Fotemustine in recurrent high­grade glioma: MRI neuro­radiological findings.

The use of fotemustine (FTM) has been authorized in certain countries for the treatment of recurrent high-grade gliomas (HGG) after Stupp therapy. However, to the best of our knowledge, no studies have assessed changes in magnetic resonance imaging (MRI) during treatment with FTM monotherapy. The aim of the present study was to assess the neuroradiological findings in a cohort of patients with recurrent HGG treated with FTM monotherapy. Patients with HGG already undergoing the Stupp protocol were retrospectively included. MRIs (pre- and post-FTM treatment) were analyzed by two neuroradiologists in consensus: Volume and diffusion values of the contrast-enhanced component were measured on T1-weighted volumetric sequences after gadolinium injection and on apparent diffusion coefficient (ADC) maps, respectively. A total of 19 patients [median age, 49 years; interquartile range (IQR), 43-57 years] were included, 17 of whom had glioblastoma and 2 had astrocytoma isocitrate dehydrogenase-mutated grade 4. The median duration of FTM therapy was 4 months (IQR, 2-6 months). The median tumor volume measured on the contrast-enhanced component was 2,216 mm3 (IQR, 768-13,169 mm3) at baseline and 9,217 mm3 (IQR, 3,455-16,697 mm3) at the end of treatment, with a median change of +38% (IQR, -45-+574%). A total of seven patients showed a volume decrease. ADC value analysis of the enhancement area demonstrated no significant difference between the pre- and the post-FTM treatment periods (P=0.36); however, in three patients, the decreases in ADC levels were particularly marked. In conclusion, the present study described a series of patients with recurrent HGG treated with FTM in monotherapy, demonstrating a prevalent increase in lesion enhancement and three cases of marked restrictions on diffusion-weighted imaging. Further prospective studies are required to corroborate such preliminary results.

Comparative effectiveness of two abbreviated rectal MRI protocols in assessing tumor response to neoadjuvant chemoradiotherapy in patients with rectal cancer.

The present study aimed to compare the effectiveness of two abbreviated magnetic resonance imaging (MRI) protocols in assessing the response to neoadjuvant chemoradiotherapy (CRT) in patients with rectal cancer. Data from the examinations of 62 patients with rectal cancer who underwent neoadjuvant CRT and standard contrast-enhanced rectal MRI were retrospectively evaluated. Standard contrast-enhanced T2-weighted imaging (T2-WI), post-contrast T1-weighted imaging (T1-WI) and diffusion-weighted imaging (DWI) MRI, as well as two abbreviated protocols derived from these images, namely protocol AB1 (T2-WI and DWI) and protocol AB2 (post-contrast fat-suppressed (FS) T1-WI and DWI), were assessed. Measurements of lesion length and width, lymph node short-axis length, tumor staging, circumferential resection margin (CRM), presence of extramural venous invasion (EMVI), luminal mucin accumulation (MAIN), mucinous response, mesorectal fascia (MRF) involvement, and MRI-based tumor regression grade (mrTRG) were obtained. The reliability and compatibility of the AB1 and AB2 protocols in the evaluation of tumor response were analyzed. The imaging performed according to the AB1 and AB2 protocols revealed significant decreases in lesion length, width and lymph node size after CRT. These protocols also showed reductions in lymph node positivity, CRM, MRF, EMVI.Furthermore, both protocols were found to be reliable in determining lesion length and width. Additionally, compliance was observed between the protocols in determining lymph node size and positivity, CRM involvement, and EMVI after CRT. In conclusion, the use of abbreviated MRI protocols, specifically T2-WI with DWI sequences or post-contrast FS T1-WI with DWI sequences, is effective for evaluating tumor response in patients with rectal cancer following neoadjuvant CRT. The AB protocols examined in this study yielded similar results in terms of lesion length and width, lymph node positivity, CRM involvement, EMVI, MAIN, and MRF involvement.

Clinical value of a radiomics model based on machine learning for the prediction of prostate cancer.

OBJECTIVE: Radiomics models have demonstrated good performance for the diagnosis and evaluation of prostate cancer (PCa). However, there are currently no validated imaging models that can predict PCa or clinically significant prostate cancer (csPCa). Therefore, we aimed to identify the best such models for the prediction of PCa and csPCa. METHODS: We performed a retrospective study of 942 patients with suspected PCa before they underwent prostate biopsy. MRI data were collected to manually segment suspicious regions of the tumor layer-by-layer. We then constructed models using the extracted imaging features. Finally, the clinical value of the models was evaluated. RESULTS: A diffusion-weighted imaging (DWI) plus apparent diffusion coefficient (ADC) random-forest model and a T2-weighted imaging plus ADC and DWI multilayer perceptron model were the best models for the prediction of PCa and csPCa, respectively. Areas under the curve (AUCs) of 0.942 and 0.999, respectively, were obtained for a training set. Internal validation yielded AUCs of 0.894 and 0.605, and external validation yielded AUCs of 0.732 and 0.623. CONCLUSION: Models based on machine learning comprising radiomic features and clinical indicators showed good predictive efficiency for PCa and csPCa. These findings demonstrate the utility of radiomic models for clinical decision-making.

