Magnetic Resonance Imaging Radiomics Research Articles

18F-FDG PET/CT Parameters Enhance MRI Radiomics for Predicting Human Papilloma Virus Status in Oropharyngeal Squamous Cell Carcinoma.

Predicting human papillomavirus (HPV) status is critical in oropharyngeal squamous cell carcinoma (OPSCC) radiomics. In this study, we developed a model for HPV status prediction using magnetic resonance imaging (MRI) radiomics and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) parameters in patients with OPSCC. Patients with OPSCC who underwent 18F-FDG PET/CT and contrast-enhanced MRI before treatment between January 2012 and February 2020 were enrolled. Training and test sets (3:2) were randomly selected. 18F-FDG PET/CT parameters and MRI radiomics feature were extracted. We developed three light-gradient boosting machine prediction models using the training set: Model 1, MRI radiomics features; Model 2, 18F-FDG PET/CT parameters; and Model 3, combination of MRI radiomics features and 18F-FDG PET/CT parameters. Area under the receiver operating characteristic curve (AUROC) values were used to analyze the performance of the models in predicting HPV status in the test set. A total of 126 patients (118 male and 8 female; mean age: 60 years) were included. Of these, 103 patients (81.7%) were HPV-positive, and 23 patients (18.3%) were HPV-negative. AUROC values in the test set were 0.762 [95% confidence interval (CI), 0.564-0.959], 0.638 (95% CI, 0.404-0.871), and 0.823 (95% CI, 0.668-0.978) for Models 1, 2, and 3, respectively. The net reclassification improvement of Model 3, compared with that of Model 1, in the test set was 0.119. When combined with an MRI radiomics model, 18F-FDG PET/CT exhibits incremental value in predicting HPV status in patients with OPSCC.

Predictive value of magnetic resonance imaging radiomics-based machine learning for disease progression in patients with high-grade glioma

Accurately predicting the prognosis of patients with high-grade glioma (HGG) is potentially important for treatment. However, the predictive value of images of various magnetic resonance imaging (MRI) sequences for prognosis at different time points is unknown. We established predictive machine learning models of HGG disease progression and recurrence using MRI radiomics and explored the factors influencing prediction accuracy. Radiomics features were extracted from T1-weighted (T1WI), contrast-enhanced T1-weighted (CE-T1WI), T2-weighted (T2WI), and fluid-attenuated inversion recovery (FLAIR) images (postoperative radiotherapy planning MRI images) obtained from 162 patients with HGG. The Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature selection. Machine learning models were used to build prediction models to estimate disease progression or recurrence. The influence of different MRI sequences, regions of interest (ROIs), and prediction time points was also explored. The receiver operating characteristic (ROC) curve was used to evaluate the discriminative performance of each model, and the DeLong test was employed to compare the ROC curves. Radiomics features from T2WI and FLAIR demonstrated greater predictive value for disease progression compared with T1WI or CE-TIWI. The best predictive models, with areas under the ROC curves (AUCs) of 0.70, 0.68, 0.78, 0.78, and 0.78 for predicting disease progression at the 6th, 9th, 12th, 15th, and 18th month after radiotherapy, respectively, were obtained by combining clinical features with gross tumor volume (GTV) and clinical target volume (CTV) features extracted from T2WI and FLAIR. Structural MRI obtained before radiotherapy can be used to predict the disease progression or posttreatment recurrence of HGG. When using MRI radiomics to predict long-term outcomes as opposed to short-term outcomes, better predictive results may be obtained.

Dual-center validation of using magnetic resonance imaging radiomics to predict stereotactic radiosurgery outcomes.

