Dual-Hit Strategy for Therapeutic Targeting of Pancreatic Cancer in Patient-Derived Xenograft Tumors

PURPOSE

Paracrine activation of pro-fibrotic hedgehog (HH) signaling in pancreatic ductal adenocarcinoma (PDAC) results in stromal amplification that compromises tumor drug delivery, efficacy, and patient survival. Interdiction of HH-mediated tumor-stroma crosstalk with smoothened (SMO) inhibitors (SHHi) "primes" PDAC patient-derived xenograft (PDX) tumors for increased drug delivery by transiently increasing vascular patency/permeability, and thereby macromolecule delivery. However, patient tumor isolates vary in their responsiveness, and responders show co-induction of epithelial-mesenchymal transition (EMT). We aimed to identify the signal derangements responsible for EMT induction and reverse them and devise approaches to stratify SHHi-responsive tumors noninvasively based on clinically-quantifiable parameters.

EXPERIMENTAL DESIGN

Animals underwent diffusion-weighted magnetic resonance (DW-MR) imaging for measurement of intratumor diffusivity. In parallel, tissue-level deposition of nanoparticle probes was quantified as a marker of vascular permeability/perfusion. Transcriptomic and bioinformatic analysis was employed to investigate SHHi-induced gene reprogramming and identify key "nodes" responsible for EMT induction.

RESULTS

Multiple patient tumor isolates responded to short-term SHH inhibitor exposure with increased vascular patency and permeability, with proportionate increases in tumor diffusivity. Nonresponding PDXs did not. SHHi-treated tumors showed elevated FGF drive and distinctly higher nuclear localization of fibroblast growth factor receptor (FGFR1) in EMT-polarized tumor cells. Pan-FGFR inhibitor NVP-BGJ398 (Infigratinib) reversed the SHHi-induced EMT marker expression and nuclear FGFR1 accumulation without compromising the enhanced permeability effect.

CONCLUSIONS

This dual-hit strategy of SMO and FGFR inhibition provides a clinically-translatable approach to compromise the profound impermeability of PDAC tumors. Furthermore, clinical deployment of DW-MR imaging could fulfill the essential clinical-translational requirement for patient stratification.

Abdominal Diffusion-Weighted MRI With Simultaneous Multi-Slice Acquisition: Agreement and Reproducibility of Apparent Diffusion Coefficients Measurements

BACKGROUND

Simultaneous multi-slice diffusion-weighted imaging (SMS-DWI) can shorten acquisition time in abdominal imaging.

PURPOSE

To investigate the agreement and reproducibility of apparent diffusion coefficient (ADC) from abdominal SMS-DWI acquired with different vendors and different breathing schemes.

STUDY TYPE

Prospective.

SUBJECTS

Twenty volunteers and 10 patients.

FIELD STRENGTH/SEQUENCE

3.0 T, SMS-DWI with a diffusion-weighted echo-planar imaging sequence.

ASSESSMENT

SMS-DWI was acquired using breath-hold and free-breathing techniques in scanners from two vendors, yielding four scans in each participant. Average ADC values were measured in the liver, pancreas, spleen, and both kidneys. Non-normalized ADC and ADCs normalized to the spleen were compared between vendors and breathing schemes.

STATISTICAL TESTS

Paired t-test or Wilcoxon signed rank test; intraclass correlation coefficient (ICC); Bland-Altman method; coefficient of variation (CV) analysis; significance level: P < 0.05.

RESULTS

Non-normalized ADCs from the four SMS-DWI scans did not differ significantly in the spleen (P = 0.262, 0.330, 0.166, 0.122), right kidney (P = 0.167, 0.538, 0.957, 0.086), and left kidney (P = 0.182, 0.281, 0.504, 0.405), but there were significant differences in the liver and pancreas. For normalized ADCs, there were no significant differences in the liver (P = 0.315, 0.915, 0.198, 0.799), spleen (P = 0.815, 0.689, 0.347, 0.423), pancreas (P = 0.165, 0.336, 0.304, 0.584), right kidney (P = 0.165, 0.336, 0.304, 0.584), and left kidney (P = 0.496, 0.304, 0.443, 0.371). Inter-reader agreements of non-normalized ADCs were good to excellent (ICCs ranged from 0.861 to 0.983), and agreement and reproducibility were good to excellent depending on anatomic location (CVs ranged from 3.55% to 13.98%). Overall CVs for abdominal ADCs from the four scans were 6.25%, 7.62%, 7.08, and 7.60%.

DATA CONCLUSION

The normalized ADCs from abdominal SMS-DWI may be comparable between different vendors and breathing schemes, showing good agreement and reproducibility. ADC changes above approximately 8% may potentially be considered as a reliable quantitative biomarker to assess disease or treatment-related changes.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Combination of prostate volume and apparent diffusion coefficient can stratify patients with a PI-RADS score of 3 to reduce unnecessary prostate biopsies

BACKGROUND

Nowadays, there are many patients who undergo unnecessary prostate biopsies after receiving a prostate imaging reporting and data system (PI-RADS) score of 3. Our purpose is to identify cutoff values of the prostate volume (PV) and minimum apparent diffusion coefficient (ADCmin) to stratify those patients to reduce unnecessary prostate biopsies.