Quantitative MRI biomarker for classification of clinically significant prostate cancer: Calibration for reproducibility across echo times.

PURPOSE: The purpose of the present study is to develop a calibration method to account for differences in echo times (TE) and facilitate the use of restriction spectrum imaging restriction score (RSIrs) as a quantitative biomarker for the detection of clinically significant prostate cancer (csPCa). METHODS: This study included 197 consecutive patients who underwent MRI and biopsy examination; 97 were diagnosed with csPCa (grade group ≥ 2). RSI data were acquired three times during the same session: twice at minimum TE ~75 ms and once at TE = 90 ms (TEmin1, TEmin2, and TE90, respectively). A linear regression model was determined to match the C-maps of TE90 to the reference C-maps of TEmin1 within the interval ranging from 95th to 99th percentile of signal intensity within the prostate. RSIrs comparisons were made at the 98th percentile within each patient's prostate. We compared RSIrs from calibrated TE90 (RSIrsTE90corr) and uncorrected TE90 (RSIrsTE90) to RSIrs from reference TEmin1 (RSIrsTEmin1) and repeated TEmin2 (RSIrsTEmin2). Calibration performance was evaluated with sensitivity, specificity and area under the ROC curve (AUC). RESULTS: Scaling factors for C1, C2, C3, and C4 were estimated as 1.68, 1.33, 1.02, and 1.13, respectively. In non-csPCa cases, the 98th percentile of RSIrsTEmin2 and RSIrsTEmin1 differed by 0.27 ± 0.86SI (mean ± standard deviation), whereas RSIrsTE90 differed from RSIrsTEmin1 by 1.82 ± 1.20SI. After calibration, this bias was reduced to -0.51 ± 1.21SI, representing a 72% reduction in absolute error. For patients with csPCa, the difference was 0.54 ± 1.98SI between RSIrsTEmin2 and RSIrsTEmin1 and 2.28 ± 2.06SI between RSIrsTE90 and RSIrsTEmin1. After calibration, the mean difference decreased to -1.03SI, a 55% reduction in absolute error. At the Youden index for patient-level classification of csPCa (8.94SI), RSIrsTEmin1 has a sensitivity of 66% and a specificity of 72%. CONCLUSIONS: The proposed linear calibration method produces similar quantitative biomarker values for acquisitions with different TE, reducing TE-induced error by 72% and 55% for non-csPCa and csPCa, respectively.

Comparative analysis of amide proton transfer and diffusion-weighted imaging for assessing Ki-67, p53 and PD-L1 expression in bladder cancer.

RATIONALE AND OBJECTIVES: To evaluate amide proton transfer (APT) imaging for assessing Ki-67, p53 and PD-L1 status in bladder cancer (BC) and compare its diagnostic efficacy with that of diffusion-weighted imaging (DWI). MATERIALS AND METHODS: Consecutive patients suspected of BC were recruited for preoperative multiparametric MRI. APT signal was quantified by asymmetric magnetization transfer ratio (MTRasym). MTRasym and apparent diffusion coefficient (ADC) were measured by two radiologists, with interobserver agreement assessed. Spearman's correlation analyzed MTRasym values and molecular markers. The Whitney U test evaluated MTRasym and ADC variation based on molecular marker status. Optimal cutoff points were determined using area under the curve (AUC) analysis. RESULTS: 88 patients (72 ± 10 years; 77 men) with BC were studied. MTRasym values were significantly correlated with Ki-67, p53 and PD-L1 levels (P < 0.05). Higher MTRasym values were found in high Ki-67 expression BCs (1.89% [0.73%] vs. 1.23% ± 0.26%; P < 0.001), high p53 expression BCs (1.63% [0.56%] vs. 1.24% [0.56%]; P < 0.001) and positive PD-L1 expression BCs (2.02% [0.81%] vs. 1.48% [0.38%]; P < 0.001). Lower ADCs were found in high Ki-67 expression BCs (1.06 ×10-3 mm2/s [0.32 ×10-3 mm2/s] vs. 1.38 ×10-3 mm2/s [0.39 ×10-3 mm2/s]; P < 0.001). For p53 status, an MTRasym threshold of 1.27% had 95% sensitivity, 60% specificity, and AUC of 0.781. For PD-L1 status, a 1.90% threshold had 88% sensitivity, 92% specificity, and AUC of 0.859. CONCLUSION: APT may significantly enhance the preoperative assessment of BC aggressiveness and inform targeted immunotherapy decisions, with performance superior to DWI.