MRI radiomic features and machine learning have been used to predict brain metastasis (BM) stereotactic radiosurgery (SRS) outcomes. Previous studies used only single-center datasets, representing a significant barrier to clinical translation and further research. This study, therefore, presents the first dual-center validation of these techniques. SRS datasets were acquired from 2 centers (n = 123 BMs and n = 117 BMs). Each dataset contained 8 clinical features, 107 pretreatment T1w contrast-enhanced MRI radiomic features, and post-SRS BM progression endpoints determined from follow-up MRI. Random decision forest models were used with clinical and/or radiomic features to predict progression. 250 bootstrap repetitions were used for single-center experiments. Training a model with one center's dataset and testing it with the other center's dataset required using a set of features important for outcome prediction at both centers, and achieved area under the receiver operating characteristic curve (AUC) values up to 0.70. A model training methodology developed using the first center's dataset was locked and externally validated with the second center's dataset, achieving a bootstrap-corrected AUC of 0.80. Lastly, models trained on pooled data from both centers offered balanced accuracy across centers with an overall bootstrap-corrected AUC of 0.78. Using the presented validated methodology, radiomic models trained at a single center can be used externally, though they must utilize features important across all centers. These models' accuracies are inferior to those of models trained using each individual center's data. Pooling data across centers shows accurate and balanced performance, though further validation is required.

Exploring Hypoxia in Prostate Cancer With T2-weighted Magnetic Resonance Imaging Radiomics and Pimonidazole Scoring.

Radiomics involves high throughput extraction of mineable precise quantitative imaging features that serve as non-invasive prognostic or predictive biomarkers. High levels of hypoxia are associated with a poorer prognosis in prostate cancer and limit radiation therapy efficacy. Most patients with prostate cancer undergo magnetic resonance imaging (MRI) as a part of their diagnostics, and T2 imaging is the most utilised imaging method. The aim of this study was to determine whether hypoxia in prostate tumors could be identified using a radiomics model extracted from T2-weighted MR images. Eighty eight intermediate or high-risk prostate cancer patients were evaluated. Prior to radical prostatectomy, all patients received pimonidazole (PIMO). PIMO hypoxic scores were assigned in whole-mount sections from prostatectomy specimens by an experienced pathologist who was blinded to MRI. The region of interest used for radiomics analysis included the prostatic index tumor. Radiomics extraction yielded 165 features using a special evaluation version of RadiomiX [RadiomiX Research Toolbox version 20180831 (OncoRadiomics SA, Liège, Belgium)] for non-clinical use. Multivariable logistic regression with Elastic Net regularization was utilised using 10 times repeated 10-fold cross-validation to select the best model hyperparameters, optimizing for area under the receiver operating characteristic curve (AUC). The average (out of sample) performance based on the repeated cross validation using the ONESE model yielded an AUC of 0.60±0.2. Shape-based features were the most prominent in the model. The development of a radiomics hypoxia model using T2 weighted MR images, standard in the staging of prostate cancer, is possible.

Predicting hepatocellular carcinoma early recurrence after ablation based on magnetic resonance imaging radiomics nomogram

The aim of this study is to investigate a model for predicting the early recurrence of hepatocellular carcinoma (HCC) after ablation. A total of 181 patients with HCC after ablation (train group was 119 cases; validation group was 62 cases) were enrolled. The cases of early recurrence in the set of train and validation were 63 and 31, respectively. Radiomics features were extracted from the enhanced magnetic resonance imaging scanning, including pre-contrast injection, arterial phase, late arterial phase, portal venous phase, and delayed phase. The least absolute shrinkage and selection operator cox proportional hazards regression after univariate and multivariate analysis was used to screen radiomics features and build integrated models. The nomograms predicting recurrence and survival of patients of HCC after ablation were established based on the clinical, imaging, and radiomics features. The area under the curve (AUC) of the receiver operating characteristic curve and C-index for the train and validation group was used to evaluate model efficacy. Four radiomics features were selected out of 34 texture features to formulate the rad-score. Multivariate analyses suggested that the rad-score, number of lesions, integrity of the capsule, pathological type, and alpha-fetoprotein were independent influencing factors. The AUC of predicting early recurrence at 1, 2, and 3 years in the train group was 0.79 (95% CI: 0.72-0.88), 0.72 (95% CI: 0.63-0.82), and 0.71 (95% CI: 0.61-0.83), respectively. The AUC of predicting early recurrence at 1, 2, and 3 years in the validation group was 0.72 (95% CI: 0.58-0.84), 0.61 (95% CI: 0.45-0.78) and 0.64 (95% CI: 0.40-0.87). The model for early recurrence of HCC after ablation based on the clinical, imaging, and radiomics features presented good predictive performance. This may facilitate the early treatment of patients.

