Magnetic Resonance Imaging Radiomics Research Articles

Prediction of lymphovascular invasion in invasive breast cancer based on clinical-MRI radiomics features

ObjectiveWe aim to develop a predictive model for lymphovascular invasion (LVI) in patients with invasive breast cancer (IBC), using magnetic resonance imaging (MRI)-based radiomics features.MethodsA total of 204 patients with IBC admitted to our hospital were included in this retrospective study. The data was split into training and validation sets at a 7:3 ratio. Feature normalization was conducted, followed by feature selection using ANOVA, correlation analysis, and LASSO in the training set. The final step involved building a logistic regression model. The LVI prediction models were established by single sequence image and combined different sequence images as follows: A: prediction model based on the optimal sequence in the 7-phase enhanced MRI scans; B: prediction model based on the optimal sequences in the sequences T1WI, T2WI, and DWI; and C: the combined model based on the optimal sequences selected from A and B. Subjects' work characteristic curves (ROC) and decision curves (DCA) were plotted to determine the extent to which they predicted LVI performance in the training and validation sets. Simultaneously, nomogram models were constructed by integrating radiomics features and independent risk factors. In addition, an additional 16 patients from the center between January and August 2024 were collected as the Nomogram external validation set. The ROC and DCA were used to evaluate the performance of the model.ResultsIn the enhanced images, Model A built based on the enhanced 2-phase achieved the best average AUC, with a validation set of 0.764. Model B built based on the T2WI had better results, with a validation set of 0.693. Model C built by combining enhanced 2-phase and T2WI sequences had a mean AUC of 0.705 in the validation set. In addition, the tumor size, whether the tumor boundary was clear or not, and whether there was a coelom in the tumor tissue had a statistically significant effect on the LVI of IBC, and a clinical-radiomics nomogram was established. DCAs as well as Nomogram also indicate that Model A has good clinical utility. The AUC of the nomogram in the training set, internal validation set, and external validation set were 0.703, 0.615, and 0.609, respectively. The DCA also showed that the radiomics nomogram combined with clinical factors had good predictive ability for LVI.ConclusionIn IBC, MRI radiomics can serve as a noninvasive predictor of LVI. The clinical-MRI radiomics model, as an efficient visual prognostic tool, shows promise in forecasting LVI. This highlights the significant potential of pre-radiomics prediction in enhancing treatment strategies.

Assessment of Y-90 Radioembolization Treatment Response for Hepatocellular Carcinoma Cases Using MRI Radiomics.

This study aimed to investigate the ability of radiomics features extracted from magnetic resonance imaging (MRI) images to differentiate between responders and non-responders for hepatocellular carcinoma (HCC) cases who received Y-90 transarterial radioembolization treatment. Thirty-six cases of HCC who underwent MRI scans after Y-90 radioembolization were included in this study. Tumors were segmented from MRI T2 images, and then 87 radiomic features were extracted through the LIFEx package software. Treatment response was determined 9 months after treatment through the modified response evaluation criteria in solid tumours (mRECIST). According to mRECIST, 28 cases were responders and 8 cases were non-responders. Two radiomics features, "Grey Level Size Zone Matrix (GLSZM)-Small Zone Emphasis" and "GLSZM-Normalized Zone Size Non-Uniformity", were the radiomics features that could predict treatment response with the area under curve (AUC)= 0.71, sensitivity= 0.93, and specificity= 0.62 for both features. Whereas the other 4 features (kurtosis, intensity histogram root mean square, neighbourhood gray-tone difference matrix strength, and GLSZM normalized grey level non-uniformity) have a relatively lower but acceptable discrimination ability range from AUC= 0.6 to 0.66. MRI radiomics analysis could be used to assess the treatment response for HCC cases treated with Y-90 radioembolization.

Prediction of feeding difficulties in neonates with hypoxic-ischemic encephalopathy using magnetic resonance imaging-derived radiomics features.