METHODS

Data from 224 qualified patients who received prostate biopsies from January 2019 to June 2023 were collected. The Mann-Whitney U test was used to compare non-normal distributed continuous variables, which were recorded as median (interquartile ranges). The correlation coefficients were calculated using Spearman's rank correlation analysis. Categorical variables are recorded by numbers (percentages) and compared by χ2 test. Both univariate and multivariate logistic regression analysis were used to determine the independent predictors. The receiver-operating characteristic curve and the area under the curve (AUC) were used to evaluate the diagnostic performance of clinical variables.

RESULTS

Out of a total of 224 patients, 36 patients (16.07%) were diagnosed with clinically significant prostate cancer (csPCa), whereas 72 patients (32.14%) were diagnosed with any grade prostate cancer. The result of multivariate analysis demonstrated that the PV (p < 0.001, odds ratio [OR]: 0.952, 95% confidence interval [95% CI]: 0.927-0.978) and ADCmin (p < 0.01, OR: 0.993, 95% CI: 0.989-0.998) were the independent factors for predicting csPCa. The AUC values of the PV and ADCmin were 0.779 (95% CI: 0.718-0.831) and 0.799 (95% CI: 0.740-0.849), respectively, for diagnosing csPCa. After stratifying patients by PV and ADCmin, 24 patients (47.06%) with "PV < 55 mL and ADCmin < 685 μm2/s" were diagnosed with csPCa. However, only one patient (1.25%) with PV ≥ 55 mL and ADCmin ≥ 685 μm2/s were diagnosed with csPCa.

CONCLUSIONS

In this study, we found the combination of PV and ADCmin can stratify patients with a PI-RADS score of 3 to reduce unnecessary prostate biopsies. These patients with "PV ≥ 55 mL and ADCmin ≥ 685 μm2/s" may safely avoid prostate biopsies.

Investigation of radiomics models for predicting biochemical recurrence of advanced prostate cancer on pretreatment MR ADC maps based on automatic image segmentation

OBJECTIVES

To develop radiomics models based on automatic segmentation of the pretreatment apparent diffusion coefficient (ADC) maps for predicting the biochemical recurrence (BCR) of advanced prostate cancer (PCa).

METHODS

A total of 100 cases with pathologically confirmed PCa were retrospectively included in this study. These cases were randomly divided into training (n = 70) and test (n = 30) datasets. Two predictive models were constructed based on the combination of age, prostate specific antigen (PSA) level, Gleason score, and clinical staging before therapy and the prostate area (Model_1) or PCa area (Model_2). Another two predictive models were constructed based on only prostate area (Model_3) or PCa area (Model_4). The area under the receiver operating characteristic curve (ROC AUC) and precision-recall (PR) curve analysis were used to analyze the models' performance.

RESULTS

Sixty-five patients without BCR (BCR-) and 35 patients with BCR (BCR+) were confirmed. The age, PSA, volume, diameter and ADC value of the prostate and PCa were not significantly different between the BCR- and BCR+ groups or between the training and test datasets (all p > 0.05). The AUCs were 0.637 (95% CI: 0.434-0.838), 0.841 (95% CI: 0.695-0.940), 0.840 (95% CI: 0.698-0.983), and 0.808 (95% CI: 0.627-0.988) for Model_1 to Model_4 in the test dataset without significant difference. The 95% bootstrap confidence intervals for the areas under the PR curve of the four models were not statistically different.

CONCLUSION

The radiomics models based on automatically segmented prostate and PCa areas on the pretreatment ADC maps developed in our study can be promising in predicting BCR of advanced PCa.

Sex differences in normal fetal regional brain apparent diffusion coefficient changes assessed by in utero DWI

Objective

There are differences in the vulnerability of male and female fetal brains to adverse intrauterine exposure, preterm birth, and associated perinatal brain injury. The main objective of this study was to identify any statistically significant difference in the change of apparent diffusion coefficient (ADC) in the intracranial regions of male and female fetuses in the second and third trimesters.

Methods

Diffusion-weighted imaging (DWI) was performed in 200 fetuses between 20 and 37 gestational ages (GA) with normal results or suspicious results on sonography followed by structural MRI. Pairwise ADC values of the regions of interest (ROIs) were manually delineated on either side of the cerebral white matter: frontal white matter (FWM), parietal white matter (PWM), occipital white matter (OWM), temporal white matter (TWM), basal ganglia (BG), thalamus (THA), cerebellar hemisphere (CBM), and a single measurement in the pons. The changes in these values were studied over the gestational range, along with potential sex differences and asymmetries of the cerebral hemispheres.

Results

During the third trimester, ADC values in OWM, TWM, and CBM were significantly higher in male fetuses than those in female fetuses (p < 0.05). After the correction of false-discovery rates (FDR), the difference in CBM was the only statistically significant (p = 0.0032). However, the decreased rate of ADC values in male fetuses in CWM (except for FWM), BG, THA, CBM, and pons was higher than that in female fetuses during the second and third trimesters.

Conclusions

We have shown some differences in the intracranial regional ADC changes between male and female fetuses using in utero DWI during the second and third trimesters.

Role of diffusion-weighted imaging in response prediction and evaluation after high dose rate brachytherapy in patients with colorectal liver metastases

BACKGROUND

The aim of the study was to assess the role of diffusion-weighted imaging (DWI) to evaluate treatment response in patients with liver metastases of colorectal cancer.