Preoperative surgical risk assessment of meningiomas: a narrative review based on MRI radiomics.

Meningiomas are one of the most common intracranial primary central nervous system tumors. Regardless of the pathological grading and histological subtypes, maximum safe resection is the recommended treatment option for meningiomas. However, considering tumor heterogeneity, surgical treatment options and prognosis often vary greatly among meningiomas. Therefore, an accurate preoperative surgical risk assessment of meningiomas is of great clinical importance as it helps develop surgical treatment strategies and improve patient prognosis. In recent years, an increasing number of studies have proved that magnetic resonance imaging (MRI) radiomics has wide application values in the diagnostic, identification, and prognostic evaluations of brain tumors. The vital importance of MRI radiomics in the surgical risk assessment of meningiomas must be apprehended and emphasized in clinical practice. This narrative review summarizes the current research status of MRI radiomics in the preoperative surgical risk assessment of meningiomas, focusing on the applications of MRI radiomics in preoperative pathological grading, assessment of surrounding tissue invasion, and evaluation of tumor consistency. We further analyze the prospects of MRI radiomics in the preoperative assessment of meningiomas angiogenesis and adhesion with surrounding tissues, while pointing out the current challenges of MRI radiomics research.

Machine learning-based multiparametric magnetic resonance imaging radiomics model for distinguishing central neurocytoma from glioma of lateral ventricle.

To develop a machine learning-based radiomics model based on multiparametric magnetic resonance imaging (MRI) for preoperative discrimination between central neurocytomas (CNs) and gliomas of lateral ventricles. A total of 132 patients from two medical centers were enrolled in this retrospective study. Patients from the first medical center were divided into a training cohort (n = 74) and an internal validation cohort (n = 30). Patients from the second medical center were used as the external validation cohort (n = 28). Features were extracted from contrast-enhanced T1-weighted and T2-weighted images. A support vector machine was used for radiomics model investigation. Performance was evaluated using the sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). The model's performance was also compared with those of three radiologists. The radiomics model achieved an AUC of 0.986 in the training cohort, 0.933 in the internal validation cohort, and 0.903 in the external validation cohort. In the three cohorts, the AUC values were 0.657, 0.786, and 0.708 for radiologist 1; 0.838, 0.799, and 0.790 for radiologist 2; and 0.827, 0.871, and 0.862 for radiologist 3. When assisted by the radiomics model, two radiologists improved their performance in the training cohort (p < 0.05) but not in the internal or external validation cohorts. The machine learning radiomics model based on multiparametric MRI showed better performance for distinguishing CNs from lateral ventricular gliomas than did experienced radiologists, and it showed the potential to improve radiologist performance. • The machine learning radiomics model shows excellent performance in distinguishing CNs from gliomas. • The radiomics model outweighs two experienced radiologists (area under the receiver operating characteristic curve, 0.90 vs 0.79 and 0.86, respectively). • The radiomics model has the potential to enhance radiologist performance.

Development and validation of a combined model based on dual-sequence MRI radiomics for predicting the efficacy of high-intensity focused ultrasound ablation for hysteromyoma

Objectives To determine the value of dual-sequence magnetic resonance imaging (MRI)-based radiomics in predicting the efficacy of high-intensity focused ultrasound (HIFU) ablation for hysteromyoma. Methods A total of 142 patients with 172 hysteromyomas (95 hysteromyomas from the sufficient ablation group, and 77 hysteromyomas from the insufficient ablation group) were enrolled in the study. The clinical–radiological model was constructed with independent clinical–radiological risk factors, the radiomics model was constructed based on the optimal radiomics features of hysteromyoma from dual sequences, and the two groups of features were incorporated to construct the combined model. A fivefold cross validation procedure was adopted to validate these models. A nomogram was constructed, applying the combined model in the training cohort. The models were assessed with receiver operating characteristic (ROC) curves and integrated discrimination improvement (IDI). An independent test cohort comprising 40 patients was used to evaluate the performance of the optimal model. Results Among the three models, the average areas under the ROC curves (AUC) of the radiomics model and combined model were 0.803 (95% confidence interval (CI): 0.726–0.881) and 0.841 (95% CI: 0.772–0.909), which were better than the clinical–radiological model in the training cohort. The IDI showed that the combined model had the best prediction accuracy. The combined model also showed good discrimination in both the validation cohort (AUC = 0.834) and the independent test cohort (AUC = 0.801). Conclusion The combined model based on the dual-sequence MRI radiomics is the most promising tool from our study to assist clinicians in predicting HIFU ablation efficacy.