The mechanisms behind brain and spinal cord injuries in hypoxic-ischemic encephalopathy (HIE) and associated feeding difficulties are unclear, with previous magnetic resonance imaging (MRI) attempts yielding inconclusive results. We aim to evaluate an MRI radiomics model for predicting feeding difficulties in HIE infants. Additionally, we investigate changes in predictive capability after incorporating the duration of mechanical ventilation and the timing of MRI examination. Retrospective study with 151 HIE infants (January 2013 to December 2021), randomly divided into training and validation sets. Radiomics features extracted from basal ganglia-thalamus and brainstem in T1-weighted and T2-weighted MRI. Established single-modality, single-site, and multimodality/multisite models. Receiver operating characteristic analysis and area under the curve evaluated models. Decision curve analysis assessed changes in predictive capability. The combined radiomics model of the basal ganglia-thalamus and brainstem regions on the T2-weighted imaging demonstrated superior performance (area under the curve: 0.958 and 0.875 for training and validation, respectively). Combining scores with duration of mechanical ventilation and MRI examination time in a calibration plot model improved and stabilized performance, showing high fitting and clinical utility. Decision curve analysis favored the combined calibration plot model. The MRI-based radiomics model predicts feeding difficulties in HIE infants, with basal ganglia-thalamus and brainstem as relevant factors. The combined calibration plot model exhibits the highest clinical predictive efficacy.

Magnetic Resonance Imaging Radiomics Analysis of Noncystic Kidney Parenchyma to Differentiate among Mayo Imaging Classification Classes

Magnetic Resonance Imaging Radiomics Analysis of Noncystic Kidney Parenchyma to Differentiate among Mayo Imaging Classification Classes

Prediction of axillary lymph node metastasis using a magnetic resonance imaging radiomics model of invasive breast cancer primary tumor

BackgroundThis study investigated the clinical value of breast magnetic resonance imaging (MRI) radiomics for predicting axillary lymph node metastasis (ALNM) and to compare the discriminative abilities of different combinations of MRI sequences.MethodsThis study included 141 patients diagnosed with invasive breast cancer from two centers (center 1: n = 101, center 2: n = 40). Patients from center 1 were randomly divided into training set and test set 1. Patients from center 2 were assigned to the test set 2. All participants underwent preoperative MRI, and four distinct MRI sequences were obtained. The volume of interest (VOI) of the breast tumor was delineated on the dynamic contrast-enhanced (DCE) postcontrast phase 2 sequence, and the VOIs of other sequences were adjusted when required. Subsequently, radiomics features were extracted from the VOIs using an open-source package. Both single- and multisequence radiomics models were constructed using the logistic regression method in the training set. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and precision of the radiomics model for the test set 1 and test set 2 were calculated. Finally, the diagnostic performance of each model was compared with the diagnostic level of junior and senior radiologists.ResultsThe single-sequence ALNM classifier derived from DCE postcontrast phase 1 had the best performance for both test set 1 (AUC = 0.891) and test set 2 (AUC = 0.619). The best-performing multisequence ALNM classifiers for both test set 1 (AUC = 0.910) and test set 2 (AUC = 0.717) were generated from DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging single-sequence ALNM classifiers. Both had a higher diagnostic level than the junior and senior radiologists.ConclusionsThe combination of DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging radiomics features had the best performance in predicting ALNM from breast cancer. Our study presents a well-performing and noninvasive tool for ALNM prediction in patients with breast cancer.

Multi-modality MRI radiomics phenotypes in intermediate-high risk endometrial cancer: correlations with histopathology and prognosis.

This study aimed to identify the magnetic resonance imaging (MRI)-based radiomics phenotypes of intermediate-to-high-risk endometrial cancers (ECs), explore their association with histopathologic features, and compare their prognostic ability with the International Federation of Gynecology and Obstetrics (FIGO) stage. This study retrospectively recruited 355 patients with pathologically confirmed EC from 01/2016 to 06/2023. 166(46.8%) were classified as intermediate-to-high-risk ECs according to the European Society for Medical Oncology guidelines. Radiomics clustering analysis was performed on preoperative MRI to identify the radiomics phenotype of intermediate-to-high-risk ECs. The association between the radiomics phenotypes and the clinicopathologic information was explored, and the added value in predicting the recurrence was also evaluated using concordance index (C-index). Of the included 166 patients (average age 56.83 ± 9.25years), 23 were recurrent patients. The corresponding tumors in various clusters were assigned to phenotypes 1 and 2. Larger tumor diameter (P < .01), cervical mucosa invasion [30(36.15%) vs 15(18.07%), P = .01], deep myometrial infiltration [51(61.45%) vs 31(37.35%), P = .00], and histologic subtype [17(20.48%) vs 5(6.02%), P = .01] were associated with subtype 1. The risk of recurrence (P = .01) was higher in phenotype 1, and the FIGO stage could further differentiate higher recurrence risk in phenotype 1 (P < .01). The C-index was 0.66 for the radiomics phenotype model, 0.69 for the FIGO stage model, and 0.72 for the combined model. MRI-based radiomics consensus clustering enabled the identification of associations between radiomics features and histopathologic features in intermediate-to-high-risk EC. The FIGO stage could further elevate the prediction ability of recurrence risk.

