Coefficient Maps Research Articles

Diffusion lacunae: a novel MR imaging finding on diffusion-weighted imaging for diagnosing placenta accreta spectrum.

To evaluate the usefulness of novel diffusion-weighted imaging (DWI) findings for diagnosing placenta accreta spectrum (PAS). This retrospective study included 49 pregnant women with suspected PAS who underwent 1.5T placental MRI. Diffusion lacunae were defined as intraplacental areas showing hypointensity on DWI and hyperintensity on the apparent diffusion coefficient map. Two radiologists evaluated the number and size of placental lacunae on DWI, and flow void in the diffusion lacunae on T2-weighted imaging. The radiologists also evaluated established MRI features of PAS described in the SAR-ESUR consensus statement. Pearson's chi-square test or Mann-Whitney U test was used to compare findings between patients with and without PAS. Interobserver reliability for DWI and established MRI features was also assessed. Optimal thresholds for the number and maximum size of diffusion lacunae for differentiating PAS from the no-PAS group were determined using receiver operating characteristic curve analyses. Eighteen patients were diagnosed with PAS, and 31 patients with placental previa without PAS. The number and maximum size of diffusion lacunae were significantly larger in patients with than in patients without PAS (p < 0.0001). Combining assessment of the number of diffusion lacunae with assessment of their maximum size yielded a diagnostic performance with sensitivity, specificity and accuracy of 83%, 94% and 90%, respectively. Flow voids within the diffusion lacunae had sensitivity, specificity and accuracy of 88%, 84% and 86%, respectively. The number and size of diffusion lacunae, and T2 flow void in diffusion lacunae may be useful findings for diagnosing PAS.

A retrospective evaluation of the potential of ChatGPT in the accurate diagnosis of acute stroke.

Stroke is a neurological emergency requiring rapid, accurate diagnosis to prevent severe consequences. Early diagnosis is crucial for reducing morbidity and mortality. Artificial intelligence (AI) diagnosis support tools, such as Chat Generative Pre-trained Transformer (ChatGPT), offer rapid diagnostic advantages. This study assesses ChatGPT's accuracy in interpreting diffusion-weighted imaging (DWI) for acute stroke diagnosis. A retrospective analysis was conducted to identify the presence of stroke using DWI and apparent diffusion coefficient (ADC) map images. Patients aged >18 years who exhibited diffusion restriction and had a clinically explainable condition were included in the study. Patients with artifacts that affected image homogeneity, accuracy, and clarity, as well as those who had undergone previous surgery or had a history of stroke, were excluded from the study. ChatGPT was asked four consecutive questions regarding the identification of the magnetic resonance imaging (MRI) sequence, the demonstration of diffusion restriction on the ADC map after sequence recognition, and the identification of hemispheres and specific lobes. Each question was repeated 10 times to ensure consistency. Senior radiologists subsequently verified the accuracy of ChatGPT's responses, classifying them as either correct or incorrect. We assumed a response to be incorrect if it was partially correct or suggested multiple answers. These responses were systematically recorded. We also recorded non-responses from ChatGPT-4V when it failed to provide an answer to a query. We assessed ChatGPT-4V's performance by calculating the number and percentage of correct responses, incorrect responses, and non-responses across all images and questions, a metric known as "accuracy." ChatGPT-4V was considered successful if it answered ≥80% of the examples correctly. A total of 530 diffusion MRI, of which 266 were stroke images and 264 were normal, were evaluated in the study. For the initial query identifying MRI sequence type, ChatGPT-4V's accuracy was 88.3% for stroke and 90.1% for normal images. For detecting diffusion restriction, ChatGPT-4V had an accuracy of 79.5% for stroke images, with a 15% false positive rate for normal images. Regarding identifying the brain or cerebellar hemisphere involved, ChatGPT-4V correctly identified the hemisphere in 26.2% of stroke images. For identifying the specific brain lobe or cerebellar area affected, ChatGPT-4V had a 20.4% accuracy for stroke images. The diagnostic sensitivity of ChatGPT-4V in acute stroke was found to be 79.57%, with a specificity of 84.87%, a positive predictive value of 83.86%, a negative predictive value of 80.80%, and a diagnostic odds ratio of 21.86. Despite limitations, ChatGPT shows potential as a supportive tool for healthcare professionals in interpreting diffusion examinations in stroke cases, where timely diagnosis is critical. ChatGPT can play an important role in various aspects of stroke cases, such as risk assessment, early diagnosis, and treatment planning.

