Multiparametric Magnetic Resonance Imaging Data Research Articles

Toward Precision Diagnosis: Machine Learning in Identifying Malignant Orbital Tumors With Multiparametric 3 T MRI.

Orbital tumors present a diagnostic challenge due to their varied locations and histopathological differences. Although recent advancements in imaging have improved diagnosis, classification remains a challenge. The integration of artificial intelligence in radiology and ophthalmology has demonstrated promising outcomes. This study aimed to evaluate the performance of machine learning models in accurately distinguishing malignant orbital tumors from benign ones using multiparametric 3 T magnetic resonance imaging (MRI) data. In this single-center prospective study, patients with orbital masses underwent presurgery 3 T MRI scans between December 2015 and May 2021. The MRI protocol comprised multiparametric imaging including dynamic contrast-enhanced (DCE), diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), as well as morphological imaging acquisitions. A repeated nested cross-validation strategy using random forest classifiers was used for model training and evaluation, considering 8 combinations of explanatory features. Shapley additive explanations (SHAP) values were used to assess feature contributions, and the model performance was evaluated using multiple metrics. One hundred thirteen patients were analyzed (57/113 [50.4%] were women; average age was 51.5 ± 17.5 years, range: 19-88 years). Among the 8 combinations of explanatory features assessed, the performance on predicting malignancy when using the most comprehensive model, which is the most exhaustive one incorporating all 46 explanatory features-including morphology, DWI, DCE, and IVIM, achieved an area under the curve of 0.9 [0.73-0.99]. When using the streamlined "10-feature signature" model, performance reached an area under the curve of 0.88 [0.71-0.99]. Random forest feature importance graphs measured by the mean of SHAP values pinpointed the 10 most impactful features, which comprised 3 quantitative IVIM features, 4 quantitative DCE features, 1 quantitative DWI feature, 1 qualitative DWI feature, and age. Our findings demonstrate that a machine learning approach, integrating multiparametric MRI data such as DCE, DWI, IVIM, and morphological imaging, offers high-performing models for differentiating malignant from benign orbital tumors. The streamlined 10-feature signature, with a performance close to the comprehensive model, may be more suitable for clinical application.

Magnetic resonance imaging as a noninvasive adjunct to conventional assessment of functional differences between kidneys in vivo and during ex vivo normothermic machine perfusion

Normothermic machine perfusion (NMP) is increasingly considered for pretransplant kidney quality assessment. However, fundamental questions about differences between in vivo and ex vivo renal function, as well as the impact of ischemic injury on ex vivo physiology, remain unanswered. This study utilized magnetic resonance imaging (MRI), alongside conventional parameters to explore differences between in vivo and ex vivo renal function and the impact of warm ischemia on a kidney’s behavior ex vivo. Renal MRI scans and samples were obtained from living pigs (n = 30) in vivo. Next, kidney pairs were procured and exposed to minimal, or 75 minutes of warm ischemia, followed by 6 hours of hypothermic machine perfusion. Both kidneys simultaneously underwent 6-hour ex vivo perfusion in MRI-compatible NMP circuits to obtain multiparametric MRI data. Ischemically injured ex vivo kidneys showed a significantly altered regional blood flow distribution compared to in vivo and minimally damaged organs. Both ex vivo groups showed diffusion restriction relative to in vivo. Our findings underscore the differences between in vivo and ex vivo MRI-based renal characteristics. Therefore, when assessing organ viability during NMP, it should be considered to incorporate parameters beyond the conventional functional markers that are common in vivo.

Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities.

Current literature emphasizes surgical complexities and customized resection for managing insular gliomas; however, radiogenomic investigations into prognostic radiomic traits remain limited. We aimed to develop and validate a radiomic model using multiparametric magnetic resonance imaging (MRI) for prognostic prediction and to reveal the underlying biological mechanisms. Radiomic features from preoperative MRI were utilized to develop and validate a radiomic risk signature (RRS) for insular gliomas, validated through paired MRI and RNA-seq data (N = 39), to identify core pathways underlying the RRS and individual prognostic radiomic features. An 18-feature-based RRS was established for overall survival (OS) prediction. Gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) were used to identify intersectional pathways. In total, 364 patients with insular gliomas (training set, N = 295; validation set, N = 69) were enrolled. RRS was significantly associated with insular glioma OS (log-rank p = 0.00058; HR = 3.595, 95% CI:1.636-7.898) in the validation set. The radiomic-pathological-clinical model (R-P-CM) displayed enhanced reliability and accuracy in prognostic prediction. The radiogenomic analysis revealed 322 intersectional pathways through GSEA and WGCNA fusion; 13 prognostic radiomic features were significantly correlated with these intersectional pathways. The RRS demonstrated independent predictive value for insular glioma prognosis compared with established clinical and pathological profiles. The biological basis for prognostic radiomic indicators includes immune, proliferative, migratory, metabolic, and cellular biological function-related pathways.