Noninvasive assessment of single kidney glomerular filtration rate using multiple diffusion weighted imaging models.

PURPOSE: This study aimed to assess single kidney glomerular filtration rate (GFR) using various diffusion weighted imaging (DWI) models. METHODS: We reviewed adult patients with kidney diseases who underwent magnetic resonance imaging (MRI) examination from February 2021 to December 2023. DWI with 13 b-values was performed using 3.0-T scanners. Diffusion parameters were calculated with multi-slice ROIs positioned in renal parenchyma using four DWI models, including monoexponential model (MEM), diffusion kurtosis imaging (DKI), stretched exponential model (SEM), and intravoxel incoherent motion (IVIM). The split GFRs were measured by 99mTc-DTPA scintigraphy using Gates' method. Four different regression algorithms including the linear regression, regression tree, Gaussian regression and support vector machine (SVM) regression were employed to predict the GFR value based on different diffusion parameters. The leave-one-out cross validation was used to evaluate prediction ability of different models, and the performance of each model was quantified using the root mean square error (RMSE) and correlation coefficient. RESULTS: Fifteen (male/female, 10/5; age, 41.60±10.83 years) patients were included in this study. Among the four DWI models, the IVIM parameters with SVM regression model achieved the best performance with 0.184 RMSE and 0.789 correlation coefficient ( p < 0.001 ). The parameters combining the four DWI models with SVM regression algorithm achieved the best performance in this study, with 0.171 RMSE and 0.815 correlation coefficient ( p < 0.001 ). CONCLUSION: The DWI characteristics are able to serve as imaging biomarkers for assessing the function of single kidney. The integration of DWI into clinical practice could contribute to the advancement of non-invasive diagnostic methodologies.

Advancing gastrointestinal stromal tumor management: The role of imagomics features in precision risk assessment.

BACKGROUND: Gastrointestinal stromal tumors (GISTs) vary widely in prognosis, and traditional pathological assessments often lack precision in risk stratification. Advanced imaging techniques, especially magnetic resonance imaging (MRI), offer potential improvements. This study investigates how MRI imagomics can enhance risk assessment and support personalized treatment for GIST patients. AIM: To assess the effectiveness of MRI imagomics in improving GIST risk stratification, addressing the limitations of traditional pathological assessments. METHODS: Analyzed clinical and MRI data from 132 GIST patients, categorizing them by tumor specifics and dividing into risk groups. Employed dimension reduction for optimal imagomics feature selection from diffusion-weighted imaging (DWI), T1-weighted imaging (T1WI), and contrast enhanced T1WI with fat saturation (CE-T1WI) fat suppress (fs) sequences. RESULTS: Age, lesion diameter, and mitotic figures significantly correlated with GIST risk, with DWI sequence features like sphericity and regional entropy showing high predictive accuracy. The combined T1WI and CE-T1WI fs model had the best predictive efficacy. In the test group, the DWI sequence model demonstrated an area under the curve (AUC) value of 0.960 with a sensitivity of 80.0% and a specificity of 100.0%. On the other hand, the combined performance of the T1WI and CE-T1WI fs models in the test group was the most robust, exhibiting an AUC value of 0.834, a sensitivity of 70.4%, and a specificity of 85.2%. CONCLUSION: MRI imagomics, particularly DWI and combined T1WI/CE-T1WI fs models, significantly enhance GIST risk stratification, supporting precise preoperative patient assessment and personalized treatment plans. The clinical implications are profound, enabling more accurate surgical strategy formulation and optimized treatment selection, thereby improving patient outcomes. Future research should focus on multicenter studies to validate these findings, integrate advanced imaging technologies like PET/MRI, and incorporate genetic factors to achieve a more comprehensive risk assessment.

Acquisition and reconstruction with motion suppression DWI enhance image quality in nasopharyngeal carcinoma patients: Non-echo-planar DWI comparison with single-shot echo-planar DWI.