Magnetic Resonance-Guided Cancer Therapy Radiomics and Machine Learning Models for Response Prediction.

Magnetic resonance imaging (MRI) is known for its accurate soft tissue delineation of tumors and normal tissues. This development has significantly impacted the imaging and treatment of cancers. Radiomics is the process of extracting high-dimensional features from medical images. Several studies have shown that these extracted features may be used to build machine-learning models for the prediction of treatment outcomes of cancer patients. Various feature selection techniques and machine models interrogate the relevant radiomics features for predicting cancer treatment outcomes. This study aims to provide an overview of MRI radiomics features used in predicting clinical treatment outcomes with machine learning techniques. The review includes examples from different disease sites. It will also discuss the impact of magnetic field strength, sample size, and other characteristics on outcome prediction performance.

251P The changes of multiparametric magnetic resonance imaging radiomics and Ki67 after two cycles of neoadjuvant therapy builds an early prediction model of pathological complete response in breast cancer

251P The changes of multiparametric magnetic resonance imaging radiomics and Ki67 after two cycles of neoadjuvant therapy builds an early prediction model of pathological complete response in breast cancer

A novel nomogram for predicting overall survival in patients with tongue squamous cell carcinoma using clinical features and MRI radiomics data: a pilot study

ObjectiveTongue squamous cell carcinoma (TSCC) accounts for 43.4% of oral cancers in China and has a poor prognosis. This study aimed to explore whether radiomics features extracted from preoperative magnetic resonance imaging (MRI) could predict overall survival (OS) in patients with TSCC.MethodsThe clinical imaging data of 232 patients with pathologically confirmed TSCC at Xiangyang No. 1 People’s Hospital were retrospectively analyzed from February 2010 to October 2022. Based on 2–10 years of follow-up, patients were categorized into two groups: control (healthy survival, n = 148) and research (adverse events: recurrence or metastasis-related death, n = 84). A training and a test set were established using a 7:3 ratio and a time node. Radiomics features were extracted from axial T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging (DWI) sequences. The corresponding radiomics scores were generated using the least absolute shrinkage and selection operator algorithm. Kaplan–Meier and multivariate Cox regression analyses were used to screen for independent factors affecting adverse events in patients with TSCC using clinical and pathological results. A novel nomogram was established to predict the probability of adverse events and OS in patients with TSCC.ResultsThe incidence of adverse events within 2–10 years after surgery was 36.21%. Kaplan–Meier analysis revealed that hot pot consumption, betel nut chewing, platelet–lymphocyte ratio, drug use, neutrophil–lymphocyte ratio, Radscore, and other factors impacted TSCC survival. Multivariate Cox regression analysis revealed that the clinical stage (P < 0.001), hot pot consumption (P < 0.001), Radscore 1 (P = 0.01), and Radscore 2 (P < 0.001) were independent factors affecting TSCC-OS. The same result was validated by the XGBoost algorithm. The nomogram based on the aforementioned factors exhibited good discrimination (C-index 0.86/0.81) and calibration (P > 0.05) in the training and test sets, accurately predicting the risk of adverse events and survival.ConclusionThe nomogram constructed using clinical data and MRI radiomics parameters may accurately predict TSCC-OS noninvasively, thereby assisting clinicians in promptly modifying treatment strategies to improve patient prognosis.