Ghost Sign on Diffusion-Weighted Imaging Generated Apparent Diffusion Coefficient Map: Additional MRI Diagnostic Marker for Extremity Osteomyelitis

Objective The aim of this study was to determine the sensitivity and specificity and inter-reader reliability of previously known “ghost sign” and “penumbra sign” on T1-weighted (T1W) imaging and “ghost sign” on apparent diffusion coefficient (ADC) map in osteomyelitis (OM) of the extremities. Materials and Methods In this cross-sectional retrospective study, two fellowship-trained musculoskeletal readers blinded to final diagnosis of OM versus no OM were asked to report the penumbra sign and ghost sign on T1W images and ghost sign on ADC map, as well as diagnosis of OM. Cohen's kappa was used. Diagnostic performance measures including sensitivity, specificity, and accuracy were calculated. Results A sample of 178 magnetic resonance imaging (MRI) scans of pathology-proven cases were included in this study, with 41 being positive for OM and 137 being negative for OM. There was a fair inter-reader agreement for imaging signs, and moderate agreement of 0.60 for OM. The sensitivities of the penumbra sign on T1W imaging, ghost sign on T1W imaging, and ghost sign on ADC map for OM are 3.7, 9.8, and 19.5%, respectively, while their respective specificities are 98.9, 97.8, and 94.5%, respectively. All three imaging signs showed a similar (good) accuracy of 76 to 78%. Conclusion The ghost sign on ADC can be used as an additional marker for OM and is a similarly highly specific but a more sensitive sign for OM than the conventionally used penumbra sign and ghost sign on T1W imaging. Key Points

Addressing intra- and inter-institution variability of a radiomic framework based on Apparent Diffusion Coefficient in prostate cancer.

Prostate cancer (PCa) is a highly heterogeneous disease, making tailored treatment approaches challenging. Magnetic resonance imaging (MRI), notably diffusion-weighted imaging (DWI) and the derived Apparent Diffusion Coefficient (ADC) maps, plays a crucial role in PCa characterization. In this context, radiomics is a very promising approach able to disclose insights from MRI data. However, the sensitivity of radiomic features to MRI settings, encompassing DWI protocols and multicenter variations, requires the development of robust and generalizable models. To develop a comprehensive radiomics framework for noninvasive PCa characterization using ADC maps, focusing on identifying reliable imaging biomarkers against intra- and inter-institution variations. Two patient cohorts, including an internal cohort (118 PCa patients) used for both training (75%) and hold-out testing (25%), and an external cohort (50 PCa patients) for independent testing, were employed in the study. DWI images were acquired with three different DWI protocols on two different MRI scanners: two DWI protocols acquired on a 1.5-T scanner for the internal cohort, and one DWI protocol acquired on a 3-T scanner for the external cohort. One hundred and seven radiomics features (i.e., shape, first order, texture) were extracted from ADC maps of the whole prostate gland. To address variations in DWI protocols and multicenter variability, a dedicated pipeline, including two-way ANOVA, sequential-feature-selection (SFS), and ComBat features harmonization was implemented. Mann-Whitney U-tests (α=0.05) were performed to find statistically significant features dividing patients with different tumor characteristics in terms of Gleason score (GS) and T-stage. Support-Vector-Machine models were then developed to predict GS and T-stage, and the performance was assessed through the area under the curve (AUC) of receiver-operating-characteristic curves. Downstream of ANOVA, two subsets of 38 and 41 features stable against DWI protocol were identified for GS and T-stage, respectively. Among these, SFS revealed the most predictive features, yielding an AUC of 0.75 (GS) and 0.70 (T-stage) in the hold-out test. Employing ComBat harmonization improved the external-test performance of the GS model, raising AUC from 0.72 to 0.78. By incorporating stable features with a harmonization procedure and validating the model on an external dataset, model robustness, and generalizability were assessed, highlighting the potential of ADC and radiomics for PCa characterization.