The Possibility of Improving the Diagnosis of Clinically Significant Prostate Cancer Based on 3D-Modeling Fccording to the Results of MRI

Introduction: In oncourology it is important not only to detect prostate cancer, it is necessary to detect a tumor with a potential for death. Multiparametric magnetic resonance imaging (mpMRI) before prostate biopsy is necessary to improve the detection of clinically significant prostate cancer. Purpose: To assess the feasibility of using targeted prostate biopsy based on the developed original approach of cognitive biopsy, involving the use of three-dimensional models based on MRI data. Material and methods: From 2017 to 2021, a systemic transrectal biopsy of the prostate gland was performed in 126 patients at the NMRC of Oncology, supplemented by a cognitive MRI targeted biopsy. As a control group, the study included 115 patients with traditional transrectal biopsy performed at the NMRC Oncology in 2017-2020. Before the biopsy, all patients underwent MRI. The obtained MRI data of the main group processed at the workstation, after which models of the prostate gland, the alleged tumor process, were built according to the methodology developed by us. Results: After performing a transrectal prostate biopsy on the basis of 3D models, histological results were compared with the MRI data performed on all patients before the biopsy. Among 126 patients of the main group, the changes in MRI using the PI-RADS v2.1 system distributed as follows: in 48 patients, the PI-RADS score was 3 points, in 32 — 4 points, in 46 — 5 points. Among 115 patients of the control group, the changes in MRI using the PI-RADS 2.1 system distributed as follows: in 44 patients, the PI-RADS score was 3 points, in 26 — 4 points, in 45 — 5 points. Among patients with 3 points on the PI-RADS scale who underwent biopsy using the cognitive FUSION method, prostate cancer was diagnosed in 7 patients (14.5 %), in 19 patients (59.4 %) with 4 points on PI-RADS and in 38 patients (82.6 %) with a value of PI-RADS 5. Clinically significant prostate cancer was detected in 4 patients (8.3 %) with 3 PI-RADS scores. Among patients with PI-RADS 4 — clinically significant prostate cancer found in 11 (34.4 %). In patients with PI-RADS 5, clinically significant prostate cancer detected in 33, which was 71.7 %. With the traditional biopsy technique, out of 115 invasive procedures, prostate cancer was detected in 41 patients (35.6 %), clinically significant prostate cancer was detected in 22, which was 19.1 %. Among 44 patients of the control group with 3 points on the PI-RADS scale who underwent a traditional biopsy, prostate cancer was diagnosed in 6 patients (13.6 %), in 12 patients (59.4 %) with 4 points on PI-RADS and in 23 patients (51.1 %) with a PI-RADS value of 5. Clinically significant prostate cancer detected in 2 patients (4.5 %) with 3 PI-RADS scores. Among patients with PI-RADS 4 — clinically significant prostate cancer was found in 5 (19.2 %). In patients of the control group with PI-RADS 5, clinically significant prostate cancer detected in 15 cases, which was 33.3 %. Conclusions: Transrectal prostate biopsy using 3D modeling based on multiparametric magnetic resonance imaging (MRI) with the marking of foci according to PI-RADS v2.1 makes it possible to effectively identify areas with a high probability of clinically significant cancer. The use of 3D models based on multiparametric MRI makes it possible to reasonably plan a targeted biopsy scheme and improve the diagnosis of clinically significant prostate cancer.

Four-quadrant vector mapping of hybrid multidimensional MRI data for the diagnosis of prostate cancer.