Delta magnetic resonance imaging radiomics features‑based nomogram predicts long‑term efficacy after induction chemotherapy in locoregionally advanced nasopharyngeal carcinoma

PurposeTo establish and validate a delta-radiomics-based model for predicting progression-free survival (PFS) in patients with locoregionally advanced nasopharyngeal carcinoma (LA-NPC) following induction chemotherapy (IC). Methods and MaterialsA total of 250 LA-NPC patients (training cohort: n = 145; validation cohort: n = 105) were enrolled. Radiomic features were extracted from MRI scans taken before and after IC, and changes in these features were calculated. Following feature selection, a delta-radiomics signature was constructed using LASSO-Cox regression analysis. A prognostic nomogram incorporating independent clinical indicators and the delta-radiomics signature was developed and assessed for calibration and discrimination. Risk stratification by the nomogram was evaluated using Kaplan-Meier methods. ResultsThe delta-radiomics signature, consisting of 12 features, was independently associated with prognosis. The nomogram, integrating the delta-radiomics signature and clinical factors demonstrated excellent calibration and discrimination. The model achieved a Harrell’s concordance index (C-index) of 0.848 in the training cohort and 0.820 in the validation cohort. Risk stratification identified two groups with significantly different PFS rates. The three-year PFS for high-risk patients who received concurrent chemoradiotherapy (CCRT) or radiotherapy plus adjuvant chemotherapy (RT+AC) after IC was significantly higher than for those who received RT alone, reaching statistical significance. In contrast, for low-risk patients, the three-year PFS after IC was slightly higher for those who received CCRT or RT+AC compared to those who received RT alone; however, this difference did not reach statistical significance. ConclusionsOur delta MRI-based radiomics model could be useful for predicting PFS and may guide subsequent treatment decisions after IC in LA-NPC.

The prognostic role of MRI-based radiomics in tongue carcinoma: a multicentric validation study

PurposeRadiomics is an emerging field that utilizes quantitative features extracted from medical images to predict clinically meaningful outcomes. Validating findings is crucial to assess radiomics applicability. We aimed to validate previously published magnetic resonance imaging (MRI) radiomics models to predict oncological outcomes in oral tongue squamous cell carcinoma (OTSCC).Materials and methodsRetrospective multicentric study on OTSCC surgically treated from 2010 to 2019. All patients performed preoperative MRI, including contrast-enhanced T1-weighted (CE-T1), diffusion-weighted sequences and apparent diffusion coefficient map. We evaluated overall survival (OS), locoregional recurrence-free survival (LRRFS), cause-specific mortality (CSM). We elaborated different models based on clinical and radiomic data. C-indexes assessed the prediction accuracy of the models.ResultsWe collected 112 consecutive independent patients from three Italian Institutions to validate the previously published MRI radiomic models based on 79 different patients. The C-indexes for the hybrid clinical-radiomic models in the validation cohort were lower than those in the training cohort but remained > 0.5 in most cases. CE-T1 sequence provided the best fit to the models: the C-indexes obtained were 0.61, 0.59, 0.64 (pretreatment model) and 0.65, 0.69, 0.70 (posttreatment model) for OS, LRRFS and CSM, respectively.ConclusionOur clinical-radiomic models retain a potential to predict OS, LRRFS and CSM in heterogeneous cohorts across different centers. These findings encourage further research, aimed at overcoming current limitations, due to the variability of imaging acquisition, processing and tumor volume delineation.

Early prediction of neoadjuvant chemotherapy efficacy for mass breast cancer based on dynamic contrast‐enhanced magnetic resonance imaging radiomics

AbstractRadiomics uses automated algorithms to extract high‐order features from images, which can contribute to clinical decisions such as therapeutic efficacy evaluation. We assessed the value of a dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI)‐based radiomics model for predicting pathological complete response (pCR) after a second cycle of neoadjuvant chemotherapy (NAC) in patients with mass breast cancer. We retrospectively analyzed data from 149 patients with mass breast cancer who underwent NAC between January 2017 and December 2022. Using DCE‐MRI, before NAC and after a second cycle of NAC, the least absolute shrinkage and selection operator and logistic regression (LR) algorithms were applied for feature selection and radiomics modeling. We found significant differences in two clinical imaging features (molecular subtypes, background parenchymal enhancement changes) and two radiomics features. Clinical and radiomics features were employed to build clinical, radiomics, and combined models to predict pCR. The LR model that combined clinical and radiomics features had an area under the curve of 0.811, higher than that for the imaging or radiomics model. Our findings suggest that a combined model based on imaging and radiomics features can improve early prediction of NAC efficacy for patients with mass breast cancer.