PATIENTS AND METHODS

In this retrospective, observational cohort study, we included 19 patients with 18 responding metastases (R-Mets; follow-up at least one year) and 11 non-responding metastases (NR-Mets; local tumor recurrence within one year) who were treated with high-dose-rate brachytherapy (HDR-BT) and underwent pre- and post-interventional MRI. DWI (qualitatively, mean apparent diffusion coefficient [ADCmean], ADCmin, intraindividual change of ADCmean and ADCmin) were evaluated and compared between pre-interventional MRI, first follow-up after 3 months and second follow-up at the time of the local tumor recurrence (in NR-Mets, mean: 284 ± 122 d) or after 12 months (in R-Mets, mean: 387+/-64 d). Sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs) for detection of local tumor recurrence were calculated on second follow up, evaluating (1) DWI images only, and (2) DWI with Gd-enhanced T1-weighted images on hepatobiliary phase (contrast-enhanced [CE] T1-weight [T1w] hepatobiliary phase [hb]).

RESULTS

ADCmean significantly increased 3 months after HDR-BT in both groups (R-Mets: 1.48 ± 0.44 and NR-Mets: 1.49 ± 0.19 x 10-3 mm2;/s, p < 0.0001 and p = 0.01), however, intraindividual change of ADCmean (175% vs.127%, p = 0.03) and ADCmin values (0.44 ± 0.24 to 0.82 ± 0.58 x 10-3 mm2/s) significantly increased only in R-Mets (p < 0.0001 and p < 0.001). ADCmin was significant higher in R-Mets compared to NR-Mets on first follow-up (p = 0.04). Sensitivity (1 vs. 0.72), specificity (0.94 vs. 0.72), PPV (0.91 vs. 0.61) and NPV (1 vs. 0.81) could be improved by combining DWI with CE T1w hb compared to DWI only.

CONCLUSIONS

DW-MRI seems to be helpful in the qualitative and quantitative evaluation of treatment response after HDR-BT of colorectal metastases in the liver.

Development and benchmarking diffusion magnetic resonance imaging analysis for integration into radiation treatment planning

PURPOSE

The rising promise in the utility of advanced multi-parametric magnetic resonance (MR) imaging in radiotherapy (RT) treatment planning creates a necessity for testing and enhancing the accuracy of quantitative imaging analysis. Standardizing the analysis of diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI) to generate meaningful and reproducible apparent diffusion coefficient (ADC) and fractional anisotropy (FA) lays the requisite needed for clinical integration. The aim of the demonstrated work is to benchmark the generation of the ADC and FA parametric map analyses using integrated tools in a commercial treatment planning system against the currently used ones.

METHODS

Three software packages were used for generating ADC and FA maps in this study; one tool was developed within a commercial treatment planning system, another by the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library FSL (Analysis Group, FMRIB, Oxford, United Kingdom), and an in-house tool developed at the M.D. Anderson Cancer Center. The ADC and FA maps generated by all three packages for 35 subjects were subtracted from one another, and the standard deviation of the images' differences was used to compare the reproducibility. The reproducibility of the ADC maps was compared with the Quantitative Imaging Biomarkers Alliance (QIBA) protocol, while that of the FA maps was compared to data in published literature.

RESULTS

Results show that the discrepancies between the ADC maps calculated for each patient using the three different software algorithms are less than 2% which meets the 3.6% recommended QIBA requirement. Except for a small number of isolated points, the majority of differences in FA maps for each patient produced by the three methods did not exceed 0.02 which is 10 times lower than the differences seen in healthy gray and white matter. The results were also compared to the maps generated by existing MR Imaging consoles and showed that the robustness of console generated ADC and FA maps is largely dependent on the correct application of scaling factors, that only if correctly placed; the differences between the three tested methods and the console generated values were within the recommended QIBA guidelines.

CONCLUSIONS

Cross-comparison difference maps demonstrated that quantitative reproducibility of ADC and FA metrics generated using our tested commercial treatment planning system were comparable to in-house and established tools as benchmarks. This integrated approach facilitates the clinical utility of diffusion imaging in radiation treatment planning workflow.

Deep learning-based radiomics model from pretreatment ADC to predict biochemical recurrence in advanced prostate cancer

Purpose

To develop deep-learning radiomics model for predicting biochemical recurrence (BCR) of advanced prostate cancer (PCa) based on pretreatment apparent diffusion coefficient (ADC) maps.

Methods

Data were collected retrospectively from 131 patients diagnosed with advanced PCa, randomly divided into training (n = 93) and test (n = 38) datasets. Pre-treatment ADC images were segmented using a pre-trained artificial intelligence (AI) model to identify suspicious PCa areas. Three models were constructed, including a clinical model, a conventional radiomics model and a deep-radiomics model. The receiver operating characteristic (ROC), precision-recall (PR) curve and decision curve analysis (DCA) were used to assess predictive performance in test dataset. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were employed to compare the performance enhancement of the deep-radiomics model in relation to the other two models.

Results

The deep-radiomics model exhibited a significantly higher area under the curve (AUC) of ROC than the other two (P = 0.033, 0.026), as well as PR curve (AUC difference 0.420, 0.432). The DCA curve demonstrated superior performance for the deep-radiomics model across all risk thresholds than the other two. Taking the clinical model as reference, the NRI and IDI was 0.508 and 0.679 for the deep-radiomics model with significant difference. Compared with the conventional radiomics model, the NRI and IDI was 0.149 and 0.164 for the deep-radiomics model without significant difference.

Conclusion

The deep-radiomics model exhibits promising potential in predicting BCR in advanced PCa, compared to both the clinical model and the conventional radiomics model.