The interpretation of prostate multiparametric magnetic resonance imaging (MRI) is subjective in nature, and there is large inter-observer variability among radiologists and up to 30% of clinically significant cancers are missed. This has motivated the development of new MRI techniques and sequences, especially quantitative approaches to improve prostate cancer diagnosis. Using hybrid multidimensional MRI, apparent diffusion coefficient (ADC) and T2 have been shown to change as a function of echo time (TE) and b-values, and that this dependence is different for cancer and benign tissue, which can be exploited for prostate cancer diagnosis. The purpose of this study is to investigate whether four-quadrant vector mapping of hybrid multidimensional MRI (HM-MRI) data can be used to diagnose prostate cancer (PCa) and determine cancer aggressiveness. Twenty-one patients with confirmed PCa underwent preoperative MRI prior to radical prostatectomy. Axial HM-MRI were acquired with all combinations of TE=47, 75, 100ms and b-values of 0, 750, 1500s/mm2 , resulting in a 3×3 data matrix associated with each voxel. Prostate Quadrant (PQ) mapping analysis represents HM-MRI data for each voxel as a color-coded vector in the four-quadrant space of HM-MRI parameters (a 2D matrix of signal values for each combination of b-value and TE) with associated amplitude and angle information representing the change in T2 and ADC as a function of b-value and TE, respectively. Cancers have a higher PQ4 (22.50%±21.27%) and lower PQ2 (69.86%±28.24%) compared to benign tissue: peripheral, transition, and central zone (PQ4=0.13%±0.56%, 5.73%±15.07%, 2.66%±4.05%, and PQ2=98.51%±3.05%, 86.18%±21.75%, 93.38%±9.88%, respectively). Cancers have a higher vector angle (206.5±41.8°) and amplitude (0.017±0.013) compared to benign tissue. PQ metrics showed moderate correlation with Gleason score (|ρ|=0.388-0.609), with more aggressive cancers being associated with increased PQ4 and angle and reduced PQ2 and amplitude. A combination of four-quadrant analysis metrics provided an area under the curve of 0.904 (p<0.001) for the differentiation of prostate cancer from benign prostatic tissue. Four-quadrant vector mapping of HM-MRI data provides effective cancer markers, with cancers associated with high PQ4 and high vector angle and lower PQ2 and vector amplitude.

Prediction of extracapsular extension in prostate cancer using the Likert scale combined with clinical and pathological parameters.

This study aimed to investigate the independent clinical, pathological, and radiological factors associated with extracapsular extension in radical prostatectomy specimens and to improve the accuracy of predicting extracapsular extension of prostate cancer before surgery. From August 2018 to June 2023, the clinical and pathological data of 229 patients with confirmed prostate cancer underwent radical prostatectomy from The Second Hospital of Yinzhou. The patients' multiparametric magnetic resonance imaging data were graded using the Likert scale. The chi-square or independent-sample T-test was used to analyze the related factors for an extracapsular extension. Multivariate analysis was used to identify independent factors associated with extracapsular extension in prostate cancer. Additionally, receiver operating characteristic curve analysis was used to calculate the area under the curve and assess the diagnostic performance of our model. The clinical decision curve was used to analyze the clinical net income of Likert scale, biopsy positive rate, biopsy GG, and combined mode. Of the 229 patients, 52 had an extracapsular extension, and 177 did not. Multivariate analysis showed that the Likert scale score, biopsy grade group and biopsy positive rate were independent risk factors for extracapsular extension in prostate cancer. The area under the curves for the Likert scale score, biopsy grade group, and biopsy positive rate were 0.802, 0.762, and 0.796, respectively. Furthermore, there was no significant difference in the diagnostic efficiency for extracapsular extension (P>0.05). However, when these three factors were combined, the diagnostic efficiency was significantly improved, and the area under the curve increased to 0.905 (P<0.05). In the analysis of the decision curve, The clinical net income of the combined model is obviously higher than that of Likert scale, biopsy positive rate, and biopsy GG. The Likert scale, biopsy grade group and biopsy positive rate are independent risk factors for extracapsular extension in prostate cancer, and their combination can significantly improve the diagnostic efficiency for an extracapsular extension.

Differential diagnosis of benign and malignant liver tumors in children using the method of quantitative assessment of multiparametric magnetic resonance imaging data

Differential diagnosis of benign and malignant liver tumors in children using the method of quantitative assessment of multiparametric magnetic resonance imaging data

Deep learning–based radiomic nomograms for predicting Ki67 expression in prostate cancer

BackgroundTo explore the value of a multiparametric magnetic resonance imaging (MRI)-based deep learning model for the preoperative prediction of Ki67 expression in prostate cancer (PCa).MaterialsThe data of 229 patients with PCa from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. Deep learning features were extracted and selected from each patient’s prostate multiparametric MRI (diffusion-weighted imaging, T2-weighted imaging, and contrast-enhanced T1-weighted imaging sequences) data to establish a deep radiomic signature and construct models for the preoperative prediction of Ki67 expression. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a joint model. The predictive performance of multiple deep-learning models was then evaluated.ResultsSeven prediction models were constructed: one clinical model, three deep learning models (the DLRS-Resnet, DLRS-Inception, and DLRS-Densenet models), and three joint models (the Nomogram-Resnet, Nomogram-Inception, and Nomogram-Densenet models). The areas under the curve (AUCs) of the clinical model in the testing, internal validation, and external validation sets were 0.794, 0.711, and 0.75, respectively. The AUCs of the deep models and joint models ranged from 0.939 to 0.993. The DeLong test revealed that the predictive performance of the deep learning models and the joint models was superior to that of the clinical model (p < 0.01). The predictive performance of the DLRS-Resnet model was inferior to that of the Nomogram-Resnet model (p < 0.01), whereas the predictive performance of the remaining deep learning models and joint models did not differ significantly.ConclusionThe multiple easy-to-use deep learning–based models for predicting Ki67 expression in PCa developed in this study can help physicians obtain more detailed prognostic data before a patient undergoes surgery.