Quantitative and Radiological Assessment of Post-cardiac-Arrest Comatose Patients with Diffusion-Weighted Magnetic Resonance Imaging.

Although magnetic resonance imaging, particularly diffusion-weighted imaging, has increasingly been used as part of a multimodal approach to prognostication in patients who are comatose after cardiac arrest, the performance of quantitative analysis of apparent diffusion coefficient (ADC) maps, as compared to standard radiologist impression, has not been well characterized. This retrospective study evaluated quantitative ADC analysis to the identification of anoxic brain injury by diffusion abnormalities on standard clinical magnetic resonance imaging reports. The cohort included 204 previously described comatose patients after cardiac arrest. Clinical outcome was assessed by (1) 3-6 month post-cardiac-arrest cerebral performance category and (2) coma recovery to following commands. Radiological evaluation was obtained from clinical reports and characterized as diffuse, cortex only, deep gray matter structures only, or no anoxic injury. Quantitative analyses of ADC maps were obtained in specific regions of interest (ROIs), whole cortex, and whole brain. A subgroup analysis of 172 was performed after eliminating images with artifacts and preexisting lesions. Radiological assessment outperformed quantitative assessment over all evaluated regions (area under the curve [AUC] 0.80 for radiological interpretation and 0.70 for the occipital region, the best performing ROI, p = 0.011); agreement was substantial for all regions. Radiological assessment still outperformed quantitative analysis in the subgroup analysis, though by smaller margins and with substantial to near-perfect agreement. When assessing for coma recovery only, the difference was no longer significant (AUC 0.83 vs. 0.81 for the occipital region, p = 0.70). Although quantitative analysis eliminates interrater differences in the interpretation of abnormal diffusion imaging and avoids bias from other prediction modalities, clinical radiologist interpretation has a higher predictive value for outcome. Agreement between radiological and quantitative analysis improved when using high-quality scans and when assessing for coma recovery using following commands. Quantitative assessment may thus be more subject to variability in both clinical management and scan quality than radiological assessment.

A canonical polyadic tensor basis for fast Bayesian estimation of multi-subject brain activation patterns.

Task-evoked functional magnetic resonance imaging studies, such as the Human Connectome Project (HCP), are a powerful tool for exploring how brain activity is influenced by cognitive tasks like memory retention, decision-making, and language processing. A fast Bayesian function-on-scalar model is proposed for estimating population-level activation maps linked to the working memory task. The model is based on the canonical polyadic (CP) tensor decomposition of coefficient maps obtained for each subject. This decomposition effectively yields a tensor basis capable of extracting both common features and subject-specific features from the coefficient maps. These subject-specific features, in turn, are modeled as a function of covariates of interest using a Bayesian model that accounts for the correlation of the CP-extracted features. The dimensionality reduction achieved with the tensor basis allows for a fast MCMC estimation of population-level activation maps. This model is applied to one hundred unrelated subjects from the HCP dataset, yielding significant insights into brain signatures associated with working memory.

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.

Review of whole-body magnetic resonance imaging in multiple myeloma.

Multiple Myeloma (MM) is a hematological malignancy affecting bone marrow, most frequently in elderly men. Imaging has a crucial role in this disease. Recently, whole-body MRI has been introduced and it has gained growing interest due to is high sensitivity and specificity in evaluating bone marrow involvement in MM. Diffusion-weighted sequences (DWI) with apparent diffusion coefficient (ADC) maps have emerged as the most sensitive technique to evaluate patients with MM, both in the pre- and post-treatment setting. Aim of this review is to provide an overview of the role and main imaging findings of whole-body MRI in MM.

Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI.

Background Multiparametric MRI can help identify clinically significant prostate cancer (csPCa) (Gleason score ≥7) but is limited by reader experience and interobserver variability. In contrast, deep learning (DL) produces deterministic outputs. Purpose To develop a DL model to predict the presence of csPCa by using patient-level labels without information about tumor location and to compare its performance with that of radiologists. Materials and Methods Data from patients without known csPCa who underwent MRI from January 2017 to December 2019 at one of multiple sites of a single academic institution were retrospectively reviewed. A convolutional neural network was trained to predict csPCa from T2-weighted images, diffusion-weighted images, apparent diffusion coefficient maps, and T1-weighted contrast-enhanced images. The reference standard was pathologic diagnosis. Radiologist performance was evaluated as follows: Radiology reports were used for the internal test set, and four radiologists' PI-RADS ratings were used for the external (ProstateX) test set. The performance was compared using areas under the receiver operating characteristic curves (AUCs) and the DeLong test. Gradient-weighted class activation maps (Grad-CAMs) were used to show tumor localization. Results Among 5735 examinations in 5215 patients (mean age, 66 years ± 8 [SD]; all male), 1514 examinations (1454 patients) showed csPCa. In the internal test set (400 examinations), the AUC was 0.89 and 0.89 for the DL classifier and radiologists, respectively (P = .88). In the external test set (204 examinations), the AUC was 0.86 and 0.84 for the DL classifier and radiologists, respectively (P = .68). DL classifier plus radiologists had an AUC of 0.89 (P < .001). Grad-CAMs demonstrated activation over the csPCa lesion in 35 of 38 and 56 of 58 true-positive examinations in internal and external test sets, respectively. Conclusion The performance of a DL model was not different from that of radiologists in the detection of csPCa at MRI, and Grad-CAMs localized the tumor. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Johnson and Chandarana in this issue.

Biparametric MRI of the prostate radiomics model for prediction of pelvic lymph node metastasis in prostate cancers : a two-centre study

ObjectivesExploring the value of adding correlation analysis (radiomic features (RFs) of pelvic metastatic lymph nodes and primary lesions) to screen RFs of primary lesions in the feature selection process of establishing prediction model.MethodsA total of 394 prostate cancer (PCa) patients (263 in the training group, 74 in the internal validation group and 57 in the external validation group) from two tertiary hospitals were included in the study. The cases with pelvic lymph node metastasis (PLNM) positive in the training group were diagnosed by biopsy or MRI with a short-axis diameter ≥ 1.5 cm, PLNM-negative cases in the training group and all cases in validation group were underwent both radical prostatectomy (RP) and extended pelvic lymph node dissection (ePLND). The RFs of PLNM-negative lesion and PLNM-positive tissues including primary lesions and their metastatic lymph nodes (MLNs) in the training group were extracted from T2WI and apparent diffusion coefficient (ADC) map to build the following two models by fivefold cross-validation: the lesion model, established according to the primary lesion RFs selected by t tests and absolute shrinkage and selection operator (LASSO); the lesion-correlation model, established according to the primary lesion RFs selected by Pearson correlation analysis (RFs of primary lesions and their MLNs, correlation coefficient > 0.9), t test and LASSO. Finally, we compared the performance of these two models in predicting PLNM.ResultsThe AUC and the DeLong test of AUC in the lesion model and lesion-correlation model were as follows: training groups (0.8053, 0.8466, p = 0.0002), internal validation group (0.7321, 0.8268, p = 0.0429), and external validation group (0.6445, 0.7874, p = 0.0431), respectively.ConclusionThe lesion-correlation model established by features of primary tumors correlated with MLNs has more advantages than the lesion model in predicting PLNM.

A DWI-based hypoxia model shows robustness in an external prostatectomy cohort.