Magnetic resonance imaging radiomics-based prediction of severe inflammatory response in locally advanced rectal cancer patients after neoadjuvant radiochemotherapy.

To predict severe inflammatory response after neoadjuvant radiochemotherapy in locally advanced rectal cancer (RC) patients using magnetic resonance imaging (MRI) radiomics models. This retrospective study included patients who underwent radical surgery for RC cancer after neoadjuvant radiochemotherapy between July 2017 and December 2019 at XXX Hospital. MRI radiomics features were extracted from T2WI images before (pre-nRCT-RF) and after (post-nRCT-RF) neoadjuvant radiochemotherapy, and the variation of radiomics features before and after neoadjuvant radiochemotherapy (delta-RF) were calculated. Eight, eight, and five most relevant features were identified for pre-nRCT-RF, post-nRCT-RF, and delta-RF, respectively. Eighty-six patients were included and randomized 3:1 to the training and test set (n = 65 and n = 21, respectively). The prediction model based on delta-RF had areas under the curve (AUCs) of 0.80 and 0.85 in the training and test set, respectively. A higher rate of difficult operations was observed in patients with severe inflammation (65.5% vs. 42.9%, P = 0.045). The prediction model based on MRI delta-RF may be a useful tool for predicting severe inflammatory response after neoadjuvant radiochemotherapy in locally advanced RC patients.

Preoperative prediction of histopathological grading in patients with chondrosarcoma using MRI-based radiomics with semantic features

BackgroundDistinguishing high-grade from low-grade chondrosarcoma is extremely vital not only for guiding the development of personalized surgical treatment but also for predicting the prognosis of patients. We aimed to establish and validate a magnetic resonance imaging (MRI)-based nomogram for predicting preoperative grading in patients with chondrosarcoma.MethodsApproximately 114 patients (60 and 54 cases with high-grade and low-grade chondrosarcoma, respectively) were recruited for this retrospective study. All patients were treated via surgery and histopathologically proven, and they were randomly divided into training (n = 80) and validation (n = 34) sets at a ratio of 7:3. Next, radiomics features were extracted from two sequences using the least absolute shrinkage and selection operator (LASSO) algorithms. The rad-scores were calculated and then subjected to logistic regression to develop a radiomics model. A nomogram combining independent predictive semantic features with radiomic by using multivariate logistic regression was established. The performance of each model was assessed by the receiver operating characteristic (ROC) curve analysis and the area under the curve, while clinical efficacy was evaluated via decision curve analysis (DCA).ResultsUltimately, six optimal radiomics signatures were extracted from T1-weighted imaging (T1WI) and T2-weighted imaging with fat suppression (T2WI-FS) sequences to develop the radiomics model. Tumour cartilage abundance, which emerged as an independent predictor, was significantly related to chondrosarcoma grading (p < 0.05). The AUC values of the radiomics model were 0.85 (95% CI, 0.76 to 0.95) in the training sets, and the corresponding AUC values in the validation sets were 0.82 (95% CI, 0.65 to 0.98), which were far superior to the clinical model AUC values of 0.68 (95% CI, 0.58 to 0.79) in the training sets and 0.72 (95% CI, 0.57 to 0.87) in the validation sets. The nomogram demonstrated good performance in the preoperative distinction of chondrosarcoma. The DCA analysis revealed that the nomogram model had a markedly higher clinical usefulness in predicting chondrosarcoma grading preoperatively than either the rad-score or clinical model alone.ConclusionThe nomogram based on MRI radiomics combined with optimal independent factors had better performance for the preoperative differentiation between low-grade and high-grade chondrosarcoma and has potential as a noninvasive preoperative tool for personalizing clinical plans.