Evaluation of Multiparametric MRI Radiomics-Based Nomogram in Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Two-Center study

IntroductionThis study evaluated the performance of primary foci of breast cancer on multiparametric magnetic resonance imaging (MRI) contributing to establish and validate radiomics-based nomograms for predicting the different pathological outcome of breast cancer patients after neoadjuvant chemotherapy (NAC). Materials and MethodsRetrospectively collected 387 patients with locally advanced breast cancer, all treated with NAC and received breast dynamic contrast-enhanced MRI (DCE-MRI) before NAC. Radiomics signatures were extracted from region of interest (ROI) on multiparametric MRI to build rad score. Clinical-pathologic data and radiological features established the clinical model. The comprehensive model featured rad-score, predictive clinical-pathologic data and radiological features, which was ultimately displayed as a nomogram. Patients were grouped in 2 different ways in accordance with the Miller-Payne (MP) grading of surgical specimens. The first grouping method: 181 patients with pathological reaction grades Ⅳ∼Ⅴ were included in the significant remission group, while 206 patients with pathological reaction grades Ⅰ∼Ⅲ were included in the nonsignificant remission group. The second grouping method: 117 patients with pathological complete response (pCR) were assigned to the pCR group, and 270 patients who failed to meet pCR were assigned to in the non-pCR group. Two combined nomograms are created from 2 grouped data for predicting different pathological responses to NAC. The area under the curves (AUC) of the receiver operating characteristic curves (ROC) were used to evaluate the performance of each model. While decision curve analysis (DCA) and calibration curves were used for estimating the clinical application value of the nomogram. ResultsTwo combined nomograms embodying rad score and clinical-pathologic data outperformed, showing good calibrations for predicting response to NAC. The combined nomogram predicting pCR showed the best performance with the AUC values of 0.97, 0.90 and 0.86 in the training, testing, and external validation cohorts respectively. The AUC values of another combined nomogram predicting significant remission: 0.98, 0.88 0.80 in the training, testing and external validation cohorts. DCA showed the comprehensive model nomogram obtained the most clinical benefit. ConclusionsThe combined nomogram could preoperatively predict significant remission or even pCR to NAC in breast cancer based on multiparametric MRI and clinical-pathologic data.

Anatomo-radiological correlation of prostate cancer: Experience within the University Hospital of Fez (about 36 cases)

Prostate cancer is a real public health problem. It is the second most common cancer in men, due to longer life expectancy, especially in developed countries. The diagnosis is confirmed by histology after the prostate biopsy, the latter also makes it possible to assess the severity by the Gleason score. Multiparametric Magnetic Resonance Imaging (MRI) currently plays an important role in the detection and localization of intra- and extra-prostatic tumor foci by combining the 3 sequences (T2 sequence, diffusion sequence, perfusion sequence). This examination thus makes it possible to evaluate the severity of the tumoral lesion through the PIRADS score. In our series, the PIRADS score varied between 3, 4 and 5. We have illustrated through a review of the literature that multiparametric MRI data are correlated with prostate biopsy data. The presence of a clearly identifiable lesion on mpMRI is correlated with an increase in the Gleason score on the prostate biopsy. In our study, it was shown that there is a statistically significant positive correlation between the PIRADS score and the Gleason score. The use of multiparametric MRI of the prostate is therefore a good means of screening, diagnosis and locoregional extension of prostate tumors. However, its use remains limited by its availability and high cost.

Prediction of response to preoperative neoadjuvant chemotherapy in extremity high-grade osteosarcoma using X-ray and multiparametric MRI radiomics.