MRI Radiomics for Predicting Survival in Patients with Locally Advanced Hypopharyngeal Cancer Treated with Concurrent Chemoradiotherapy.

A reliable prognostic stratification of patients with locally advanced hypopharyngeal cancer who had been treated with concurrent chemoradiotherapy (CCRT) is crucial for informing tailored management strategies. The purpose of this retrospective study was to develop robust and objective magnetic resonance imaging (MRI) radiomics-based models for predicting overall survival (OS) and progression-free survival (PFS) in this patient population. The study participants included 198 patients (median age: 52.25 years (interquartile range = 46.88-59.53 years); 95.96% men) who were randomly divided into a training cohort (n = 132) and a testing cohort (n = 66). Radiomic parameters were extracted from post-contrast T1-weighted MR images. Radiomic features for model construction were selected from the training cohort using least absolute shrinkage and selection operator-Cox regression models. Prognostic performances were assessed by calculating the integrated area under the receiver operating characteristic curve (iAUC). The ability of radiomic models to predict OS (iAUC = 0.580, 95% confidence interval (CI): 0.558-0.591) and PFS (iAUC = 0.625, 95% CI = 0.600-0.633) was validated in the testing cohort. The combination of radiomic signatures with traditional clinical parameters outperformed clinical variables alone in the prediction of survival outcomes (observed iAUC increments = 0.279 [95% CI = 0.225-0.334] and 0.293 [95% CI = 0.232-0.351] for OS and PFS, respectively). In summary, MRI radiomics has value for predicting survival outcomes in patients with hypopharyngeal cancer treated with CCRT, especially when combined with clinical prognostic variables.

Comparison of MRI and CT-based radiomics for preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma.

The preoperative prediction of lymph node metastasis (LNM) in pancreatic ductal adenocarcinoma (PDAC) is essential in prognosis and treatment strategy formulation. To compare the performance of computed tomography (CT) and magnetic resonance imaging (MRI) radiomics models for the preoperative prediction of LNM in PDAC. In total, 160 consecutive patients with PDAC were retrospectively included, who were divided into the training and validation sets (ratio of 8:2). Two radiologists evaluated LNM basing on morphological abnormalities. Radiomics features were extracted from T2-weighted imaging, T1-weighted imaging, and multiphase contrast enhanced MRI and multiphase CT, respectively. Overall, 1184 radiomics features were extracted from each volume of interest drawn. Only features with an intraclass correlation coefficient ≥0.75 were included. Three sequential feature selection steps-variance threshold, variance thresholding and least absolute shrinkage selection operator-were repeated 20 times with fivefold cross-validation in the training set. Two radiomics models based on multiphase CT and multiparametric MRI were built with the five most frequent features. Model performance was evaluated using the area under the curve (AUC) values. Multiparametric MRI radiomics model achieved improved AUCs (0.791 and 0.786 in the training and validation sets, respectively) than that of the CT radiomics model (0.672 and 0.655 in the training and validation sets, respectively) and of the radiologists' assessment (0.600-0.613 and 0.560-0.587 in the training and validation sets, respectively). Multiparametric MRI radiomics model may serve as a potential tool for preoperatively evaluating LNM in PDAC and had superior predictive performance to multiphase CT-based model and radiologists' assessment.