Non-invasively identifying candidates of active surveillance for prostate cancer using magnetic resonance imaging radiomics

Active surveillance (AS) is the primary strategy for managing patients with low or favorable-intermediate risk prostate cancer (PCa). Identifying patients who may benefit from AS relies on unpleasant prostate biopsies, which entail the risk of bleeding and infection. In the current study, we aimed to develop a radiomics model based on prostate magnetic resonance images to identify AS candidates non-invasively. A total of 956 PCa patients with complete biopsy reports from six hospitals were included in the current multicenter retrospective study. The National Comprehensive Cancer Network (NCCN) guidelines were used as reference standards to determine the AS candidacy. To discriminate between AS and non-AS candidates, five radiomics models (i.e., eXtreme Gradient Boosting (XGBoost) AS classifier (XGB-AS), logistic regression (LR) AS classifier, random forest (RF) AS classifier, adaptive boosting (AdaBoost) AS classifier, and decision tree (DT) AS classifier) were developed and externally validated using a three-fold cross-center validation based on five classifiers: XGBoost, LR, RF, AdaBoost, and DT. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were calculated to evaluate the performance of these models. XGB-AS exhibited an average of AUC of 0.803, ACC of 0.693, SEN of 0.668, and SPE of 0.841, showing a better comprehensive performance than those of the other included radiomic models. Additionally, the XGB-AS model also presented a promising performance for identifying AS candidates from the intermediate-risk cases and the ambiguous cases with diagnostic discordance between the NCCN guidelines and the Prostate Imaging-Reporting and Data System assessment. These results suggest that the XGB-AS model has the potential to help identify patients who are suitable for AS and allow non-invasive monitoring of patients on AS, thereby reducing the number of annual biopsies and the associated risks of bleeding and infection.

A head-to-head comparison of computed tomography- and magnetic resonance imaging-based radiomics in assessing pediatric peripheral neuroblastic tumor cell behavior.

To compare the performance of radiomics from contrast-enhanced computed tomography (CECT) and non-contrast magnetic resonance imaging (MRI) in assessing cellular behavior in pediatric peripheral neuroblastic tumors (PNTs). A retrospective analysis of 81 PNT patients who underwent venous phase CECT, T1-weighted imaging (T1WI), and T2-weighted imaging (T2WI) scans was conducted. The patients were classified into neuroblastoma and ganglioneuroblastoma/ganglioneuroma based on their pathological subtypes. Additionally, they were categorized into favorable histology and unfavorable histology according to the International Neuroblastoma Pathology Classification (INPC). Tumor regions of interest were segmented on CECT, axial T1WI, and axial T2WI images, and radiomics models were developed based on the selected radiomics features. Following five-fold cross-validation, the performance of the radiomics models derived from CECT and MRI was compared using the area under the receiver operating characteristic curve (AUC) and accuracy. For discriminating pathological subtypes, the AUC for CECT radiomics models ranged from 0.765 to 0.870, with an accuracy range of 0.728 to 0.815. In contrast, the AUC for MRI radiomics models ranged from 0.549 to 0.748, with an accuracy range of 0.531 to 0.778. Regarding the discrimination of INPC subgroups, the AUC for CECT radiomics models ranged from 0.503 to 0.759, with an accuracy range of 0.432 to 0.741. Meanwhile, the AUC for MRI radiomics models ranged from 0.512 to 0.739, with an accuracy range of 0.605 to 0.815. CECT radiomics outperforms non-contrast MRI radiomics in evaluating pathological subtypes. When assessing INPC subgroups, CECT radiomics demonstrates comparability with non-contrast MRI radiomics.

MRI Radiomics in Imaging of Focal Hepatic Lesions: A Narrative Review.

Magnetic resonance imaging (MRI) is generally used to identify, describe, and evaluate treatment responses for focal hepatic lesions.However, the diagnosis and differentiation of such lesions require considerable input from radiologists. In order to reduce these difficulties, radiomics is an artificial intelligence (AI)-based quantitative method that employs the extraction of image features to reliably detect and differentiate focal hepatic lesions. MRI radiomics is a novel technique for the characterization of focal hepatic lesions. It can aid in preoperative evaluation, treatment approach, and forecast microvascular invasion. Although many studies have illustrated its efficiency there are certain limitations such as the absence of a large diverse dataset, comparison with other AI models, integration with histopathological findings, clinical utility, and feasibility.

Prediction of cognitive decline in Parkinson's disease based on MRI radiomics and clinical features: A multicenter study.