This study aims to evaluate the value of applying X-ray and magnetic resonance imaging (MRI) models based on radiomics feature to predict response of extremity high-grade osteosarcoma to neoadjuvant chemotherapy (NAC). A retrospective dataset was assembled involving 102 consecutive patients (training dataset, n = 72; validation dataset, n = 30) diagnosed with extremity high-grade osteosarcoma. The clinical features of age, gender, pathological type, lesion location, bone destruction type, size, alkaline phosphatase (ALP), and lactate dehydrogenase (LDH) were evaluated. Imaging features were extracted from X-ray and multi-parametric MRI (T1-weighted, T2-weighted, and contrast-enhanced T1-weighted) data. Features were selected using a two-stage process comprising minimal-redundancy-maximum-relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression. Logistic regression (LR) modelling was then applied to establish models based on clinical, X-ray, and multi-parametric MRI data, as well as combinations of these datasets. Each model was evaluated using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI). AUCs of 5 models using clinical, X-ray radiomics, MRI radiomics, X-ray plus MRI radiomics, and combination of all were 0.760 (95% CI: 0.583-0.937), 0.706 (95% CI: 0.506-0.905), 0.751 (95% CI: 0.572-0.930), 0.796 (95% CI: 0.629-0.963), 0.828 (95% CI: 0.676-0.980), respectively. The DeLong test showed no significant difference between any pair of models (p > 0.05). The combined model yielded higher performance than the clinical and radiomics models as demonstrated by net reclassification improvement (NRI) and integrated difference improvement (IDI) values, respectively. This combined model was also found to be clinically useful in the decision curve analysis (DCA). Modelling based on combination of clinical and radiomics data improves the ability to predict pathological responses to NAC in extremity high-grade osteosarcoma compared to the models based on either clinical or radiomics data.

The Safety and Efficacy of Salvage Stereotactic Radiation Therapy in Patients with Intraprostatic Tumor Recurrence After Previous External Radiation Therapy: Phase 1 Results from the GETUG-AFU 31 Study

BackgroundThere is no consensus on the best local salvage treatment for prostate cancer recurrence after primary external beam radiotherapy. Prospective data on stereotactic body radiation therapy (SBRT) are very scarce. ObjectiveTo determine the optimal dose regimen for salvage SBRT. Design, setting, and participantsThe present report concerns the phase 1 part of the GETUG-AFU 31 multicenter open-label study. The main inclusion criteria were histologically proven biochemical recurrence, clinical stage T1-T2 upon relapse, multiparametric magnetic resonance imaging data, prostate-specific antigen (PSA) level ≤10 ng/ml prior to salvage SBRT, PSA doubling time >10 mo, and an International Prostate Symptom Score of ≤12. InterventionFive or six fractions of 6 Gy were delivered using focal SBRT. Outcome measurements and statistical analysisDose-limiting toxicity (DLT) was defined as grade ≥3 gastrointestinal or genitourinary tract toxicity, or any grade 4 toxicity (according to the National Cancer Institute Common Terminology Criteria for Adverse Events version 4.03) occurring in the first 18 wk following treatment initiation. A time-to-event continual reassessment method was used to select the dose regimen. Results and limitationsTwenty-one patients were treated (median [interquartile range] age: 76.8 yr [72.2–80.8]), including 12 at 6 × 6 dose level. No DLT was observed. The acute grade 2 genitourinary tract toxicity rate was 19%. With a median follow-up of 12.3 mo, the estimated cumulative incidence of late grade 2 genitourinary toxicity was 41.2% (95% confidence interval: 18.1–63.1%). No grade >2 genitourinary toxicity and no grade ≥2 gastrointestinal toxicity were reported. All treated patients were alive and relapse free at the last follow-up. ConclusionsA 6 × 6 Gy dose regimen was selected for our phase 2 study of salvage SBRT. With a short follow-up period, the level of toxicity appears to be acceptable. Patient summaryThere is no consensus on the best local treatment for patients with local relapse after radiotherapy for prostate cancer. Prospective data are very scarce. Our early phase trial allowed us to recommend six fractions of 6 Gy using high-precision radiotherapy for further studies.

Abstract 5980: Identification of pre-treatment tumor habitats for the prediction of neoadjuvant therapy response in triple negative breast cancer