Using ensemble learning and genetic algorithm on magnetic resonance imaging radiomics to classify molecular subtypes of breast cancer

AbstractBreast cancer (BRCA) is one of the most frequent malignant tumors with the highest incidence of cancer and the second most common oncologic cause of death in women. BRCA can be classified into different molecular subtypes, such as basal‐like, represented by triple‐negative BRCA (estrogen receptor [ER] negative, progesterone receptor [PR] negative, and human epidermal growth factor receptor 2 [HER‐2] negative). This study aims to determine whether radiomics features extracted from magnetic resonance imaging (MRI) could be used to distinguish various BRCA molecular subtypes. This study retrospectively collected a dataset of 922 BRCA patients with MRIs and experimental genomic profiles. A genetic algorithm is then employed to select the optimal MRI features for each subproblem. Subsequently, stacking ensemble learning is implemented to learn these features and generate the prediction outcomes. Our model showed a significant performance of 0.700, 0.732, and 0.642 (area under the curve; AUC) in predicting ER, PR, and HER‐2 statuses. For multiclassification of Luminal A, Luminal B, HER2, and TNBC, the AUCs reached 0.672, 0.624, 0.639, and 0.669, respectively. Our model is superior in most subtypes compared to the state‐of‐the‐art predictors on the same dataset. In conclusion, genetic algorithm and ensemble learning can be suitable for BRCA subtype classification with high performance.

Assessment of Thigh MRI Radiomics and Clinical Characteristics for Assisting in Discrimination of Juvenile Dermatomyositis.

Magnetic resonance imaging (MRI) is an important non-invasive examination in the early diagnosis of juvenile dermatomyositis (JDM). We aimed to evaluate the feasibility of radiomics to establish a quantitative analysis of MRI images. Radiomics and machine learning were used to retrospectively analyze MRI T2 fat suppression sequences and relevant clinical data. The model associated with radiomics features was established using a cohort of patients who underwent thigh MRI at the children's hospital from June 2014 to September 2021. In total, 75 patients with JDM and 75 control children were included in the training cohort (n = 102) and validation cohort (n = 48). The independent factors including lower muscle strength (OR, 0.75; 95% CI, 0.59-0.90), higher creatine kinase (CK) level (OR, 1.65; 95% CI, 1.20-2.38), and higher radiomics score (OR, 2.30; 95% CI, 1.63-3.62) were associated with a clinical diagnosis of JDM. The combined model achieved good discrimination performance compared the radiomics score model under linear discriminant analyses in the training cohort (AUC, 0.949; 95% CI, 0.912-0.986 vs. AUC, 0.912; 95% CI, 0.858-0.967; p = 0.02) and in the validation cohort (AUC, 0.945; 95% CI, 0.878-1 vs. AUC, 0.905; 95% CI, 0.812-0.998; p = 0.03). The combined model showed the diagnostic value was not weaker than the biopsy (AUC, 0.950; 95% CI, 0.919-0.981, n = 150 vs. AUC, 0.952; 95% CI, 0.889-1, n = 72; p = 0.95) and electromyogram (EMG) (AUC, 0.950; 95% CI, 0.919-0.981 vs. AUC, 0.900; 95% CI, 0.852-0.948; p = 0.10) among all the patients. The combination of radiomics features extracted from the MRI and non-invasive clinical characteristics obtained a pronounced discriminative performance to assist in discriminating JDM.

Prediction of therapeutic response of unresectable hepatocellular carcinoma to hepatic arterial infusion chemotherapy based on pretherapeutic MRI radiomics and Albumin-Bilirubin score

PurposeTo construct and validate a combined nomogram model based on magnetic resonance imaging (MRI) radiomics and Albumin-Bilirubin (ALBI) score to predict therapeutic response in unresectable hepatocellular carcinoma (HCC) patients treated with hepatic arterial infusion chemotherapy (HAIC).MethodsThe retrospective study was conducted on 112 unresectable HCC patients who underwent pretherapeutic MRI examinations. Patients were randomly divided into training (n = 79) and validation cohorts (n = 33). A total of 396 radiomics features were extracted from the volume of interest of the primary lesion by the Artificial Kit software. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify optimal radiomic features. After feature selection, three models, including the clinical, radiomics, and combined models, were developed to predict the non-response of unresectable HCC to HAIC treatment. The performance of these models was evaluated by the receiver operating characteristic curve. According to the most efficient model, a nomogram was established, and the performance of which was also assessed by calibration curve and decision curve analysis. Kaplan–Meier curve and log-rank test were performed to evaluate the Progression-free survival (PFS).ResultsUsing the LASSO regression, we ultimately selected three radiomics features from T2-weighted images to construct the radiomics score (Radscore). Only the ALBI score was an independent factor associated with non-response in the clinical model (P = 0.033). The combined model, which included the ALBI score and Radscore, achieved better performance in the prediction of non-response, with an AUC of 0.79 (95% CI 0.68–0.90) and 0.75 (95% CI 0.58–0.92) in the training and validation cohorts, respectively. The nomogram based on the combined model also had good discrimination and calibration (P = 0.519 for the training cohort and P = 0.389 for the validation cohort). The Kaplan–Meier analysis also demonstrate that the high-score patients had significantly shorter PFS than the low-score patients (P = 0.031) in the combined model, with median PFS 6.0 vs 9.0 months.ConclusionThe nomogram based on the combined model consisting of MRI radiomics and ALBI score could be used as a biomarker to predict the therapeutic response of unresectable HCC after HAIC.