Prognostic Value of Apparent Diffusion Coefficient (ADC) in Patients with Diffuse Gliomas

This study aimed to evaluate potential posttreatment changes in ADC values within the tissue surrounding the enhancing lesion, particularly in areas not exhibiting MRI characteristics of involvement. Additionally, the objective was to investigate the correlations among ADC values, treatment response, and survival outcomes in individuals diagnosed with gliomas. This retrospective study included a total of 49 patients that underwent either stereotactic biopsy or maximal surgical resection. Histologically confirmed as Grade III or IV gliomas, all cases adhered to the 2016 and 2021 WHO classifications, with subsequent radio-chemotherapy administered post-surgery. Patients were divided into two groups: short and long survival groups. Baseline and follow-up MRI scans were obtained on a 1.5 T MRI scanner. Two ROI circles were positioned near the enhancing area, one ROI in the NAWM ipsilateral to the neoplasm and another symmetrically in the contralateral hemisphere on ADC maps. At follow-up there was a significant difference in both ipsilateral and contralateral NAWM between the two groups, -0.0857 (p = 0.004) and -0.0607 (p = 0.037), respectively. There was a weak negative correlation between survival and ADC values in ipsilateral and contralateral NAWM at the baseline with the correlation coefficient -0.328 (p = 0.02) and -0.302 (p = 0.04), respectively. The correlation was stronger at the follow-up. The findings indicate that ADC values in normal-appearing white matter (NAWM) may function as a prognostic biomarker in patients with diffuse gliomas.

Apparent diffusion coefficient values can predict neuromotor outcome in term neonates with hypoxic-ischaemic encephalopathy

AIM

To determine the apparent diffusion coefficient (ADC) in brain structures during the first 2 weeks of life and its relation with neurological outcome for hypoxic-ischaemic encephalopathy (HIE) in term neonates.

METHODS

We retrospectively evaluated 56 term-born neonates. The ADC values were measured for 11 brain regions. The clinical outcomes at least 2 years of age were defined as normal outcome, mild disability and severe disability. The area under curves (AUCs) by ROC analysis were performed to predict the neurodevelopmental outcomes. The clinical outcomes were compared between favourable outcome and adverse outcome and also between normal outcome and unfavourable outcome.

RESULTS

Thirty-four patients were judged as normal outcome, 10 as mild disability and 12 as severe disability. When the clinical outcomes were compared between favourable outcome and adverse outcome, the AUC on the 1st week was highest value at the thalamus. When the clinical outcomes were compared between normal outcome and unfavourable outcome, the AUC on the 1st week was highest at the thalamus.

CONCLUSION

The ADC values in the thalamus in the 1st week can predict the neurological outcome. The ADC values in centrum semiovale on the 2nd week can be used to predict neurodevelopmental outcomes.

Diagnostic value of apparent diffusion coefficient of psoas muscles for evaluating complications in patients with Crohn's disease

Aim

To assess the association of the apparent diffusion coefficient (ADC) of the psoas muscles and psoas muscle index (PMI) with the activity and behaviour of Crohn's disease (CD).

Material and methods

This was a retrospective study of 88 CD patients who underwent magnetic resonance enterography. Patients were classified according to the Montreal Classification in uncomplicated (non-stricturing, non-penetrating, B1), and complicated (structuring [B2] and penetrating disease [B3]). At the level of the third lumbar vertebra, the ADC and PMI were estimated. CD activity was analysed using the Magnetic Resonance Index of Activity (MaRIA), and depending on its values patients were categorized as high or low activity. Additionally, the presence of creeping fat (CrF) was used to evaluate activity. ADC and PMI were using Student's t-test.

Results

Our study included 47 males and 41 females (mean age of 38.69 ±14.4 years). The ADC in uncomplicated (B1, n = 45) and complicated disease (B2 + B3, n = 43) were 1.11 ±0.19 and 1.03 ±0.10 (10-3*mm2/s), respectively, (p = 0.02). ADC was significantly lower in patients with stricturing disease than in patients without strictures (1.02 ±0.11 and 1.10 ±0.18 [10-3 mm2/s], respectively, p = 0.01). The group with non-penetrating disease showed higher PMI than those with penetrating disease (5.71 ±1.88 vs. 4.42 ±1.55 cm2/m2, respectively, p = 0.10). There was no significant difference in PMI and ADC between patients with low and high MaRIA or positive and negative CrF.

Conclusions

The ADC of the psoas muscles is significantly lower in CD patients with uncomplicated disease, particularly those with stricturing disease. Therefore, ADC can be considered as an imaging biomarker of myopathic changes in CD patients.

Learning ADC maps from accelerated radial k-space diffusion-weighted MRI in mice using a deep CNN-transformer model

PURPOSE

To accelerate radially sampled diffusion weighted spin-echo (Rad-DW-SE) acquisition method for generating high quality ADC maps.

METHODS

A deep learning method was developed to generate accurate ADC maps from accelerated DWI data acquired with the Rad-DW-SE method. The deep learning method integrates convolutional neural networks (CNNs) with vision transformers to generate high quality ADC maps from accelerated DWI data, regularized by a monoexponential ADC model fitting term. A model was trained on DWI data of 147 mice and evaluated on DWI data of 36 mice, with acceleration factors of 4× and 8× compared to the original acquisition parameters.

RESULTS

Ablation studies and experimental results have demonstrated that the proposed deep learning model generates higher quality ADC maps from accelerated DWI data than alternative deep learning methods under comparison when their performance is quantified in whole images as well as in regions of interest, including tumors, kidneys, and muscles.