Prostate cancer hypoxia is a negative prognostic biomarker. A promising MRI-based tool to assess hypoxia is the 'Consumption and Supply based Hypoxia' (CSH) model based on diffusion-weighted imaging (DWI). The aim of the study was to validate the association between the CSH hypoxia fraction (HFDWI) with pathological Grade Group (pGG) and pathological T-staging (pTstage) in an external prostatectomy cohort. Apparent diffusion coefficient (ADC) and fractional blood volume (fBV) maps were assessed from DWI data from 291 prostatectomies and combined by the CSH model. HFDWI was calculated for each lesion after median scaling of ADC and fBV to address differences in acquisition and analysis between centers. The absolute HFDWI values and the associations of HFDWI between pGG < 3 versus ≥ 3, and pTstage = 2 versus = 3 in the Netherlands Cancer Institute (NKI) cohort were compared to the obtained by original cohort (Oslo cohort). Statistical T- and Mann-Whitney tests (p<0.05) were performed. Pearson correlation was determined between HFDWI and individual pGG groups. The HFDWI showed comparable absolute values and similar metric performance as in the original published cohort. Higher HFDWI values were observed for higher pGG (Oslo: 0.27; NKI: 0.24) compared to lower pGG (Oslo: 0.11; NKI: 0.17). Similar results were obtained for pTstage. Furthermore, HFDWI demonstrated a significant positive correlation with pGG groups 1-5 (ρ = 0.41, p<0.001). The CSH model exhibited sufficient robustness in the external cohort, suggesting a plausible reflection of true hypoxia and enabling the use of the HFDWI metric for further research into prostate cancer and hypoxia.

Assessment of imaging risks for recurrence after stereotactic radiosurgery for brain metastases (IRRaS-BM)

BackgroundThe identification of viable tumors and radiation necrosis after stereotactic radiosurgery (SRS) is crucial for patient management. Tumor habitat analysis involving the grouping of similar voxels can identify subregions that share common biology and enable the depiction of areas of tumor recurrence and treatment-induced change. This study aims to validate an imaging biomarker for tumor recurrence after SRS for brain metastasis by conducting tumor habitat analysis using multi-parametric MRI.MethodsIn this prospective study (NCT05868928), patients with brain metastases will undergo multi-parametric MRI before SRS, and then follow-up MRIs will be conducted every 3 months until 24 months after SRS. The multi-parametric MRI protocol will include T2-weighted and contrast-enhanced T1-weighted imaging, diffusion-weighted imaging, and dynamic susceptibility contrast imaging. Using k-means voxel-wise clustering, this study will define three structural MRI habitats (enhancing, solid low-enhancing, and nonviable) on T1- and T2-weighted images and three physiologic MRI habitats (hypervascular cellular, hypovascular cellular, and nonviable) on apparent diffusion coefficient maps and cerebral blood volume maps. Using RANO-BM criteria as the reference standard, via Cox proportional hazards analysis, the study will prospectively evaluate associations between parameters of the tumor habitats and the time to recurrence. The DICE similarity coefficients between the recurrence site and tumor habitats will be calculated.DiscussionThe tumor habitat analysis will provide an objective and reliable measure for assessing tumor recurrence from brain metastasis following SRS. By identifying subregions for local recurrence, our study could guide the next therapeutic targets for patients after SRS.Trial registrationThis study is registered at ClinicalTrials.gov (NCT05868928).

Stability of Radiomic Features against Variations in Lesion Segmentations Computed on Apparent Diffusion Coefficient Maps of Breast Lesions.