PURPOSE: To evaluate the impact of application acquisition and reconstruction with motion suppression (ARMS) technology on improving the image quality of diffusion-weighted Imaging (DWI) for nasopharyngeal carcinoma (NPC), compared to single-shot echo-planar imaging (SS-EPI). METHODS: A total of 90 patients with NPC underwent MR examination, including ARMS DWI and SS-EPI DWI sequences. Both DWI sequences were acquired with b-values 0 and 800 s/mm2. Two radiologists evaluated the visibility of the lesion, geometric distortion, and overall image quality of the two DWI sequences. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), geometric distortion degree, and apparent diffusion coefficient (ADC) values of the nasopharyngeal lesions were assessed and compared for two sequences. The Wilcoxon signed-rank test was used to compare the quantitative and qualitative parameters of the two sequences. RESULTS: The lesion visibility, geometric distortion, and overall image quality scores were significantly higher in ARMS DWI (all P<0.001). Four small-sized lesions were not visible and four lesions were partially visible in the SS-EPI DWI sequence. Lesion detection rate of ARMS DWI is 100 %, while that of SS-EPI is 95.56 %, P<0.043. The mismatch distance between the fusion images of ARMS DWI and T2WI was smaller than that of SS-EPI DWI and T2WI (all P<0.001). The SNR and CNR of ARMS DWI were lower than that of SS-EPI DWI (114.48 ± 37.89 vs. 202.61 ± 78.84, P<0.001 and 1.81 ± 1.84 vs. 3.29 ± 3.71, P<0.003) while the ADC value was higher (839.19 ± 138.44 × 10-6 mm2/s vs. 788.82 ± 110.96 × 10-6 mm2/s, P<0.002). CONCLUSION: ARMS DWI improves the image quality by reducing geometric distortion and magnetic susceptibility artifacts. ARMS DWI is superior to SS-EPI DWI for diagnosing small-sized nasopharyngeal lesions, although it has lower SNR and CNR.

Assessment of chemical-shift and diffusion-weighted magnetic resonance imaging in differentiating malignant and benign vertebral lesions in oncologic patients. A single institution experience.

BACKGROUND: To analyze the contribution of two non-standard magnetic resonance imaging (MRI) techniques the chemical-shift image (CSI), and diffusion-weighted imaging (DWI) in distinguishing malignant and benign vertebral bone marrow lesions (VBMLs). PATIENTS AND METHODS: Conventional spine MRI protocol, followed by CSI and DWI was performed with a 1.5 T system on 102 oncologic patients between January 2020 and December 2023. From the identified 325 VBMLs, 102 representative lesions (one per patient) were selected. VBMLs were divided into malignant (n = 74) and benign (n = 28) based on histopathology, or imaging follow-up. The quantitative parameters for VBMLs assessment were signal intensity ratio (SIR) derived from CSI and apparent diffusion coefficient (ADC) derived from DWI. RESULTS: The malignant VBMLs had significantly higher SIR values (p < 0.05) and lower ADC values compared to benign VBMLs (p < 0.05). The area under the curve (AUC) was 0.953 (p < 0.001) for SIR, and 0.894 for ADC (p < 0.001) (cut-off at > 0.82, and ≤ 1.57x10-3 mm2/s, respectively). The sensitivity and specificity for SIR were 93.6%, and 88.5%, while for ADC were 88.2% and 92.3% (respectively). The combined use of SIR and ADC improved the diagnostic accuracy to AUC of 0.988 (p < 0.001, cut-off at > 0.19), sensitivity, and specificity of 100.0% and 90.9% (respectively). CONCLUSIONS: Quantitative parameters, SIR and ADC, derived from two non-standard MRI techniques, CSI, and DWI, showed diagnostic strength in differentiating malignant and benign VBMLs. Combining both methods can further enhance the diagnostic performance and accuracy of spine MRI in clinical practice.

Characteristics and temporal evolution of asymptomatic diffusion-weighted imaging lesions in patients with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL).

BACKGROUND AND PURPOSE: The role of asymptomatic diffusion-weighted imaging-positive (aDWI+) lesions in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) patients remains unclear, and their radiographic features may differ from those of symptomatic diffusion-weighted imaging-positive (sDWI+) lesions. We aimed to investigate the clinicoradiographic characteristics of aDWI+ lesions in CADASIL patients. METHODS: We conducted a retrospective analysis using data from the Taiwan CADASIL Registry. aDWI+ lesions were defined as incidentally detected DWI+ lesions without corresponding acute neurological deficits. We compared the baseline clinical characteristics of patients with and without aDWI+ lesions and analyzed their radiological features and evolution in relation to sDWI+ lesions. RESULTS: Among 154 enrolled patients (mean age 62 ± 10 years), 17 (11%) had aDWI+ lesions. Baseline clinical characteristics were similar in the two groups, but those with aDWI+ lesions had more lacunes (median 8 vs. 2), multiple cerebral microbleeds (CMBs; 85% vs. 40%), and anterior temporal white matter hyperintensity (WMH; 47% vs. 14%). Multivariable analysis showed that aDWI+ lesions were associated with anterior temporal WMH (odds ratio 5.7, 95% confidence interval 1.5-21.0) after adjusting for multiple lacunes, multiple CMBs, and total WMH score. Compared to sDWI+ lesions, aDWI+ lesions were more often small infarcts (<1 cm; 89% vs. 23%) and less likely to involve the corticospinal tract (11% vs. 96%). Among the 11 aDWI+ lesions with follow-up magnetic resonance imaging, seven became microinfarcts, three became lacunes, and one disappeared. CONCLUSIONS: aDWI+ lesions in CADASIL are not uncommon and are associated with higher burdens of small vessel disease and anterior temporal WMH. Further research is needed to assess their long-term impact on CADASIL.