CONCLUSIONS

The deep learning method with integrated CNNs and transformers provides an effective means to accurately compute ADC maps from accelerated DWI data acquired with the Rad-DW-SE method.

Quantitative MRI in the Local Staging of Prostate Cancer: A Systematic Review and Meta-Analysis

BACKGROUND

Local staging of prostate cancer (PCa) is important for treatment planning. Radiologist interpretation using qualitative criteria is variable with high specificity but low sensitivity. Quantitative methods may be useful in the diagnosis of extracapsular extension (ECE).

PURPOSE

To assess the performance of quantitative MRI markers for detecting ECE.

STUDY TYPE

Systematic review and meta-analysis.

SUBJECTS

4800 patients from 28 studies with histopathologically confirmed PCa on radical prostatectomy were pooled for meta-analysis. Patients from 46 studies were included for systematic review.

FIELD STRENGTH/SEQUENCE

Diffusion-weighted, T2-weighted, and dynamic contrast-enhanced MRI at 1.5 T or 3 T.

ASSESSMENT

PubMed, Embase, Web of Science, Scopus, and Cochrane databases were searched to identify studies on diagnostic test accuracy or association of any quantitative MRI markers with ECE. Results extracted by two independent reviewers for tumor contact length (TCL) and mean apparent diffusion coefficient (ADC-mean) were pooled for meta-analysis, but not for other quantitative markers including radiomics due to low number of studies available.

STATISTICAL TESTS

Hierarchical summary receiver operating characteristic (HSROC) curves were computed for both TCL and ADC-mean, but summary operating points were computed for TCL only. Heterogeneity was investigated by meta-regression. Results were significant if P ≤ 0.05.

RESULTS

At the 10 mm threshold for TCL, summary sensitivity and specificity were 0.76 [95% confidence interval (CI) 0.71-0.81] and 0.68 [95% CI 0.63-0.73], respectively. At the 15 mm threshold, summary sensitivity and specificity were 0.70 [95% CI 0.53-0.83] and 0.74 [95% CI 0.60-0.84] respectively. The area under the HSROC curves for TCL and ADC-mean were 0.79 and 0.78, respectively. Significant sources of heterogeneity for TCL included timing of MRI relative to biopsy.

DATA CONCLUSION

Both 10 mm and 15 mm thresholds for TCL may be reasonable for clinical use. From comparison of the HSROC curves, ADC-mean may be superior to TCL at higher sensitivities.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Efficiency and Accuracy Evaluation of Multiple Diffusion-Weighted MRI Techniques Across Different Scanners

BACKGROUND

The choice between different diffusion-weighted imaging (DWI) techniques is difficult as each comes with tradeoffs for efficient clinical routine imaging and apparent diffusion coefficient (ADC) accuracy.

PURPOSE

To quantify signal-to-noise-ratio (SNR) efficiency, ADC accuracy, artifacts, and distortions for different DWI acquisition techniques, coils, and scanners.

STUDY TYPE

Phantom, in vivo intraindividual biomarker accuracy between DWI techniques and independent ratings.

POPULATION/PHANTOMS

NIST diffusion phantom. 51 Patients: 40 with prostate cancer and 11 with head-and-neck cancer at 1.5 T FIELD STRENGTH/SEQUENCE: Echo planar imaging (EPI): 1.5 T and 3 T Siemens; 3 T Philips. Distortion-reducing: RESOLVE (1.5 and 3 T Siemens); Turbo Spin Echo (TSE)-SPLICE (3 T Philips). Small field-of-view (FOV): ZoomitPro (1.5 T Siemens); IRIS (3 T Philips). Head-and-neck and flexible coils.

ASSESSMENT

SNR Efficiency, geometrical distortions, and susceptibility artifacts were quantified for different b-values in a phantom. ADC accuracy/agreement was quantified in phantom and for 51 patients. In vivo image quality was independently rated by four experts.

STATISTICAL TESTS

QIBA methodology for accuracy: trueness, repeatability, reproducibility, Bland-Altman 95% Limits-of-Agreement (LOA) for ADC. Wilcoxon Signed-Rank and student tests on P < 0.05 level.

RESULTS

The ZoomitPro small FOV sequence improved b-image efficiency by 8%-14%, reduced artifacts and observer scoring for most raters at the cost of smaller FOV compared to EPI. The TSE-SPLICE technique reduced artifacts almost completely at a 24% efficiency cost compared to EPI for b-values ≤500 sec/mm2 . Phantom ADC 95% LOA trueness were within ±0.03 × 10-3  mm2 /sec except for small FOV IRIS. The in vivo ADC agreement between techniques, however, resulted in 95% LOAs in the order of ±0.3 × 10-3  mm2 /sec with up to 0.2 × 10-3  mm2 /sec of bias.

DATA CONCLUSION

ZoomitPro for Siemens and TSE SPLICE for Philips resulted in a trade-off between efficiency and artifacts. Phantom ADC quality control largely underestimated in vivo accuracy: significant ADC bias and variability was found between techniques in vivo.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Discrimination of benign, atypical, and malignant peripheral nerve sheath tumors in neurofibromatosis type 1 using diffusion-weighted MRI

Background

Neurofibromatosis type 1 (NF1) is associated with the development of benign (BPNST) and malignant (MPNST) peripheral nerve sheath tumors. Recently described atypical neurofibromas (ANF) are considered pre-malignant precursor lesions to MPNSTs. Previous studies indicate that diffusion-weighted magnetic resonance imaging (DW-MRI) can reliably discriminate MPNSTs from BPNSTs. We therefore investigated the diagnostic accuracy of DW-MRI for the discrimination of benign, atypical, and malignant peripheral nerve sheath tumors.

