mucin content, invasion depth, lymph node (LN) metastasis, extramural vascular invasion (EMVI), and involvement of the mesorectal fascia (MRF). However, these features alone remain insufficient to precisely guide treatment decisions. Therefore, new imaging biomarkers are necessary to define tumour characteristics for staging and restaging patients with RC. During the last decades, RC evaluation via MRI-based radiomics and artificial intelligence (AI) tools has been a research hotspot. The aim of this review was to summarise the achievement of MRI-based radiomics and AI for the evaluation of staging, response to therapy, genotyping, prediction of high-risk factors, and prognosis in the field of RC. Moreover, future challenges and limitations of these tools that need to be solved to favour the transition from academic research to the clinical setting will be discussed.

Multiparametric MRI-based radiomics for the prediction of microvascular invasion in hepatocellular carcinoma

BACKGROUND

Preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is essential in obtaining a successful surgical treatment, in decreasing recurrence, and in improving survival.

PURPOSE

To investigate the value of multiparametric magnetic resonance imaging (MRI)-based radiomics in the prediction of peritumoral MVI in HCC.

MATERIAL AND METHODS

A total of 102 patient with pathologically proven HCC after surgical resection from June 2014 to March 2018 were enrolled in this retrospective study. Histological analysis of resected specimens confirmed positive MVI in 48 patients and negative MVI in 54 patients. Radiomics features were extracted from four MRI sequences and selected with the least absolute shrinkage and selection operator (LASSO) regression and used to analyze the tumoral and peritumoral regions for MVI. Univariate logistic regression was employed to identify the most important clinical factors, which were integrated with the radiomics signature to develop a nomogram.

RESULTS

In total, 11 radiomics features were selected and used to build the radiomics signature. The serum level of alpha-fetoprotein was identified as the clinical factor with the highest predictive value. The developed nomogram achieved the highest AUC in predicting MVI status. The decision curve analysis confirmed the potential clinical utility of the proposed nomogram.

CONCLUSION

The multiparametric MRI-based radiomics nomogram is a promising tool for the preoperative diagnosis of peritumoral MVI in HCCs and helps determine the appropriate medical or surgical therapy.

Novel multiparametric MRI-based radiomics in preoperative prediction of perirectal fat invasion in rectal cancer

OBJECTIVES

To investigate the feasibility and efficacy of a nomogram that combines clinical and radiomic features of magnetic resonance imaging (MRI) for preoperative perirectal fat invasion (PFI) prediction in rectal cancer.

METHODS

This was a retrospective study. A total of 363 patients from two centers were included in the study. Patients in the first center were randomly divided into training cohort (n = 212) and internal validation cohort (n = 91) at the ratio of 7:3. Patients in the second center were allocated to the external validation cohort (n = 60). Among the training cohort, the numbers of patients who were PFI positive and PFI negative were 108 and 104, respectively. The radiomics features of preoperative T₂-weighted images, diffusion-weighted images and enhanced T₁-weighted images were extracted, and the total Radscore of each patient was obtained. We created Clinic model and Radscore model, respectively, according to clinical data or Radscore only. And that, we assembled the combined model using the clinical data and Radscore. We used DeLong's test, receiver operating characteristic, calibration and decision curve analysis to assess the models' performance.

RESULTS

The three models had good performance. Clinic model and Radscore model showed equivalent performance with AUCs of 0.85, 0.82 (accuracy of 81%, 81%) in the training cohort, AUCs of 0.78, 0.86 (accuracy of 74%, 84%) in the internal cohort, and 0.84, 0.84 (accuracy of 80%, 82%) in the external cohort without statistical difference (DeLong's test, p > 0.05). AUCs and accuracy of Combined model were 0.89 and 87%, 0.90 and 88%, and 0.90 and 88% in the three cohorts, respectively, which were higher than that of Clinic model and Radscore model, but only in the training cohort with a statistical difference (DeLong's test, p < 0.05). The calibration curves of the nomogram exhibited acceptable consistency, and the decision curve analysis indicated higher net benefit in clinical practice.

CONCLUSION

A nomogram combining clinical and radiomic features of MRI to compute the probability of PFI in rectal cancer was developed and validated. It has the potential to serve as a preoperative biomarker for predicting pathological PFI of rectal cancer.