Abstract Introduction: Triple negative breast cancer (TNBC) patients exhibit varied levels of response to neoadjuvant chemotherapy (NAC), with only 27-51% of patients achieving pathological complete response (pCR). This diverse response can be attributed in-part to heterogeneity of the tumor microenvironment, affecting therapeutic delivery and efficacy. Multiparametric magnetic resonance imaging (MRI) can be used to spatially resolve intratumoral heterogeneity into distinct tumor subregions, or habitats. We investigated whether MRI-derived tumor habitats identified prior to initiation of NAC were predictive of pathological response in TNBC patients. Methods: Women with stage II/III TNBC who received a pre-treatment (baseline) breast MRI and NAC at our institution (2012-2019) were retrospectively identified. Pathological response was determined at surgery, with pCR defined as no residual tumor within the breast or lymph nodes. Both diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI data were collected prior to initiation of NAC. The apparent diffusion coefficient (a measure of cell density) was calculated for each voxel from DW-MRI. Signal enhancement ratio, percent enhancement, and wash-out slope were calculated for each voxel from DCE-MRI, providing measures of vascularity. Hierarchical clustering of voxel data was used to identify tumor habitats, with each subregion labeled in terms of “high” or “low” vascularity and cellularity based on mean parameter values for the subregion. Tumor composition was quantified as percent tumor volume comprised by each habitat. Differences between pCR and non-pCR patients were assessed using Wilcoxon rank sum test, with p&amp;lt;0.05 considered significant. Results: 46 women with TNBC were retrospectively identified (median age: 48, range 31-77 yrs), of which 14 (30%) achieved pCR. No significant differences between pCR and non-pCR patients were observed in baseline tumor volume or longest diameter (p&amp;gt;0.05). Clustering analysis yielded four tumor habitats: low-vascularity low-cellularity (LV-LC), low-vascularity high-cellularity (LV-HC), high-vascularity low-cellularity (HV-LC), and high-vascularity high-cellularity (HV-HC). Patients who achieved pCR had significantly higher fraction of the HV-HC habitat at baseline (p=0.02). No significant differences were observed for other habitats. Discussion &amp; Conclusion: Our findings suggest multiparametric MRI can identify physiologically-distinct tumor habitats prior to NAC, which are predictive of response. A higher fraction of HV-HC habitat was associated with pCR, potentially suggestive of increased therapeutic delivery/sensitivity. Clinical translation of this approach would enable more specific characterizations of tumor heterogeneity and prediction of response, which could aid in personalizing regimens for optimal outcomes. Citation Format: Anum S. Kazerouni, Laura C. Kennedy, Shaveta Vinayak, Suzanne Dintzis, Habib Rahbar, Savannah C. Partridge. Identification of pre-treatment tumor habitats for the prediction of neoadjuvant therapy response in triple negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5980.

Diffusion Restriction Comparison between Gleason 4 Fused Glands and Cribriform Glands within Patient Using Whole-Mount Prostate Pathology as Ground Truth.

The presence and extent of cribriform patterned Gleason 4 (G4) glands are associated with poor prognosis following radical prostatectomy. This study used whole-mount prostate histology and multiparametric magnetic resonance imaging (MP-MRI) to evaluate diffusion differences in G4 gland morphology. Fourty-eight patients underwent MP-MRI prior to prostatectomy, of whom 22 patients had regions of both G4 cribriform glands and G4 fused glands (G4CG and G4FG, respectively). After surgery, the prostate was sliced using custom, patient-specific 3D-printed slicing jigs modeled according to the T2-weighted MR image, processed, and embedded in paraffin. Whole-mount hematoxylin and eosin-stained slides were annotated by our urologic pathologist and digitally contoured to differentiate the lumen, epithelium, and stroma. Digitized slides were co-registered to the T2-weighted MRI scan. Linear mixed models were fitted to the MP-MRI data to consider the different hierarchical structures at the patient and slide level. We found that Gleason 4 cribriform glands were more diffusion-restricted than fused glands.

Automated detection and grading of prostate cancer in multiparametric MRI

Prostate cancer is a major health issue worldwide and automatic segmentation of prostate from magnetic resonance imaging (MRI) is crucial task in image guided intervention. The objective of this paper is to develop a transparent and meticulous feature learning framework for prostate cancer detection and grading of prostate cancer using multiparametric magnetic resonance imaging (MPMRI). Prostate cancer is confirmed using approved rules of prostate cancer diagnosis from MPMRI data. The clustering is done in apparent diffusion coefficient (ADC) and diffusion weighted images (DWI) to obtain a probabilistic map which confirms cancerous region. The performance of proposed work is enormously tested on the dataset that contains T2Weighted, DWI and ADC map images of 236 subjects. In this study a total of 218 regions were used for analysis which includes 53 non-cancerous regions and 165 cancerous lesions. We have obtained tumour detection accuracy of 93.2% and AUC of 0.94 by using random forest classifier. The results yield by proposed algorithm is validated by two experienced radiologists.

A deep learning-based model for prediction of hemorrhagic transformation after stroke.