6P Deep learning radiomics supports perioperative treatment decisions and is associated with LncRNAs in breast cancer: A multicenter study

Several studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. This study aims to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological underpinning.

MRI and CT Radiomics Features to Predict Overall Survival of Locally Advanced Esophageal Cancer after Definite Chemoradiotherapy

<h3>Purpose/Objective(s)</h3> We establish prediction models to predict 2-year overall survival and stratify patients with different risks based on the radiomics features extracted from machine learning method using magnetic resonance imaging (MRI) and computed tomography (CT) before definite chemoradiotherapy (dCRT) in locally advanced esophageal cancer. <h3>Materials/Methods</h3> A total of 192 patients receiving the definite chemoradiotherapy were enrolled. The inclusion criteria were as follows 1) ≥18 years old; 2) Clinical stages T1-4, N0-1, M0-1B (AJCC version 6, M1b is limited to lymph node metastasis in the supraclavicular or celiac trunk area); 3) Radiation dose EQD2≥40Gy. 4)Minimum follow-up time is 2 years. We extracted 547 radiomics features from MRI and CT images respectively. The least absolute shrinkage and selection operator (LASSO) for COX algorithm was used to obtain features highly correlated with survival outcomes. Based on MRI, CT and the hybrid image data, three prediction models were built. In each hold-out experiment, patients were randomized into the training and validation cohorts at a ratio of 7:3. The predictive performance was evaluated in the training cohort and verified in the validation cohort using the concordance index (C-index). <h3>Results</h3> The median age of the patients was 63 years, and 78% were males. The clinical stages were stage ⅡA in 7 patients (3.6%), ⅡB in 10 patients (5.2%), Ⅲ in 131 patients (68.2%), Ⅳa in 16 patients (8.3%) and Ⅳb in 28 patients (14.6%). The median survival time was 20.9 months and the median follow-up time was 28.3 months. The predictive performances of MRI-based model were tested in the training sets and validation sets with the C-index of 0.725 (95%CI:0.617-0.834) and 0.603 (95%CI:0.399-0.808). For the prediction of 2-year OS, the AUC values were 0.822 for the training sets and 0.578 for the validation sets. We also created a CT radiomics-based model and a similar performance as MRI radiomics-based model was observed with an average C-index of 0.679 (95%CI:0.565-0.794) in the training sets and 0.629 (95%CI:0.417-0.840) in the validation sets. The hybrid model combining MRI and CT radiomics features were furtherly investigated, and the best performances were shown among three models with an average C-index of 0.721(95%CI:0.621-0.833) in the training cohorts and 0.689 (95%CI:0.497-0.881) in the validation cohorts. It showed significant differences (p<0.0001) of 2-year OS in the high-risk and low-risk groups divided by the best cutoff value of the receiver operator characteristic (ROC) curve in the hybrid radiomics-based model. The median survival of high-risk and low-risk groups were 11.3 VS 19.5 months in training cohorts and 13.3 VS 30.0 months in validation cohorts, and 2-year OS of high-risk and low-risk was 17.1% VS 69.2% in training groups compared to 26.2% VS 48.4% in validation groups. <h3>Conclusion</h3> The hybrid radiomics-based model has the best performances of predicting 2-year overall survival and can differentiate the high-risk and low-risk patients.