MRI-Based Artificial Intelligence in Rectal Cancer

Rectal cancer (RC) accounts for approximately one-third of colorectal cancer (CRC), with death rates increasing in patients younger than 50 years old. Magnetic resonance imaging (MRI) is routinely performed for tumor evaluation. However, the semantic features from images alone remain insufficient to guide treatment decisions. Functional MRIs are useful for revealing microstructural and functional abnormalities and nevertheless have low or modest repeatability and reproducibility. Therefore, during the preoperative evaluation and follow-up treatment of patients with RC, novel noninvasive imaging markers are needed to describe tumor characteristics to guide treatment strategies and achieve individualized diagnosis and treatment. In recent years, the development of artificial intelligence (AI) has created new tools for RC evaluation based on MRI. In this review, we summarize the research progress of AI in the evaluation of staging, prediction of high-risk factors, genotyping, response to therapy, recurrence, metastasis, prognosis, and segmentation with RC. We further discuss the challenges of clinical application, including improvement in imaging, model performance, and the biological meaning of features, which may also be major development directions in the future. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer

By breaking the traditional medical image analysis framework, precision medicine–radiomics has attracted much attention in the past decade. The use of various mathematical algorithms offers radiomics the ability to extract vast amounts of detailed features from medical images for quantitative analysis and analyzes the confidential information related to the tumor in the image, which can establish valuable disease diagnosis and prognosis models to support personalized clinical decisions. This article summarizes the application of radiomics and dosiomics in radiation oncology. We focus on the application of radiomics in locally advanced rectal cancer and also summarize the latest research progress of dosiomics in radiation tumors to provide ideas for the treatment of future related diseases, especially ¹²⁵I CT-guided radioactive seed implant brachytherapy.

Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors

Objective

This study aims to investigate the value of machine learning models based on clinical-radiological features and multiphasic CT radiomics features in the differentiation of benign parotid tumors (BPTs) and malignant parotid tumors (MPTs).

Methods

This retrospective study included 312 patients (205 cases of BPTs and 107 cases of MPTs) who underwent multiphasic enhanced CT examinations, which were randomly divided into training (N = 218) and test (N = 94) sets. The radiomics features were extracted from the plain, arterial, and venous phases. The synthetic minority oversampling technique was used to balance minority class samples in the training set. Feature selection methods were done using the least absolute shrinkage and selection operator (LASSO), mutual information (MI), and recursive feature extraction (RFE). Two machine learning classifiers, support vector machine (SVM), and logistic regression (LR), were then combined in pairs with three feature selection methods to build different radiomics models. Meanwhile, the prediction performances of different radiomics models based on single phase (plain, arterial, and venous phase) and multiphase (three-phase combination) were compared to determine which model construction method and phase were more discriminative. In addition, clinical models based on clinical-radiological features and combined models integrating radiomics features and clinical-radiological features were established. The prediction performances of the different models were evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and the drawing of calibration curves.

Results

Among the 24 established radiomics models composed of four different phases, three feature selection methods, and two machine learning classifiers, the LASSO-SVM model based on a three-phase combination had the optimal prediction performance with AUC (0.936 [95% CI = 0.866, 0.976]), sensitivity (0.78), specificity (0.90), and accuracy (0.86) in the test set, and its prediction performance was significantly better than with the clinical model based on LR (AUC = 0.781, p = 0.012). In the test set, the combined model based on LR had a lower AUC than the optimal radiomics model (AUC = 0.933 vs. 0.936), but no statistically significant difference (p = 0.888).

Conclusion

Multiphasic CT-based radiomics analysis showed a machine learning model based on clinical-radiological features and radiomics features has the potential to provide a valuable tool for discriminating benign from malignant parotid tumors.