Diffusion-weighted imaging (DWI) combined with radiomics can aid in the differentiation of breast lesions. Segmentation characteristics, however, might influence radiomic features. To evaluate feature stability, we implemented a standardized pipeline featuring shifts and shape variations of the underlying segmentations. A total of 103 patients were retrospectively included in this IRB-approved study after multiparametric diagnostic breast 3T MRI with a spin-echo diffusion-weighted sequence with echoplanar readout (b-values: 50, 750 and 1500 s/mm2). Lesion segmentations underwent shifts and shape variations, with >100 radiomic features extracted from apparent diffusion coefficient (ADC) maps for each variation. These features were then compared and ranked based on their stability, measured by the Overall Concordance Correlation Coefficient (OCCC) and Dynamic Range (DR). Results showed variation in feature robustness to segmentation changes. The most stable features, excluding shape-related features, were FO (Mean, Median, RootMeanSquared), GLDM (DependenceNonUniformity), GLRLM (RunLengthNonUniformity), and GLSZM (SizeZoneNonUniformity), which all had OCCC and DR > 0.95 for both shifting and resizing the segmentation. Perimeter, MajorAxisLength, MaximumDiameter, PixelSurface, MeshSurface, and MinorAxisLength were the most stable features in the Shape category with OCCC and DR > 0.95 for resizing. Considering the variability in radiomic feature stability against segmentation variations is relevant when interpreting radiomic analysis of breast DWI data.

Development of a prognostic model for diagnosis of prostate cancer based on radiomics of biparametric magnetic resonance imaging apparent diffusion coefficient maps and stacking of machine learning algorithms

Development of a prognostic model for diagnosis of prostate cancer based on radiomics of biparametric magnetic resonance imaging apparent diffusion coefficient maps and stacking of machine learning algorithms

The Use of Apparent Diffusion Coefficient Values for Differentiating Bevacizumab-Related Cytotoxicity from Tumor Recurrence and Radiation Necrosis in Glioblastoma.

Glioblastomas (GBM) are the most common primary invasive neoplasms of the brain. Distinguishing between lesion recurrence and different types of treatment related changes in patients with GBM remains challenging using conventional MRI imaging techniques. Therefore, accurate and precise differentiation between true progression or pseudoresponse is crucial in deciding on the appropriate course of treatment. This retrospective study investigated the potential of apparent diffusion coefficient (ADC) map values derived from diffusion-weighted imaging (DWI) as a noninvasive method to increase diagnostic accuracy in treatment response. A cohort of 21 glioblastoma patients (mean age: 59.2 ± 11.8, 12 Male, 9 Female) that underwent treatment with bevacizumab were selected. The ADC values were calculated from the DWI images obtained from a standardized brain protocol across 1.5-T and 3-T MRI scanners. Ratios were calculated for rADC values. Lesions were classified as bevacizumab-induced cytotoxicity based on characteristic imaging features (well-defined regions of restricted diffusion with persistent diffusion restriction over the course of weeks without tissue volume loss and absence of contrast enhancement). The rADC value was compared to these values in radiation necrosis and recurrent lesions, which were concluded in our prior study. The nonparametric Wilcoxon signed rank test with p < 0.05 was used for significance. The mean ± SD age of the selected patients was 59.2 ± 11.8. ADC values and corresponding mean rADC values for bevacizumab-induced cytotoxicity were 248.1 ± 67.2 and 0.39 ± 0.10, respectively. These results were compared to the ADC values and corresponding mean rADC values of tumor progression and radiation necrosis. Significant differences between rADC values were observed in all three groups (p < 0.001). Bevacizumab-induced cytotoxicity had statistically significant lower ADC values compared to both tumor recurrence and radiation necrosis. The study demonstrates the potential of ADC values as noninvasive imaging biomarkers for differentiating recurrent glioblastoma from radiation necrosis and bevacizumab-induced cytotoxicity.

Multiparametric quantitative magnetic resonance imaging of uterine fibroids for prediction of growth rate-a pilot study.