The diagnostic value of histogram analysis of DWI and DKI for the mismatch repair status of rectal adenocarcinoma.

Objectives: To compare the diagnostic value of histogram analysis derived from diffusion weighted imaging (DWI) and diffusion kurtosis imaging (DKI) in differentiating the mismatch repair (MMR) status of rectal adenocarcinoma. Methods: DWI and DKI were performed in 124 patients with rectal adenocarcinoma, which were divided into deficient mismatch repair (dMMR) group and proficient mismatch repair (pMMR) group. The patients' general clinical information, pathology and image characteristics were compared. The histogram analysis of apparent diffusion coefficient (ADC), diffusion kurtosis (K) and diffusion coefficient (D)derived from DWI and DKI at b values of 1000 and 2000 s/mm2 were calculated. The diagnostic efficacy of quantitative parameters for MMR in rectal adenocarcinoma was compared. Results: The mean, 50th, 75th and 90th in ADC quantitative parameters of dMMR group were lower when the b value was 2000 s/mm2 (all P < 0.05). With b value of 1000 s/mm2, the 10th, 25th, and 50th in the dMMR group were lower, and the skewness was higher (all P < 0.05). D values (10th, 25th and 50th) derived from DKI quantitative parameters were lower in the dMMR group. The K values (75th, 90th and Kskewness) were higher in the dMMR group, while Kkurtosis was lower (all P < 0.05). The results of multivariate logistic regression analysis showed that ADC75th(b = 2000 s/mm2), ADCskewness (b = 1000 s/mm2) and Kskewness were the statistical significant parameters (P = 0.014, 0.036 and 0.002, respectively), and the AUC values were 0.713, 0.818 and 0.835, respectively. Conclusion: Histogram analysis derived from DWI and DKI can be good predictor of MMR. Kskewness is the strongest independent factor for predicting MMR.

Predictive value of mono-exponential and multiple mathematical models in locally advanced rectal cancer response to neoadjuvant chemoradiotherapy.

PURPOSE: This prospective study aimed to assess the predictive value of mono-exponential and multiple mathematical diffusion-weighted imaging (DWI) models in determining the response to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). METHODS: The study included 103 LARC patients scheduled for preoperative chemoradiotherapy between December 2021 and June 2023 Magnetic resonance imaging (MRI) scans were performed using a 3.0-T MR scanner, encompassing sagittal, axial, and oblique coronal T2-weighted images without fat saturation, along with DWI perpendicular to the rectum's long axis. Various DWI parameters, including apparent diffusion coefficient (ADC), stretched exponential model (SEM), continuous-time random-walk model (CTRW), and fractional-order calculus model (FROC), were measured. The pathologic complete response (pCR) rate and tumor downstaging (T-downstage) rate were determined. RESULTS: After nCRT, SEM-α, SEM-DDC, CTRW-α, CTRW-ß, CTRW-D, FROC-ß, and ADC values were significantly higher in the pCR group compared to the non-pCR group (all P < 0.05). SEM-DDC, CTRW-α, CTRW-D, FROC-ß, FROC-µ, and ADC values were significantly higher in the T-downstage group (ypT0-1) than in the non-T-downstage group (ypT2-4) (P < 0.05). The combination of CTRW (α + ß + D) exhibited the best diagnostic performance for assessing pCR after nCRT (AUC = 0.840, P < 0.001). Pre-nCRT CTRW (α + ß) demonstrated a predictive AUC of 0.652 (95%CI: 0.552-0.743), 90.3% sensitivity, and 43.1% specificity for pCR. Regarding T-downstage assessment after nCRT, the combination of CTRW (α + D) yielded the best diagnostic performance (AUC = 0.877, P = 0.048). CONCLUSION: In LARC patients, imaging markers derived from CTRW show promise in predicting tumor response before nCRT and assessing pCR after nCRT.