Differentiation of Cerebral Dissecting Aneurysm from Hemorrhagic Saccular Aneurysm by Machine-Learning Based on Vessel Wall MRI: A Multicenter Study

The differential diagnosis of a cerebral dissecting aneurysm (DA) and a hemorrhagic saccular aneurysm (SA) often depends on the intraoperative findings; thus, improved non-invasive imaging diagnosis before surgery is essential to distinguish between these two aneurysms, in order to provide the correct formulation of surgical procedure. We aimed to build a radiomic model based on high-resolution vessel wall magnetic resonance imaging (VW-MRI) and a machine-learning algorithm. In total, 851 radiomic features from 146 cases were analyzed retrospectively, and the ElasticNet algorithm was used to establish the radiomic model in a training set of 77 cases. A clinico-radiological model using clinical features and MRI features was also built. Then an integrated model was built by combining the radiomic model and clinico-radiological model. The area under the ROC curve (AUC) was used to quantify the performance of models. The models were evaluated using leave-one-out cross-validation in a training set, and further validated in an external test set of 69 cases. The diagnostic performance of experienced radiologists was also assessed for comparison. Eight features were used to establish the radiomic model, and the radiomic model performs better (AUC = 0.831) than the clinico-radiological model (AUC = 0.717), integrated model (AUC = 0.813), and even experienced radiologists (AUC = 0.801). Therefore, a radiomic model based on VW-MRI can reliably be used to distinguish DA and hemorrhagic SA, and, thus, be widely applied in clinical practice.

Discriminating rectal cancer grades using restriction spectrum imaging

PURPOSE

Restriction spectrum imaging (RSI) is a novel diffusion MRI model that separates water diffusion into several microscopic compartments. The restricted compartment correlating to the tumor cellularity is expected to be a potential indicator of rectal cancer aggressiveness. Our aim was to assess the ability of RSI model for rectal tumor grading.

METHODS

Fifty-eight patients with different rectal cancer grading confirmed by biopsy were involved in this study. DWI acquisitions were performed using single-shot echo-planar imaging (SS-EPI) with multi-b-values at 3 T. We applied a three-compartment RSI model, along with ADC model and diffusion kurtosis imaging (DKI) model, to DWI images of 58 patients. ROC and AUC were used to compare the performance of the three models in differentiating the low grade (G1 + G2) and high grade (G3). Mean ± standard deviation, ANOVA, ROC analysis, and correlation analysis were used in this study.

RESULTS

The volume fraction of restricted compartment C₁ from RSI was significantly correlated with grades (r = 0.403, P = 0.002). It showed significant difference between G1 and G3 (P = 0.008) and between G2 and G3 (P = 0.01). As for the low-grade and high-grade discrimination, significant difference was found in C₁ (P < 0.001). The AUC of C₁ for differentiation between low-grade and high-grade groups was 0.753 with a sensitivity of 72.0% and a specificity of 70.0%.

CONCLUSION

The three-compartment RSI model was able to discriminate the rectal cancer of low and high grades. The results outperform the traditional ADC model and DKI model in rectal cancer grading.

Multi-parametric MRI-based radiomics for the diagnosis of malignant soft-tissue tumor

PURPOSE

To develop and validate a multiparametric magnetic resonance imaging-based radiomics nomogram for differentiating malignant and benign soft-tissue tumors.

METHODS

A total of 91 patients with pathologically confirmed soft-tissue tumors were enrolled between January 2017 and October 2020. Forty-eight patients were consecutively enrolled between November 2020 and March 2022, as a time-independent cohort. All patients underwent contrast-enhanced T1-weighted and T2-weighted fat-suppression magnetic resonance scans at 3.0 T. Radiomics features were extracted and selected from the two modalities to develop the radiomics signature. Significant clinical/morphological characteristics were identified using a multivariate logistic regression analysis. The least absolute shrinkage and selection operator regression were applied to identify discriminative features. A clinical-radiomics nomogram was constructed based on clinical/morphological characteristics and radiomics features. Finally, the performance of the nomogram was validated using the receiver operating characteristic and decision curve analysis (DCA).

RESULTS

Six features were selected to establish the combined RS. Size, margin, and peritumoral edema were identified as the most important clinical and morphological factors, respectively. The radiomics signature outperformed the clinical model in terms of AUC and sensitivity. The nomogram integrating the combined RS, size, margin, and peritumoral edema achieved favorable predictive efficacy, generating AUCs of 0.954 (95% confidence interval [CI]: 0.907-1.000, Sen = 0.861, Spe = 0.917), 0.962 (95% CI: 0.901-1.000, Sen = 0.944, Spe = 0.923), and 0.935 (95% CI: 0.871-0.998, Sen = 0.815, Spe = 0.952) in the training (n = 60), validation (n = 31) and time-independent (n = 48) cohorts, respectively. The DCA curve indicated good clinical usefulness of the nomogram.