To develop and validate a multimodal combinatorial model based on whole-brain magnetic resonance imaging (MRI) radiomic features for predicting cognitive decline in patients with Parkinson's disease (PD). This study included a total of 222 PD patients with normal baseline cognition, of whom 68 had cognitive impairment during a 4-year follow-up period. All patients underwent MRI scans, and radiomic features were extracted from the whole-brain MRI images of the training set, and dimensionality reduction was performed to construct a radiomics model. Subsequently, Screening predictive factors for cognitive decline from clinical features and then combining those with a radiomics model to construct a multimodal combinatorial model for predicting cognitive decline in PD patients. Evaluate the performance of the comprehensive model using the receiver-operating characteristic curve, confusion matrix, F1 score, and survival curve. In addition, the quantitative characteristics of diffusion tensor imaging (DTI) from corpus callosum were selected from 52 PD patients to further validate the clinical efficacy of the model. The multimodal combinatorial model has good classification performance, with areas under the curve of 0.842, 0.829, and 0.860 in the training, test, and validation sets, respectively. Significant differences were observed in the number of cognitive decline PD patients and corpus callosum-related DTI parameters between the low-risk and high-risk groups distinguished by the model (p < 0.05). The survival curve analysis showed a statistically significant difference in the progression time of mild cognitive impairment between the low-risk and the high-risk groups. The building of a multimodal combinatorial model based on radiomic features from MRI can predict cognitive decline in PD patients, thus providing adaptive strategies for clinical practice.

Preoperative Classification of Peripheral Nerve Sheath Tumors on MRI Using Radiomics.

Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive soft-tissue tumors prevalent in neurofibromatosis type 1 (NF1) patients, posing a significant risk of metastasis and recurrence. Current magnetic resonance imaging (MRI) imaging lacks decisiveness in distinguishing benign peripheral nerve sheath tumors (BPNSTs) and MPNSTs, necessitating invasive biopsies. This study aims to develop a radiomics model using quantitative imaging features and machine learning to distinguish MPNSTs from BPNSTs. Clinical data and MRIs from MPNST and BPNST patients (2000-2019) were collected at a tertiary sarcoma referral center. Lesions were manually and semi-automatically segmented on MRI scans, and radiomics features were extracted using the Workflow for Optimal Radiomics Classification (WORC) algorithm, employing automated machine learning. The evaluation was conducted using a 100× random-split cross-validation. A total of 35 MPNSTs and 74 BPNSTs were included. The T1-weighted (T1w) MRI radiomics model outperformed others with an area under the curve (AUC) of 0.71. The incorporation of additional MRI scans did not enhance performance. Combining T1w MRI with clinical features achieved an AUC of 0.74. Experienced radiologists achieved AUCs of 0.75 and 0.66, respectively. Radiomics based on T1w MRI scans and clinical features show some ability to distinguish MPNSTs from BPNSTs, potentially aiding in the management of these tumors.

MRI radiomics nomogram integrating postoperative adjuvant treatments in recurrence risk prediction for patients with early-stage cervical cancer

Background and purposeAdjuvant treatments are valuable to decrease the recurrence rate and improve survival for early-stage cervical cancer patients (ESCC), Therefore, recurrence risk evaluation is critical for the choice of postoperative treatment. A magnetic resonance imaging (MRI) based radiomics nomogram integrating postoperative adjuvant treatments was constructed and validated externally to improve the recurrence risk prediction for ESCC. Material and methods212 ESCC patients underwent surgery and adjuvant treatments from three centers were enrolled and divided into the training, internal validation, and external validation cohorts. Their clinical data, pretreatment T2-weighted images (T2WI) were retrieved and analyzed. Radiomics models were constructed using machine learning methods with features extracted and screen from sagittal and axial T2WI. A nomogram for recurrence prediction was build and evaluated using multivariable logistic regression analysis integrating radiomic signature and adjuvant treatments. ResultsA total of 8 radiomic features were screened out of 1020 extracted features. The extreme gradient boosting (XGboost) model based on MRI radiomic features performed best in recurrence prediction with an area under curve (AUC) of 0.833, 0.822 in the internal and external validation cohorts, respectively. The nomogram integrating radiomic signature and clinical factors achieved an AUC of 0.806, 0.718 in the internal and external validation cohorts, respectively, for recurrence risk prediction for ESCC. ConclusionIn this study, the nomogram integrating T2WI radiomic signature and clinical factors is valuable to predict the recurrence risk, thereby allowing timely planning for effective treatments for ESCC with high risk of recurrence.