Uterine fibroid (UF) growth rate and future morbidity cannot be predicted. This can lead to sub-optimal clinical management, with women being lost to follow-up and later presenting with severe disease that may require hospitalization, transfusions, and urgent surgical interventions. Multi-parametric quantitative magnetic resonance imaging (MRI) could provide a biomarker to predict growth rate facilitating better-informed disease management and better clinical outcomes. We assessed the ability of putative quantitative and qualitative MRI predictive factors to predict UF growth rate. Twenty women with UFs were recruited and completed baseline and follow-up MRI exams, 1-2.5 years apart. The subjects filled out symptom severity and health-related quality of life questionnaires at each visit. A standard clinical pelvic MRI non-contrast exam was performed at each visit, followed by a contrast-enhanced multi-parametric quantitative MRI (mp-qMRI) exam with T2, T2*, and apparent diffusion coefficient (ADC) mapping and dynamic contrast-enhanced MRI. Up to 3 largest fibroids were identified and outlined on the T2-weighted sequence. Fibroid morphology and enhancement patterns were qualitatively assessed on dynamic contrast-enhanced MRI. The UFs' volumes and average T2, T2*, and ADC values were calculated. Pearson correlation coefficients were calculated between UF growth rate and T2, T2*, ADC, and baseline volume. Multiple logistic regression and receiver operating characteristic (ROC) analysis were performed to predict fast-growing UFs using combinations of up to 2 significant predictors. A significance level of alpha =0.05 was used. Forty-four fibroids in 20 women had growth rate measurement available, and 36 fibroids in 16 women had follow-up quantitative MRI available. The distribution of fibroid growth rate was skewed, with approximately 20% of the fibroids exhibiting fast growth (>10 cc/year). However, there were no significant changes in median baseline and follow-up values of symptom severity and health-related quality of life scores. There was no change in average T2, T2*, and ADC at follow-up exams and there was a moderate to strong correlation to the fibroid growth rate in baseline volume and average T2 and ADC in slow-growing fibroids (<10 cc/year). A multiple logistic regression to identify fast growing UFs (>10 cc/year) achieved an area under the curve (AUC) of 0.80 with specificity of 69% at 100% sensitivity. The mp-qMRI parameters T2, ADC, and UF volume obtained at the time of initial fibroid diagnosis may be able to predict UF growth rate. Mp-qMRI could be integrated into the management of UFs, for individualized care and improved clinical outcomes.

Comparative Analysis of the Apparent Diffusion Coefficient and Diffusion Tensor Imaging in the Diagnosis of Prostate Cancer.

The aim of this work was to demonstrate capabilities of diffusion tensor imaging as a diagnostic tool for prostate cancer in comparison with the apparent diffusion coefficient. 364 patients with suspected prostate cancer underwent multiparametric magnetic resonance imaging including diffusion tensor imaging. The anatomical structure of the prostate obtained on T2-weighted imaging was compared with the apparent diffusion coefficient and diffusion tensor imaging maps. The rest of the gland (central and peripheral regions) were used as healthy areas. The apparent diffusion coefficient at diffusion-weighted imaging, fractional anisotropy and mean diffusivity at diffusion tensor imaging were evaluated in pathological zones. Cancer-suspicious areas of the prostate had high fractional anisotropy fractional anisotropy and low mean diffusivity compared to unaltered areas. Fractional anisotropy values were significantly elevated in central gland cancer, compared to normal tissue, and slightly elevated in peripheral zone cancer. Diffusion tensor imaging has the potential to identify prostate cancer with high accuracy and specificity. The combination of standard magnetic resonance imaging and diffusion tensor imaging can significantly improve the prognosis of the disease during active surveillance. The fractional anisotropy and mean diffusivity values can be useful in assessing the grade of malignancy and the radiolopathological correlation of the lesion.

Treatment response assessment of acute pyelonephritis: A multi-reader DWI-based MRI approach