CONCLUSIONS

Our study demonstrated the clinical potential of multiparametric MRI-based radiomics in distinguishing malignant from benign soft-tissue tumors, which can be considered as a noninvasive tool for individual treatment management.

A radiomics-based nomogram for preoperative T staging prediction of rectal cancer

Purpose

To investigate the value of a radiomics-based nomogram in predicting preoperative T staging of rectal cancer.

Methods

A total of 268 eligible rectal cancer patients from August 2012 to December 2018 were enrolled and allocated into two datasets: training (n = 188) and validation datasets (n = 80). Another set of 32 patients from January 2019 to July 2019 was included in a prospective analysis. Pretreatment T2-weighted images were used to radiomics features extraction. Feature selection and radiomics score (Rad-score) construction were performed through a least absolute shrinkage and selection operator regression analysis. The nomogram, which included Rad-scores and clinical factors, was built using multivariate logistic regression. Discrimination, calibration, and clinical utility were used to evaluate the performance of the nomogram.

Results

The Rad-score containing nine selected features was significantly related to T staging. Patients who had locally advanced rectal cancer (LARC) generally had higher Rad-scores than patients with early-stage rectal cancer. The nomogram incorporated Rad-scores and carcinoembryonic antigen levels and showed good discrimination, with an area under the curve (AUC) of 0.882 (95% confidence interval [CI] 0.835–0.930) in the training dataset and 0.846 (95% CI 0.757–0.936) in the validation dataset. The calibration curves confirmed high goodness of fit, and the decision curve analysis revealed the clinical value. A prospective analysis demonstrated that the AUC of the nomogram to predict LARC was 0.859 (95% CI 0.730–0.987).

Conclusion

A radiomics-based nomogram is a novel method for predicting LARC and can provide support in clinical decision making.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00261-021-03137-1.

Radiomics and machine learning applications in rectal cancer: Current update and future perspectives

The high incidence of rectal cancer in both sexes makes it one of the most common tumors, with significant morbidity and mortality rates. To define the best treatment option and optimize patient outcome, several rectal cancer biological variables must be evaluated. Currently, medical imaging plays a crucial role in the characterization of this disease, and it often requires a multimodal approach. Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors. Computed tomography is widely adopted for the detection of distant metastases. However, conventional imaging has recognized limitations, and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation. There is a growing interest in artificial intelligence applications in medicine, and imaging is by no means an exception. The introduction of radiomics, which allows the extraction of quantitative features that reflect tumor heterogeneity, allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers. To manage such a huge amount of data, the use of machine learning algorithms has been proposed. Indeed, without prior explicit programming, they can be employed to build prediction models to support clinical decision making. In this review, current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented, with an imaging modality-based approach and a keen eye on unsolved issues. The results are promising, but the road ahead for translation in clinical practice is rather long.

Development and validation of a risk prediction score for patients with nasopharyngeal carcinoma

Background

We aimed to develop and validate a predictive model for the overall survival (OS) of patients with nasopharyngeal carcinoma (NPC).

Methods

Overall, 519 patients were retrospectively reviewed in this study. In addition, a random forest model was used to identify significant prognostic factors for OS among NPC patients. Then, calibration plot and concordance index (C-index) were utilized to evaluate the predictive accuracy of the nomogram model.

Results

We used a random forest model to select the three most important features, dNLR, HGB and EBV DNA, which were significantly associated with the OS of NPC patients. Furthermore, the C-index of our model for OS were 0.733 (95% CI 0.673 ~ 0.793) and 0.772 (95% CI 0.691 ~ 0.853) in the two cohorts, which was significantly higher than that of the TNM stage, treatment, and EBV DNA. Based on the model risk score, patients were divided into two groups, associated with low-risk and high-risk. Kaplan–Meier curves demonstrated that the two subgroups were significantly associated with OS in the primary cohort, as well as in the validation cohort. The nomogram for OS was established using the risk score, TNM stage and EBV DNA in the two cohorts. The nomogram achieved a higher C-index of 0.783 (95% CI 0.730 ~ 0.836) than that of the risk score model 0.733 (95% CI 0.673 ~ 0.793) in the primary cohort (P = 0.005).

Conclusions

The established risk score model and nomogram resulted in more accurate prognostic prediction for individual patient with NPC.