Methods

In this prospective explorative single-center phase II diagnostic study, 44 NF1 patients (23 male; 30.1 ± 11.8 years) underwent DW-MRI (b-values 0-800 s/mm²) at 3T. Two radiologists independently assessed mean and minimum apparent diffusion coefficients (ADCmean/min) in areas of largest tumor diameters and ADCdark in areas of lowest signal intensity by manual contouring of the tumor margins of 60 BPNSTs, 13 ANFs, and 21 MPNSTs. Follow-up of ≥ 24 months (BPNSTs) or histopathological evaluation (ANFs + MPNSTs) served as diagnostic reference standard. Diagnostic ADC-based cut-off values for discrimination of the three tumor groups were chosen to yield the highest possible specificity while maintaining a clinically acceptable sensitivity.

Results

ADC values of pre-malignant ANFs clustered between BPNSTs and MPNSTs. Best BPNST vs. ANF + MPNST discrimination was obtained using ADCdark at a cut-off value of 1.6 × 10-3 mm2/s (85.3% sensitivity, 93.3% specificity), corresponding to an AUC of 94.3% (95% confidence interval: 85.2-98.0). Regarding BPNST + ANF vs. MPNST, best discrimination was obtained using an ADCdark cut-off value of 1.4 × 10-3 mm2/s (83.3% sensitivity, 94.5% specificity).

Conclusions

DW-MRI using ADCdark allows specific and noninvasive discrimination of benign, atypical, and malignant nerve sheath tumors in NF1.

Clinical value of conventional magnetic resonance imaging combined with diffusion-weighted imaging in predicting pelvic lymph node metastasis of cervical cancer

Background

In cervical cancer (CC), the involvement of pelvis lymph nodes is a crucial factor for patients' outcome. We aimed to investigate the value of conventional magnetic resonance imaging (MRI) combined with diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) in predicting CC pelvic lymph node metastasis (PLNM).

Methods

This retrospective study included CC patients who received surgical treatments. Surgical pathology results served as the gold standard for investigating the diagnostic performance of conventional MRI combined with DWI. We analyzed the association between tumor ADC and PLNM, as well as other pathological factors. The areas under the receiver operating characteristic curves (AUCs) for ADC in assessing PLNM and pathological factors were evaluated, and optimal cut-off points were obtained.

Results

A total of 261 CC patients were analyzed. PLNM patients had significantly lower tumor ADC (0.829 ± 0.144×10-3mm2/s vs. 1.064 ± 0.345×10-3mm2/s, p<0.0001), than non-PLNM CC. The agreement between conventional MRI combined with DWI and pathological results on PLNM diagnosis was substantial (Kappa=0.7031, p<0.0001), with 76% sensitivity, 94.31% specificity, and 90.8% accuracy. The AUC of tumor ADC was 0.703, and the optimal cut-off was 0.95×10-3 mm2/s. In multivariate analysis model 1, tumor ADC<0.95×10-3mm2/s was significantly associated with PLNM (OR, 2.83; 95%CI, 1.08-7.43; p= 0.0346) after adjusting for age and pathological risk factors. In multivariate analysis model 2, tumor ADC<0.95×10-3mm2/s (OR, 4.00; 95%CI, 1.61-9.89; p=0.0027), age<35 years old (OR, 2.93; 95%CI, 1.04-8.30; p=0.0428), increased tumor diameter on MRI (OR, 2.17; 95%CI, 1.18-3.99; p=0.0128), vaginal vault involvement on MRI (OR, 2; 95%CI, 1.002-3.99; p=0.0494) were independent predictors for PLNM. Tumor ADC<0.95×10-3mm2/s was significantly associated with higher risk of tumor diameter ≥4cm (OR, 2.60; 95%CI, 1.43-4.73; p=0.0017), muscular layer infiltration >1/2 (OR, 5.46; 95%CI, 3.19-9.34; p<0.0001), vaginal vault involvement (OR, 2.25; 95%CI, 1.28-3.96; p=0.0051), and lymphovascular space involvement (OR, 3.81; 95%CI, 2.19-6.63; p<0.0001).

Conclusion

Conventional MRI combined with DWI had a good diagnostic performance in detecting PLNM. The tumor ADC value in PLNM patients was significantly lower than that in non-PLNM patients. Tumor ADC <0.95×10-3mm2/s, age <35 years old, increased tumor diameter on MRI, vaginal vault involvement on MRI were independent predictors for PLNM.

Diffusion-Weighted MR Imaging of the Thymus in Children with Non-Thymic Neoplasms

Purpose: To investigate the diffusion-weighted imaging (DWI) findings and apparent diffusion coefficient (ADC) value of the thymus in children under the age of 13 who have non-thymic neoplasms. Materials and Methods: From January 2021 to April 2023, a single-center retrospective study analyzed consecutive 191 thoracic MRI scans with DWI from 67 children (<13 years) with non-thymic neoplasms. The scans were categorized based on the presence of restricted diffusion on DWI. We analyzed the demographics, laboratory data, and MR findings of the thymus. Statistical differences were assessed using generalized estimating equations. Results: Forty-five percent (86/191) of the scans exhibited restricted diffusion in the thymus: multifocal (n = 65; 76%), focal (n = 19; 22%), and diffuse (n = 2; 2%) patterns. The restricted diffusion group demonstrated higher absolute lymphocyte counts, more prominent thymus sizes, and higher frequency of off-treatment status compared to the unrestricted diffusion group (p>0.05). The mean ADC value across all 191 scans was 1.80 × 10-3 mm2/s. No significant difference was observed in the ADC value related to restricted diffusion patterns, the MRI vendors, or the age at the time of the scan. Conclusions: Restricted diffusion was observed in 45% of the thymus in children with non-thymic neoplasms, with a mean ADC value of 1.80 × 10-3 mm2/s. Recognizing the DWI findings can prevent unnecessary invasive procedures, alleviating concerns for patients and families.