Imaging evaluation focused on microstructural tissue changes using tensor-valued diffusion encoding in breast cancers after neoadjuvant chemotherapy: is it a promising way forward?

Background: Single diffusion encoding is a widely used, noninvasive technique for probing the tissue microstructure in breast tumors. However, it does not provide detailed information about the microenvironmental complexity. This study investigated the clinical utility of tensor-valued diffusion encoding for evaluating microstructural changes in breast cancer after neoadjuvant chemotherapy (NAC). Methods: We retrospectively included patients underwent chemotherapy for histologically proven invasive breast cancer between July 2020 and June 2023 and monitored the tumor response with breast magnetic resonance imaging (MRI), including tensor-valued diffusion encoding. We reviewed pre- and post-NAC MRIs regarding chemotherapy in 23 breast cancers. Q-space trajectory imaging (QTI) parameters were estimated at each time-point, and were compared with histopathological parameters. Results: The mean total mean kurtosis (MKT), anisotropic mean kurtosis (MKA), and microscopic fractional anisotropy (µFA) were significantly decreased on post-NAC MRI compared with pre-NAC MRI, with the large effect size (ES) in MKA and µFA (0.81±0.41 vs. 0.99±0.33, ES: 0.48, P=0.03; 0.48±0.30 vs. 0.73±0.27, ES: 0.88, P<0.001; 0.58±0.14 vs. 0.68±0.11, ES: 0.79, P=0.003; respectively). Regarding prognostic factors, tumors with high Ki-67 expression showed significantly lower pre-NAC mean diffusivity (MD) and higher pre-NAC µFA compared to tumors with low Ki-67 expression (0.98±0.09 vs. 1.25±0.20, P=0.002; and 0.72±0.07 vs. 0.57±0.10, P=0.005; respectively). And negative progesterone receptor (PR) group revealed significantly lower MKT, MKA, and isotropic mean kurtosis than positive PR group on the post-NAC MRI (0.60±0.31 vs. 1.03±0.40, P=0.008; 0.36±0.21 vs. 0.61±0.33, P=0.04; and 0.23±0.17 vs. 0.42±0.25, P=0.046; respectively). Conclusions: QTI parameters reflected the microstructural changes in breast cancer treated with NAC and can be used as noninvasive imaging biomarkers correlated with prognostic factors.

Microstructural and functional substrates underlying dispositional greed and its link with trait but not state impulsivity.

The interplay between personality traits and impulsivity has long been a central theme in psychology and psychiatry. However, the potential association between Greed Personality Traits (GPT) and impulsivity, encompassing both trait and state impulsivity and future time perspective, remains largely unexplored. To address these issues, we employed questionnaires and an inter-temporal choice task to estimate corresponding trait/state impulsivity and collected multi-modal neuroimaging data (resting-state functional imaging: n = 430; diffusion-weighted imaging: n = 426; task-related functional imaging: n = 53) to investigate the underlying microstructural and functional substrates. Behavioral analyses revealed that GPT mediated the association between time perspective (e.g., present fatalism) and trait impulsivity (e.g., motor impulsivity). Functional imaging analyses further identified that brain activation strengths and patterns related to delay length, particularly in the dorsomedial prefrontal cortex, superior parietal lobule, and cerebellum, were associated with GPT. Moreover, individuals with similar levels of greed exhibited analogous spontaneous brain activity patterns, predominantly in the Default Mode Network (DMN), Fronto-Parietal Network (FPN), and Visual Network (VIS). Diffusion imaging analysis observed specific microstructural characteristics in the spinocerebellar/pontocerebellar fasciculus, internal/external capsule, and corona radiata that support the formation of GPT. Furthermore, the corresponding neural activation pattern, spontaneous neural activity pattern, and analogous functional couplings among the aforementioned brain regions mediated the relationships between time perspective and GPT and between GPT and motor impulsivity. These findings provide novel insights into the possible pathway such as time perspective → dispositional greed → impulsivity and uncover their underlying microstructural and functional substrates.

Prospective Comparison of FOCUS MUSE and Single-Shot Echo-Planar Imaging for Diffusion-Weighted Imaging in Evaluating Thyroid-Associated Ophthalmopathy.