Hemorrhagic transformation (HT) is one of the most serious complications after endovascular thrombectomy (EVT) in acute ischemic stroke (AIS) patients. The purpose of this study is to develop and validate deep-learning (DL) models based on multiparametric magnetic resonance imaging (MRI) to automatically predict HT in AIS patients. Multiparametric MRI and clinical data of AIS patients with EVT from two centers (data set 1 for training and testing: n=338; data set 2 for validating: n=54) were used in the DL models. The acute infarction area of diffusion-weighted imaging (DWI) and hypoperfusion of perfusion-weighted imaging (PWI) was labeled manually. Two forms of data sets (volume of interest [VOI] data sets and slice data sets) were analyzed, respectively. The models based on single parameter and multiparameter models were developed and validated to predict HT in AIS patients after EVT. Performance was evaluated by area under the receiver-operating characteristic curve (AUC), accuracy (ACC), sensitivity, specificity, negative predictive value, and positive predictive value. The results showed that the performance of single parameter model based on MTT (VOI data set: AUC=0.933, ACC=0.843; slice data set: AUC=0.945, ACC=0.833) and TTP (VOI data set: AUC=0.916, ACC=0.873; slice data set: AUC=0.889, ACC=0.818) were better than the other single parameter model. The multiparameter model based on DWI & MTT & TTP & Clinical (DMTC) had the best performance for predicting HT (VOI data set: AUC=0.948, ACC=0.892; slice data set: AUC=0.932, ACC=0.873). The DMTC model in the external validation set achieved similar performance with the testing set (VOI data set: AUC=0.939, ACC=0.884; slice data set: AUC=0.927, ACC=0.871) (p>0.05). The proposed clinical, DWI, and PWI multiparameter DL model has great potential for assisting the periprocedural management in the early prediction HT of the AIS patients with EVT.

Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers

This study attempts to explore the radiomics-based features of multi-parametric magnetic resonance imaging (MRI) and construct a machine-learning model to predict the blood supply in vestibular schwannoma preoperatively. By retrospectively collecting the preoperative MRI data of patients with vestibular schwannoma, patients were divided into poor and rich blood supply groups according to the intraoperative recording. Patients were divided into training and test cohorts (2:1), randomly. Stable features were retained by intra-group correlation coefficients (ICCs). Four feature selection methods and four classification methods were evaluated to construct favorable radiomics classifiers. The mean area under the curve (AUC) obtained in the test set for different combinations of feature selecting methods and classifiers was calculated separately to compare the performance of the models. Obtain and compare the best combination results with the performance of differentiation through visual observation in clinical diagnosis. 191 patients were included in this study. 3918 stable features were extracted from each patient. Least absolute shrinkage and selection operator (LASSO) and logistic regression model was selected as the optimal combinations after comparing the AUC calculated by models, which predicted the blood supply of vestibular schwannoma by K-Fold cross-validation method with a mean AUC = 0.88 and F1-score = 0.83. Radiomics machine-learning classifiers can accurately predict the blood supply of vestibular schwannoma by preoperative MRI data.

Development and validation of a nomogram based on multiparametric magnetic resonance imaging and elastography-derived data for the stratification of patients with prostate cancer.

This study sought to develop and validate a nomogram combining the elastographic Q-analysis score (EQS), the Prostate Imaging Reporting and Data System (PI-RADS) score, and clinical parameters for the stratification of patients with prostate cancer (PCa). A retrospective study was conducted of 375 patients with 375 lesions who underwent volume-navigation transrectal ultrasound (TRUS) and multiparametric magnetic resonance imaging (MP-MRI)-fusion targeted biopsies between April 2017 and January 2020. Based on a multivariate logistic regression model, a nomogram was created to assess any PCa and high-risk PCa [Gleason score (GS) ≥4+3] using data from patients diagnosed between April 2017 and June 2019 (n=271), and was validated in patients diagnosed after July 2019 (n=104). The nomogram's performance was evaluated based on its discrimination, calibration, and clinical usefulness. The areas under the curve (AUCs) of the nomogram for predicting any PCa and high-risk PCa were 0.949 [95% confidence interval (CI), 0.921 to 0.978] and 0.936 (95% CI, 0.906 to 0.965), respectively, in the training cohort, and 0.946 (95% CI, 0.894 to 0.997) and 0.971 (95% CI, 0.9331 to 1), respectively, in the validation cohort. The nomogram was well calibrated, and no significant difference was found between the predicted and observed probabilities. A decision curve analysis (DCA) for the nomogram with and without the EQS showed that the threshold probability of for any PCa was <67%. The nomogram that combined elastography-derived and MP-MRI data was more clinically useful than the model based on PI-RADS and clinical parameters alone. Our nomogram could aid urologists to make decisions and avoid unnecessary biopsies.

Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation

High-grade gliomas are an aggressive and invasive malignancy which are susceptible to treatment resistance due to heterogeneity in intratumoral properties such as cell proliferation and density and perfusion. Non-invasive imaging approaches can measure these properties, which can then be used to calibrate patient-specific mathematical models of tumor growth and response. We employed multiparametric magnetic resonance imaging (MRI) to identify tumor extent (via contrast-enhanced T1-weighted, and T2-FLAIR) and capture intratumoral heterogeneity in cell density (via diffusion-weighted imaging) to calibrate a family of mathematical models of chemoradiation response in nine patients with unresected or partially resected disease. The calibrated model parameters were used to forecast spatially-mapped individual tumor response at future imaging visits. We then employed the Akaike information criteria to select the most parsimonious member from the family, a novel two-species model describing the enhancing and non-enhancing components of the tumor. Using this model, we achieved low error in predictions of the enhancing volume (median: − 2.5%, interquartile range: 10.0%) and a strong correlation in total cell count (Kendall correlation coefficient 0.79) at 3-months post-treatment. These preliminary results demonstrate the plausibility of using multiparametric MRI data to inform spatially-informative, biologically-based predictive models of tumor response in the setting of clinical high-grade gliomas.

Abstract PS13-44: Identifying relevant parameters that characterize the early response to NAT in breast cancer patients using a novel personalized mechanistic model integrating in vitro and in vivo imaging data

Abstract The early determination of response to neoadjuvant therapy (NAT) in triple-negative breast cancer would enable the treating oncologist to adapt the therapeutic regimen of a non-responding patient (e.g., by changing dosage, dose schedule, prescribed drugs), and thereby improve treatment outcomes while avoiding unnecessary toxicities. To address this challenge, we propose to use personalized, in silico forecasts of tumor response to therapeutic regimens via a mechanistic mathematical model calibrated with patient-specific longitudinal multi-parametric magnetic resonance imaging (MRI) data acquired early in the course of NAT. Here, we extend our mechanistic model to include a new term describing the synergistic effects of NAT drug combinations and identify the driving parameters involved in its formulation by means of a sensitivity analysis. Our model describes tumor cell dynamics as a combination of proliferation, which is regulated by a logistic term, and mobility, which is described as a diffusion process constrained by the local tumor-induced mechanical stress. Tumor cell density is extracted from diffusion-weighted MRI data, while tissue mechanical properties are defined from segmented T1-weighted MRI data. We adjust the tumor proliferation rate in response to NAT drug combinations with a recent model of drug synergy, MuSyC, which accounts for distinct types of synergistic drug effects (synergy of potency vs. synergy of efficacy). We also consider the heterogeneous intratumoral delivery of drugs by means of perfusion maps estimated from dynamic contrast-enhanced MRI data. We use Sobol’s method for the sensitivity analysis of two different tumors - one well-perfused and one poorly-perfused. We simulate a four-cycle NAT protocol in which NAT drugs are delivered every 14 days, and assess the total effect (ST) of each parameter on the mean relative difference of tumor cell density with respect to a control simulation of tumor growth without NAT. Sensitivity analysis results directly depend on the definition of the parameter space, which we construct by combining two approaches. First, we experimentally constrain parameter ranges using time-resolved, high-throughput, automated microscopy assays to capture the changes in proliferation rates of various breast cancer lines (HCC1143, SUM149, MDAMB231, and MDAMB468) caused by two standard drug combinations: paclitaxel with carboplatin and doxorubicin with perfosfamide (metabolic derivative of the pro-drug cyclophosphamide), and fitting the MuSyC model to these data. Second, we scale the resulting in vitro parameter ranges to clinically-relevant in vivo ranges by running an in silico study with our mechanistic model of breast cancer growth and NAT response. Our results show that, out of the ten parameters involved in the synergy term, three have a dominant role in the dynamics of breast cancer during NAT (ST &amp;gt; 0.1): synergistic potency, the maximal change in tumor cell proliferation by the slowest decaying drug, and its concentration producing half of maximal effects. The other parameters have marginal (0.02 &amp;lt; ST &amp;lt; 0.1) to negligible effect (ST &amp;lt; 0.02). Ongoing studies are assessing the ability of our mechanistic model to forecast NAT response over a small patient cohort after patient-specific calibration of the driving parameters identified in the present study. Citation Format: Guillermo Lorenzo, Angela M. Jarrett, Christian T. Meyer, Darren R. Tyson, Vito Quaranta, Thomas E. Yankeelov. Identifying relevant parameters that characterize the early response to NAT in breast cancer patients using a novel personalized mechanistic model integrating in vitro and in vivo imaging data [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS13-44.