Performances of Whole Tumor Texture Analysis Based on MRI: Predicting Preoperative T Stage of Rectal Carcinomas

Objective

To determine whether there is a correlation between texture features extracted from high-resolution T2-weighted imaging (HR-T2WI) or apparent diffusion coefficient (ADC) maps and the preoperative T stage (stages T1–2 versus T3–4) in rectal carcinomas.

Materials and Methods

One hundred and fifty four patients with rectal carcinomas who underwent preoperative HR-T2WI and diffusion-weighted imaging were enrolled. Patients were divided into training (n = 89) and validation (n = 65) cohorts. 3D Slicer was used to segment the entire volume of interest for whole tumors based on HR-T2WI and ADC maps. The least absolute shrinkage and selection operator (LASSO) was performed to select feature. The significantly difference was tested by the independent sample t-test and Mann-Whitney U test. The support vector machine (SVM) model was used to develop classification models. The correlation between features and T stage was assessed by Spearman’s correlation analysis. Multivariate logistic regression analysis was performed to identify independent predictors of tumor invasion. The performance of classifiers was evaluated by the receiver operating characteristic (ROC) curves.

Results

The wavelet HHH NGTDM strength (R_S = -0.364, P < 0.001) from HR-T2WI was an independent predictor of stage T3–4 tumors. The shape maximum 2D diameter column (R_S = 0.431, P < 0.001), log σ = 5.0 mm 3D first-order maximum (R_S = 0.276, P = 0.009), and log σ = 5.0 mm 3D first-order interquartile range (R_S = -0.229, P = 0.032) from ADC maps were independent predictors. In training cohorts, the classification models from HR-T2WI, ADC maps and the combination of two achieved the area under the ROC curves (AUCs) of 0.877, 0.902 and 0.941, with the accuracy of 79.78%, 89.86% and 89.89%, respectively. In validation cohorts, the three models achieved AUCs of 0.845, 0.881 and 0.910, with the accuracy of 78.46%, 83.08% and 87.69%, respectively.

Conclusions

Texture analysis based on ADC maps shows more potential than HR-T2WI in identifying preoperative T stage in rectal carcinomas. The combined application of HR-T2WI and ADC maps may help to improve the accuracy of preoperative diagnosis of rectal cancer invasion.

Radiomics Based on T2-Weighted Imaging and Apparent Diffusion Coefficient Images for Preoperative Evaluation of Lymph Node Metastasis in Rectal Cancer Patients

Purpose

To develop and validate a radiomics nomogram based on T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) features for the preoperative prediction of lymph node (LN) metastasis in rectal cancer patients.

Materials and Methods

One hundred and sixty-two patients with rectal cancer confirmed by pathology were retrospectively analyzed, who underwent T2WI and DWI sequences. The data sets were divided into training (n = 97) and validation (n = 65) cohorts. For each case, a total of 2,752 radiomic features were extracted from T2WI, and ADC images derived from diffusion-weighted imaging. A two-sample t-test was used for prefiltering. The least absolute shrinkage selection operator method was used for feature selection. Three radiomics scores (rad-scores) (rad-score 1 for T2WI, rad-score 2 for ADC, and rad-score 3 for the combination of both) were calculated using the support vector machine classifier. Multivariable logistic regression analysis was then used to construct a radiomics nomogram combining rad-score 3 and independent risk factors. The performances of three rad-scores and the nomogram were evaluated using the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was used to assess the clinical usefulness of the radiomics nomogram.

Results

The AUCs of the rad-score 1 and rad-score 2 were 0.805, 0.749 and 0.828, 0.770 in the training and validation cohorts, respectively. The rad-score 3 achieved an AUC of 0.879 in the training cohort and an AUC of 0.822 in the validation cohort. The radiomics nomogram, incorporating the rad-score 3, age, and LN size, showed good discrimination with the AUC of 0.937 for the training cohort and 0.884 for the validation cohort. DCA confirmed that the radiomics nomogram had clinical utility.

Conclusions

The radiomics nomogram, incorporating rad-score based on features from the T2WI and ADC images, and clinical factors, has favorable predictive performance for preoperative prediction of LN metastasis in patients with rectal cancer.