OBJECTIVE: To prospectively compare single-shot (SS) echo-planar imaging (EPI) and field-of-view optimized and constrained undistorted single-shot multiplexed sensitivity-encoding (FOCUS MUSE) for diffusion-weighted imaging (DWI) in evaluating thyroid-associated ophthalmopathy (TAO). MATERIALS AND METHODS: SS EPI and FOCUS MUSE DWIs were obtained from 39 patients with TAO (18 male; mean ± standard deviation: 48.3 ± 13.3 years) and 26 healthy controls (9 male; mean ± standard deviation: 43.0 ± 18.5 years). Two radiologists scored the visual image quality using a 4-point Likert scale. The image quality score, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) of extraocular muscles (EOMs) were compared between the two DWIs. Differences in the ADC of EOMs were also evaluated. The performance of discriminating active from inactive TAO was assessed using receiver operating characteristic curves. The correlation between ADC and clinical activity score (CAS) was analyzed using Spearman correlation. RESULTS: Compared with SS EPI DWI, FOCUS MUSE DWI demonstrated significantly higher image quality scores (P < 0.001), a higher SNR and CNR on the lateral rectus muscle (LRM) and medial rectus muscle (MRM) (P < 0.05), and a non-significant difference in the ADC of the LRM and MRM. Active TAO showed higher ADC than inactive TAO and healthy controls with both SS EPI and FOCUS MUSE DWIs (P < 0.001). Inactive TAO and healthy controls did not show a significant ADC difference with both DWIs. Compared with SS EPI DWI, FOCUS MUSE DWI demonstrated better discrimination of active from inactive TAO (AUC: 0.925 vs. 0.779; P = 0.007). The ADC was significantly correlated with CAS in SS EPI DWI (r = 0.391, P < 0.001) and FOCUS MUSE DWI (r = 0.645, P < 0.001). CONCLUSION: FOCUS MUSE DWI provides better images for evaluating EOMs and better performance in diagnosing active TAO than SS EPI DWI. The application of FOCUS MUSE will facilitate the DWI evaluation of TAO.

Demystifying the challenging diagnosis of post-radiation nasopharyngeal necrosis on multimodality imaging.

Post-radiation nasopharyngeal necrosis (PRNN) is a rare but life-threatening condition that often poses a diagnostic challenge in imaging studies owing to its overlapping features with recurrent nasopharyngeal tumours. We herein describe the characteristic imaging appearance of PRNN on post-contrast T1-weighted magnetic resonance imaging, diffusion-weighted imaging (DWI) and fluorodeoxyglucose (FDG)-PET/CT which may provide insights into its pathological findings.

Deep Learning Classification of Ischemic Stroke Territory on Diffusion-Weighted MRI: Added Value of Augmenting the Input with Image Transformations.

Our primary aim with this study was to build a patient-level classifier for stroke territory in DWI using AI to facilitate fast triage of stroke to a dedicated stroke center. A retrospective collection of DWI images of 271 and 122 consecutive acute ischemic stroke patients from two centers was carried out. Pretrained MobileNetV2 and EfficientNetB0 architectures were used to classify territorial subtypes as middle cerebral artery, posterior circulation, or watershed infarcts along with normal slices. Various input combinations using edge maps, thresholding, and hard attention versions were explored. The effect of augmenting the three-channel inputs of pre-trained models on classification performance was analyzed. ROC analyses and confusion matrix-derived performance metrics of the models were reported. Of the 271 patients included in this study, 151 (55.7%) were male and 120 (44.3%) were female. One hundred twenty-nine patients had MCA (47.6%), 65 patients had posterior circulation (24%), and 77 patients had watershed (28.0%) infarcts for center 1. Of the 122 patients from center 2, 78 (64%) were male and 44 (34%) were female. Fifty-two patients (43%) had MCA, 51 patients had posterior circulation (42%), and 19 (15%) patients had watershed infarcts. The Mobile-Crop model had the best performance with 0.95 accuracy and a 0.91 mean f1 score for slice-wise classification and 0.88 accuracy on external test sets, along with a 0.92 mean AUC. In conclusion, modified pre-trained models may be augmented with the transformation of images to provide a more accurate classification of affected territory by stroke in DWI.

Diffusion-Weighted Magnetic Resonance Imaging for the Diagnosis of Lymph Node Metastasis in Patients with Biliary Tract Cancer.