ADC histogram parameters differentiating atypical from transitional meningiomas: correlation with Ki-67 proliferation index

BACKGROUND

Preoperative differentiation of atypical meningioma (AtM) from transitional meningioma (TrM) is critical to clinical treatment.

PURPOSE

To investigate the role of apparent diffusion coefficient (ADC) histogram analysis in differentiating AtM from TrM and its correlation with the Ki-67 proliferation index (PI).

METHODS

Clinical, imaging, and pathological data of 78 AtM and 80 TrM were retrospectively collected. Regions of interest (ROIs) were delineated on axial ADC images using MaZda software and histogram parameters (mean, variance, skewness, kurtosis, 1st percentile [ADCp1], 10th percentile [ADCp10], 50th percentile [ADCp50], 90th percentile [ADCp90], and 99th percentile [ADCp99]) were generated. The Mann-Whitney U test was used to compare the differences in histogram parameters between the two groups; receiver operating characteristic (ROC) curves were used to assess diagnostic efficacy in differentiating AtM from TrM preoperatively. The correlation between histogram parameters and Ki-67 PI was analyzed.

RESULTS

All histogram parameters of AtM were lower than those of TrM, and the variance, skewness, kurtosis, ADCp90, and ADCp99 were significantly different (P < 0.05). Combined ADC histogram parameters (variance, skewness, kurtosis, ADCp90, and ADCp99) achieved the best diagnostic performance for distinguishing AtM from TrM. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.800%, 76.25%, 67.95%, 70.15%, 70.93%, and 73.61%, respectively. All histogram parameters were negatively correlated with Ki-67 PI (r = -0.012 to -0.293).

CONCLUSION

ADC histogram analysis is a potential tool for non-invasive differentiation of AtM from TrM preoperatively, and ADC histogram parameters were negatively correlated with the Ki-67 PI.

The Role of Diffusion-Weighted Imaging in the Evaluation of Treatment of Newly Diagnosed Type 2 Diabetic Patients

INTRODUCTION

To compare the pre and post-treatment pancreatic apparent diffusion coefficient (ADC) values of type II diabetes patients with control subjects, and also to evaluate its effectiveness in evaluating the response to treatment.

METHODS

The study included 35 newly diagnosed type 2 diabetic patients and 35 non-diabetic participants, matched for sex and age. Insulin and metformin treatment was given to the patients. Abdominal diffusion-weighted MR imaging was performed before and after the treatment. ADCs of the control group and patients pre and post-treatment were compared. In addition, the clinical parameters of the patients related to diabetes were recorded.

RESULTS

There was a significant difference between the median pancreatic ADC values of the patients pre and post-treatment. While there was a significant difference between the median pancreatic ADC values of the patient and the control groups before the treatment, no significant difference after the treatment was observed. There was a positive correlation between mean pancreatic ADC values and age, as well as a negative correlation with Hb1Ac level and eGFR.

CONCLUSION

Pancreatic ADC values of newly diagnosed type II diabetes patients can be used as a marker of pancreatic function in the evaluation of response to treatment and clinical decisions.

Apparent diffusion coefficient measurements of bone marrow infiltration patterns in multiple myeloma for the assessment of tumor burden - a feasibility study

BACKGROUND

The purpose of our study was to explore and compare the tumor burden of different bone marrow infiltration patterns and evaluate the feasibility of apparent diffusion coefficient (ADC) value to identify patterns in multiple myeloma (MM).

PATIENTS AND METHODS

Ninety-three patients with newly diagnosed multiple myeloma and 23 controls had undergone routine magnetic resonance imaging (MRI) and diffusion-weighted MRI (DWI) from January 2019 to November 2020. Five bone marrow (BM) infiltration patterns were allocated according to routine MRI. The laboratory data and ADC values of patterns were analyzed and compared. ROC analysis was used to establish the best diagnostic ADC threshold value for identifying these patterns and distinguishing normal pattern from controls. Besides, the correlation between the ADC values of diffuse pattern and the plasma cells ratio was assessed.

RESULTS

The values of hemoglobin, beta-2 microglobulin (β2-MG), plasma cell, M protein, the percentages of stage, high-risk fluorescence in situ hybridization, and ADC values showed significant difference among patterns. ADCmean at a specific value (368.5×10-6 mm2/s) yielded a maximum specificity (95.5%) and sensitivity (92.0%) in diagnosing MM. A specific value (335.5×10-6mm2/s) yielded a maximum specificity (84.7%) and sensitivity (88.0%) in discriminating visually normal pattern in MM from controls. There was a moderate positive correlation between the plasma cells ratio and ADCs of diffuse infiltration patterns (r = 0.648, P < 0.001).

CONCLUSIONS

The bone marrow infiltration patterns in MM patients can indicate the tumor burden and ADC value has the ability to discriminate these patterns objectively.