Leveraging Serial MRI Radiomics and Machine Learning to Predict Risk of Radiation Necrosis in Patients with Brain Metastases Managed with Stereotactic Radiation and Immunotherapy

<h3>Purpose/Objective(s)</h3> Radiation necrosis (RN) is the primary dose-limiting toxicity of stereotactic radiotherapy (SRT) for brain metastases (BMs), especially in patients also receiving immunotherapy (IO). We investigated the utility of serial magnetic resonance imaging (MRI) radiomic features to develop predictive quantitative imaging biomarkers of brain metastases response to SRT and IO. <h3>Materials/Methods</h3> Using an IRB-approved single-institution database, we retrospectively identified adult patients with brain metastases managed with SRT and IO between 2006 and 2021. We collected per-lesion outcomes measures of RN (radiographic and/or symptomatic), progressive disease (PD), or neither (NA). Routine pre- and post-SRT contrast-enhanced T1-weighted MRIs were pushed to MIM Software (Beachwood, OH), where individual BMs were manually contoured. A total of 1061 radiomics features were extracted per lesion using a digital gene expression panel. They were selected from the following categories: shape, intensity, neighborhood intensity difference (NID), grey-level co-occurrence matrix (GLCM), and grey-level run-length matrix (GLRLM). Feature reduction followed using Spearman's correlation coefficient (0.3 cut-off). Features with non-significantly different mean ranks pre- and post-SRT (p<0.01) per Wilcoxon signed-rank test were also excluded. Simple then multiple nominal logistic regressions (SLR and MLR) with Bonferroni correction were used to model the risk of RN, PD, or NA based on pre- and post-SRT, and delta-radiomic ([post-pre]/pre) features. 95% CI of ROC AUC was obtained by 10,000 bootstrapping. Statistical analysis was performed using software. <h3>Results</h3> Our dataset encompassed 301 BMs from 92 patients with NSCLC, melanoma, and renal cell carcinoma, all treated with brain SRT and IO. 74 (24.6%), 75 (24.9%), and 152 developed RN, PD, or NA, respectively. The final radiomic feature set included 39 features that significantly changed post-SRT (p<0.01). MLR resulted in a 3-radiomic feature model that predicted risk of RN (ROC AUC 0.71). The model included post-SRT BM surface area and texture (GLCM 3D Homogeneity) and pre-RT BM roundness. Interestingly, another 5-radiomic feature MLR model predicted RN and PD (ROC AUC 0.71) based on pre-SRT GLCM 3D texture feature, 3 post-SRT shape features (surface area, mean breadth, and roundness), in addition to delta-volume. <h3>Conclusion</h3> Our analysis showed that static and serial MR radiomic features could potentially predict BMs response to SRT and IO. Model external validation, combination with clinical variables, and integration of other MRI sequences are warranted before the large-scale adoption of this novel diagnostic approach.

The accuracy of breast MRI radiomic methodologies in predicting pathological complete response to neoadjuvant chemotherapy: A systematic review and network meta-analysis

BackgroundAchieving pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) improves survival outcomes for breast cancer patients. Currently, conventional histopathological biomarkers predicting such responses are inconsistent. Studies investigating radiomic texture analysis from breast magnetic resonance imaging (MRI) to predict pCR have varied radiomic protocols introducing heterogeneity between results. Thus, the efficacy of radiomic profiles compared to conventional strategies to predict pCR are inconclusive. PurposeComparing the predictive accuracy of different breast MRI radiomic protocols to identify the optimal strategy in predicting pCR to NAC. Material and methodsA systematic review and network meta-analysis was performed according to PRISMA guidelines. Four databases were searched up to October 4th, 2021. Nine predictive strategies were compared, including conventional biomarker parameters, MRI radiomic analysis conducted before, during, or after NAC, combination strategies and nomographic methodology. Results14 studies included radiomic data from 2,722 breast cancers, of which 994 were used in validation cohorts. All MRI derived radiomic features improved predictive accuracy when compared to biomarkers, except for pre-NAC MRI radiomics (odds ratio [OR]: 0.00; 95 % CI: −0.07–0.08). During-NAC and post-NAC MRI improved predictive accuracy compared to Pre-NAC MRI (OR: 0.14, 95 % CI: 0.02–0.26) and (OR: 0.26, 95 % CI: 0.07–0.45) respectively. Combining multiple MRIs did not improve predictive performance compared to Mid- or Post-NAC MRIs individually. ConclusionRadiomic analysis of breast MRIs improve identification of patients likely to achieve a pCR to NAC. Post-NAC MRI are the most accurate imaging method to extrapolate radiomic data to predict pCR.