PurposeTo evaluate the diagnostic accuracy of a structured reporting score (SRS) in treatment response assessment for acute pyelonephritis (APN) using a diffusion-weighted imaging (DWI) -based MRI approach. Additionally, we explored the influence of reader experience on the interpretation of SRS and DWI, including lesion conspicuity and measurements of Apparent Diffusion Coefficient (ADC) maps. MethodsFollow-up DWI-based MRIs of 36 patients treated for APN between September 2021 and June 2023 were retrospectively reviewed by three readers. Follow-up blood inflammatory markers were used as reference standard. Treatment response was assessed using a structured reporting score (SRS). Each reader assigned a score from 1 to 3 to the "conspicuity" of the residual disease on DWI. Quantitative ADC measurements were compared with the Mann-Whitney U test. Descriptive statistics and Intraclass Correlation Coefficient (ICC) were calculated. ResultsThe diagnostic accuracy of SRS was 80.6 %, 76.9 %, and 72.2 % for the Reader 1, 2, and 3 respectively. ICC decreased from 0.82 (Reader 1 and 2), to 0.68 when considering all readers. The average conspicuity varied between 2.3 and 2.7. ADC values were significantly higher in complete responders for Reader 1 and 2 (153.5-154.5 vs 107.7-116.2, p < 0.001). The ICC was good (0.89) for Reader 1 and 2 and moderate (0.60) when considering all readers. ConclusionsTreatment response of pyelonephritis can be accurately assessed by a DWI-based MRI, potentially avoiding unnecessary contrast agent administration and radiation exposure. SRS and DWI analysis showed a good inter-observer agreement but a certain learning curve may be necessary for less expert readers.

Identifying gastric cancer by optical coherence tomography based on an optimized depth-resolved attenuation estimation

Optical coherence tomography (OCT) is a reliable technique for cancer detection, with the potential to improve accuracy in identifying cancerous tissue through effective use of OCT image data. We proposed an optimized depth resolution estimation based optical attenuation coefficient (OAC) mapping method to reduce the error effect caused by discretization without considering whether the light is completely attenuated or not. The attenuation coefficient maps of gastric tissue were reconstructed using this method. Features were extracted from these maps using gray-level co-occurrence matrix and gray histogram, and a support vector machine was employed as a classifier for identifying gastric cancer tissues. The recognition accuracy achieved was 98.60%, which was higher than the 94.30% accuracy in the control group without OACs. This approach enhances the utilization of OCT image information and improves its practical application ability by increasing the recognition accuracy.

A pretreatment multiparametric MRI-based radiomics-clinical machine learning model for predicting radiation-induced temporal lobe injury in patients with nasopharyngeal carcinoma.

To establish and validate a machine learning model using pretreatment multiparametric magnetic resonance imaging-based radiomics data with clinical data to predict radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT). Data from 230 patients with NPC who received IMRT (130 with RTLI and 130 without) were randomly divided into the training (n = 161) and validation cohort (n = 69) with a ratio of 7:3. Radiomics features were extracted from pretreatment apparent diffusion coefficient (ADC) map, T2-weighted imaging (T2WI), and CE-T1-weighted imaging (CE-T1WI). T-test, spearman rank correlation, and least absolute shrinkage and selection operator (LASSO) algorithm were employed to identify significant radiomics features. Clinical features were selected with univariate and multivariate analyses. Radiomics and clinical models were constructed using multiple machine learning classifiers, and a clinical-radiomics nomogram that combined clinical with radiomics features was developed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were drawn to compare and verify the predictive performances of the clinical model, radiomics model, and clinical-radiomics nomogram. A total of 5064 radiomics features were extracted, from which 52 radiomics features were selected to construct the radiomics signature. The AUC of the radiomics signature based on multiparametric MRI was 0.980 in the training cohort and 0.969 in the validation cohort, outperforming the radiomics signature only based on T2WI and CE-T1WI (p < 0.05), which highlighted the significance of the DWI sequence in the prediction of temporal lobe injury. The area under the curve (AUC) of the clinical model was 0.895 in the training cohort and 0.905 in the validation cohort. The nomogram, which integrated radiomics and clinical features, demonstrated an impressive AUC value of 0.984 in the validation set; however, no statistically significant difference was observed compared to the radiomics model. The calibration curve and decision curve analysis of the nomogram demonstrated excellent predictive performance and clinical feasibility. The clinical-radiomics nomogram, integrating clinical features with radiomics features derived from pretreatment multiparametric MRI, exhibits compelling predictive performance for RTLI in patients diagnosed with NPC.