Objective: The diagnostic efficacy of the apparent diffusion coefficient (ADC) in diffusion-weighted magnetic resonance imaging (DW-MRI) for lymph node metastasis in biliary tract cancer was investigated in the present study. Methods: In total, 112 surgically resected lymph nodes from 35 biliary tract cancer patients were examined in this study. The mean and minimum ADC values of the lymph nodes as well as the long-axis and short-axis diameters of the lymph nodes were assessed by computed tomography (CT). The relationship between these parameters and the presence of histological lymph node metastasis was evaluated. Results: Histological lymph node metastasis was detected in 31 (27.7%) out of 112 lymph nodes. Metastatic lymph nodes had a significantly larger short-axis diameter compared with non-metastatic lymph nodes (p = 0.002), but the long-axis diameter was not significantly different between metastatic and non-metastatic lymph nodes. The mean and minimum ADC values for metastatic lymph nodes were significantly reduced compared with those for non-metastatic lymph nodes (p < 0.001 for both). However, the minimum ADC value showed the highest accuracy for the diagnosis of histological lymph node metastasis, with an area under the curve of 0.877, sensitivity of 87.1%, specificity of 82.7%, and accuracy of 83.9%. Conclusions: The minimum ADC value in DW-MRI is highly effective for the diagnosis of lymph node metastasis in biliary tract cancer. Accurate preoperative diagnosis of lymph node metastasis in biliary tract cancer should enable the establishment of more appropriate treatment strategies.

Machine learning with multiple modalities of brain magnetic resonance imaging data to identify the presence of bipolar disorder.

BACKGROUND: Bipolar disorder (BD) is a chronic psychiatric mood disorder that is solely diagnosed based on clinical symptoms. These symptoms often overlap with other psychiatric disorders. Efforts to use machine learning (ML) to create predictive models for BD based on data from brain imaging are expanding but have often been limited using only a single modality and the exclusion of the cerebellum, which may be relevant in BD. METHODS: In this study, we sought to improve ML classification of BD by combining information from structural, functional, and diffusion-weighted imaging. Participants (108 BD I, 78 control) with BD type I and matched controls were recruited into an imaging study. This dataset was randomly divided into training and testing sets. For each of the three modalities, a separate ML model was selected, trained, and then used to generate a prediction of the class of each test subject. Majority voting was used to combine results from the three models to make a final prediction of whether a subject had BD. An independent replication sample was used to evaluate the ability of the ML classification to generalize to data collected at other sites. RESULTS: Combining the three machine learning models through majority voting resulted in an accuracy of 89.5 % for classification of the test subjects as being in the BD or control group. Bootstrapping resulted in a 95 % confidence interval of 78.9 %-97.4 % for test accuracy. Performance was reduced when only using 2 of the 3 modalities. Analysis of feature importance revealed that the cerebellum and nodes of the emotional control network were among the most important regions for classification. The machine learning model performed at chance on the independent replication sample. CONCLUSION: BD I could be identified with high accuracy in our relatively small sample by combining structural, functional, and diffusion-weighted imaging data within a single site but not generalize well to an independent replication sample. Future studies using harmonized imaging protocols may facilitate generalization of ML models.

Single-shot multi-b-value (SSMb) diffusion-weighted MRI using spin echo and stimulated echoes with variable flip angles.

Conventional diffusion-weighted imaging (DWI) sequences employing a spin echo or stimulated echo sensitize diffusion with a specific b-value at a fixed diffusion direction and diffusion time (Δ). To compute apparent diffusion coefficient (ADC) and other diffusion parameters, the sequence needs to be repeated multiple times by varying the b-value and/or gradient direction. In this study, we developed a single-shot multi-b-value (SSMb) diffusion MRI technique, which combines a spin echo and a train of stimulated echoes produced with variable flip angles. The method involves a pair of 90° radio frequency (RF) pulses that straddle a diffusion gradient lobe (GD), to rephase the magnetization in the transverse plane, producing a diffusion-weighted spin echo acquired by the first echo-planar imaging (EPI) readout train. The magnetization stored along the longitudinal axis is successively re-excited by a series of n variable-flip-angle pulses, each followed by a diffusion gradient lobe GD and a subsequent EPI readout train to sample n stimulated-echo signals. As such, (n + 1) diffusion-weighted images, each with a distinct b-value, are acquired in a single shot. The SSMb sequence was demonstrated on a diffusion phantom and healthy human brain to produce diffusion-weighted images, which were quantitative analyzed using a mono-exponential model. In the phantom experiment, SSMb provided similar ADC values to those from a commercial spin-echo EPI (SE-EPI) sequence (r = 0.999). In the human brain experiment, SSMb enabled a fourfold scan time reduction and yielded slightly lower ADC values (0.83 ± 0.26 µm2/ms) than SE-EPI (0.88 ± 0.29 µm2/ms) in all voxels excluding cerebrospinal fluid, likely due to the influence of varying diffusion times. The feasibility of using SSMb to acquire multiple images in a single shot for intravoxel incoherent motion (IVIM) analysis was also demonstrated. In conclusion, despite a relatively low signal-to-noise ratio, the proposed SSMb technique can substantially increase the data acquisition efficiency in DWI studies.