Severity assessment of COVID-19 using CT image features and laboratory indices

The coronavirus disease 2019 (COVID-19) is now a global pandemic. Tens of millions of people have been confirmed with infection, and also more people are suspected. Chest computed tomography (CT) is recognized as an important tool for COVID-19 severity assessment. As the number of chest CT images increases rapidly, manual severity assessment becomes a labor-intensive task, delaying appropriate isolation and treatment. In this paper, a study of automatic severity assessment for COVID-19 is presented. Specifically, chest CT images of 118 patients (age 46.5 ± 16.5 years, 64 male and 54 female) with confirmed COVID-19 infection are used, from which 63 quantitative features and 110 radiomics features are derived. Besides the chest CT image features, 36 laboratory indices of each patient are also used, which can provide complementary information from a different view. A random forest (RF) model is trained to assess the severity (non-severe or severe) according to the chest CT image features and laboratory indices. Importance of each chest CT image feature and laboratory index, which reflects the correlation to the severity of COVID-19, is also calculated from the RF model. Using three-fold cross-validation, the RF model shows promising results: 0.910 (true positive ratio), 0.858 (true negative ratio) and 0.890 (accuracy), along with AUC of 0.98. Moreover, several chest CT image features and laboratory indices are found to be highly related to COVID-19 severity, which could be valuable for the clinical diagnosis of COVID-19.

Combining Radiomics and Blood Test Biomarkers to Predict the Response of Locally Advanced Rectal Cancer to Chemoradiation

BACKGROUND/AIM

A noninvasive method for predicting a patient's response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer would be useful because this would help determine the subsequent treatment strategy. Two types of noninvasive biomarkers have previously been studied, based on radiomics and based on blood test parameters. We hypothesized that a combination of both types would provide a better predictive power, and this has not previously been investigated.

PATIENTS AND METHODS

Data from 135 patients with locally advanced rectal cancer who underwent nCRT were retrospectively allocated into training and validation cohorts in a 2:1 ratio. Sixty-five radiomics features were extracted from tumors segmented on T2-weighted magnetic resonance images. An elastic net was applied to generate four models for discerning the patients with good responses to nCRT based on radiomics features (model R), blood biomarkers (model B), both (model RB), and a linear combination of models R and B (model R+B).

RESULTS

Among 65 radiomics features, 17 were selected as robust features for model development. The AUC values of model R, model B, model RB, and model R+B achieved 0.751, 0.627, 0.785, and 0.711 in the training cohort (n=90), and 0.705, 0.603, 0.679, and 0.705 in validation cohort (n=45), respectively. In the entire cohort, models RB and R+B demonstrated a significantly better performance than model B but not R. There was no correlation between the scores of models R and B (p=0.76). Radiomics features had a greater influence than blood biomarkers on models RB and R+B.

CONCLUSION

A non-redundancy between radiomics features and blood-based biomarkers was observed. Furthermore, radiomics features are more valuable in terms of predicting response to nCRT. The importance of combining non-invasive biomarkers in future investigations is highlighted.

Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study

Locally advanced rectal cancer (LARC) response to neoadjuvant chemoradiotherapy (nCRT) is very heterogeneous and up to 30% of patients are considered non-responders, presenting no tumor regression after nCRT. This study aimed to determine the ability of pre-treatment T2-weighted based radiomics features to predict LARC non-responders. A total of 67 LARC patients who underwent a pre-treatment MRI followed by nCRT and total mesorectal excision were assigned into training (n = 44) and validation (n = 23) groups. In both datasets, the patients were categorized according to the Ryan tumor regression grade (TRG) system into non-responders (TRG = 3) and responders (TRG 1 and 2). We extracted 960 radiomic features/patient from pre-treatment T2-weighted images. After a three-step feature selection process, including LASSO regression analysis, we built a radiomics score with seven radiomics features. This score was significantly higher among non-responders in both training and validation sets (p < 0.001 and p = 0.03) and it showed good predictive performance for LARC non-response, achieving an area under the curve (AUC) = 0.94 (95% CI: 0.82–0.99) in the training set and AUC = 0.80 (95% CI: 0.58–0.94) in the validation group. The multivariate analysis identified the radiomics score as an independent predictor for the tumor non-response (OR = 6.52, 95% CI: 1.87–22.72). Our results indicate that MRI radiomics features could be considered as potential imaging biomarkers for early prediction of LARC non-response to neoadjuvant treatment.