Radiomics based on magnetic resonance imaging for preoperative prediction of lymph node metastasis in head and neck cancer: Machine learning study.

In this study, we use machine learning techniques to develop an efficient preoperative magnetic resonance imaging (MRI) radiomics approach for evaluation of cervical lymph node (CLN) status. After collecting all patients' MRI images, we used CLN radiomic features, the apparent diffusion coefficients (ADC) from diffusion-weighted imaging (DWI), and lymph node short diameter of the CLN to build MRI model to predict the status of the CLN. One hundred and twenty cases met inclusion criteria. The MRI model including the radiomic features, ADC, and lymph node size of the CLN achieved better performance for CLN status prediction with the area under the receiver operating characteristic (ROC) curve (AUC) of 0.83. The multiomic signature of MRI radiomics, ADC, and lymph node size of CLNs has high predictive value for the status of CLNs. This model has provided scientific value to the surgeon regarding cervical lymph nodes before surgery.

Generating Counterfactual Explanations For Causal Inference in Breast Cancer Treatment Response.

Imaging phenotypes extracted via radiomics of magnetic resonance imaging has shown great potential at predicting the treatment response in breast cancer patients after administering neoadjuvant systemic therapy (NST). Existing machine learning models are, however, limited in providing an expert-level interpretation of these models, particularly interpretability towards generating causal inference. Causal relationships between imaging phenotypes, clinical information, molecular features, and the treatment response may be useful in guiding the treatment strategies, management plans, and gaining acceptance in medical communities. In this work, we leverage the concept of counterfactual explanations to extract causal relationships between various imaging phenotypes, clinical information, molecular features, and the treatment response after NST. We implement the methodology on a publicly available breast cancer dataset and demonstrate the causal relationships generated from counterfactual explanations. We also compare and contrast our results with traditional explanations, such as LIME and Shapley.

MRI-based radiomics nomogram for the preoperative prediction of deep myometrial invasion of FIGO stage I endometrial carcinoma.

Endometrial carcinoma (EC) is one of the most common gynecological malignancies with an increasing incidence, and an accurate preoperative diagnosis of deep myometrial invasion (DMI) is crucial for personalized treatment. To determine the predictive value of a magnetic resonance imaging (MRI)-based radiomics nomogram for the presence of DMI in the International Federation of Gynecology and Obstetrics (FIGO) stage I EC. We retrospectively collected 163 patients with pathologically confirmed stage I EC from two centers and divided all samples into a training group (Center 1) and a validation group (Center 2). Clinical and routine imaging indicators were analyzed by logistical regression to construct a conventional diagnostic model (M1). Radiomics features extracted from the axial T2-weighted and axial contrast-enhanced T1-weighted (CE-T1W) images were treated with the intraclass correlation coefficient, Mann-Whitney U test, least absolute shrinkage and selection operator, and logistic regression analysis with Akaike information criterion to build a combined radiomics signature (M2). A nomogram (M3) was constructed by M1 and M2. Calibration and decision curves were drawn to evaluate the nomogram in the training and validation cohorts. The diagnostic performance of each indicator and model was evaluated by the area under the receiver operating characteristic curve (AUC). The four most significant radiomics features were finally selected from the CE-T1W MRI. For the diagnosis of DMI, the AUCT /AUCV of M1 was 0.798/0.738, the AUCT /AUCV of M2 was 0.880/0.852, and the AUCT /AUCV of M3 was 0.936/0.871 in the training and validation groups, respectively. The calibration curves showed that M3 was in good agreement with the ideal values. The decision curve analysis suggested potential clinical application values of the nomogram. A nomogram based on MRI radiomics and clinical imaging indicators can improve the diagnosis of DMI in patients with FIGO stage I EC.