Regional ADC values of the morphologically normal canine brain

Introduction

Diffusion-weighted magnetic resonance imaging is increasingly available for investigation of canine brain diseases. Apparent diffusion coefficient (ADC) of normal canine brains is reported only in small numbers of subjects. The aim of the study was to investigate the ADC of different anatomical regions in the morphologically normal brain in a large population of canine patients in clinical setting. Additionally, possible influence on the ADC value of patient-related factors like sex, age and body weight, difference between the left and right side of the cerebral hemispheres, and between gray and white matter were investigated.

Methods

Brain magnetic resonance studies including diffusion-weighted images of dogs presented at the Vetsuisse Faculty-University Zurich between 2015 and 2020 were reviewed retrospectively. Only morphologically normal brain magnetic resonance studies of dogs presented with neurological signs or non-neurological signs were included. Apparent diffusion coefficient values of 12 regions of interest (ROIs) in each hemisphere and an additional region in the cerebellar vermis were examined in each dog.

Results

A total of 321 dogs (including 247 dogs with neurological signs and 62 dogs with non-neurological signs) of various breeds, sex and age were included. Apparent diffusion coefficient significantly varied among most anatomical brain regions. A significantly higher ADC was measured in the gray [median 0.79 (range 0.69-0.90) × 10-3 mm2/s] compared to the white matter [median 0.70 (range 0.63-0.85) × 10-3 mm2/s]. No significant differences were found between the left and right cerebral hemispheres in most of the regions, neither between sexes, different reproductive status, and not consistently between body weight groups. Age was correlated first with a decrease from dogs <1 year of age to middle-age (⩾3 to <8 years) dogs and later with an increase of ADC values in dogs ⩾8 years.

Discussion

Apparent diffusion coefficient values of 25 ROIs were described in 321 morphologically normal canine brains in clinical setting. Apparent diffusion coefficient differences depending on the brain anatomical region are present. Apparent diffusion coefficient differences among age classes are present, likely consistent with brain maturation and aging. The described data can be a reference for future studies in clinical settings on the canine brain.

Feasibility of quantitative diffusion-weighted imaging during intra-procedural MRI-guided brachytherapy of locally advanced cervical and vaginal cancers

PURPOSE

To determine the feasibility of quantitative apparent diffusion coefficient (ADC) acquisition during magnetic resonance imaging-guided brachytherapy (MRgBT) using reduced field-of-view (rFOV) diffusion-weighted imaging (DWI).

METHODS AND MATERIALS

T2-weighted (T2w) MR and full-FOV single-shot echo planar (ssEPI) DWI were acquired in 7 patients with cervical or vaginal malignancy at baseline and prior to brachytherapy, while rFOV-DWI was acquired during MRgBT following brachytherapy applicator placement. The gross target volume (GTV) was contoured on the T2w images and registered to the ADC map. Voxels at the GTV's maximum Maurer distance comprised a central sub-volume (GTVcenter). Contour ADC mean and standard deviation were compared between timepoints using repeated measures ANOVA.

RESULTS

ssEPI-DWI mean ADC increased between baseline and prebrachytherapy from 1.03 ± 0.18 10-3 mm2/s to 1.34 ± 0.28 10-3 mm2/s for the GTV (p = 0.06) and from 0.84 ± 0.13 10-3 mm2/s to 1.26 ± 0.25 10-3 mm2/s at the level of the GTVcenter (p = 0.03), consistent with early treatment response. rFOV-DWI during MRgBT demonstrated mean ADC values of 1.28 ± 0.14 10-3 mm2/s and 1.28 ± 0.19 10-3 mm2/s for the GTV and GTVcenter, respectively (p = 0.02 and p = 0.03 relative to baseline). No significant differences were observed between ssEPI-DWI and rFOV-DWI ADC measurements.

CONCLUSIONS

Quantitative ADC measurement in the setting of MRI guided brachytherapy implant placement for cervical and vaginal cancers is feasible using rFOV-DWI, with comparable mean ADC comparable to prebrachytherapy ssEPI-DWI, and may enable MRI-guided radiotherapy targeting of low ADC, radiation resistant sub-volumes of tumor.

Potential Antihuman Epidermal Growth Factor Receptor 2 Target Therapy Beneficiaries: The Role of MRI-Based Radiomics in Distinguishing Human Epidermal Growth Factor Receptor 2-Low Status of Breast Cancer

BACKGROUND

Multiparametric MRI radiomics could distinguish human epidermal growth factor receptor 2 (HER2)-positive from HER2-negative breast cancers. However, its value for further distinguishing HER2-low from HER2-negative breast cancers has not been investigated.

PURPOSE

To investigate whether multiparametric MRI-based radiomics can distinguish HER2-positive from HER2-negative breast cancers (task 1) and HER2-low from HER2-negative breast cancers (task 2).

STUDY TYPE

Retrospective.

POPULATION

Task 1: 310 operable breast cancer patients from center 1 (97 HER2-positive and 213 HER2-negative); task 2: 213 HER2-negative patients (108 HER2-low and 105 HER2-zero); 59 patients from center 2 (16 HER2-positive, 27 HER2-low and 16 HER2-zero) for external validation.

FIELD STRENGTH/SEQUENCE

A 3.0 T/T1-weighted contrast-enhanced imaging (T1CE), diffusion-weighted imaging (DWI)-derived apparent diffusion coefficient (ADC).

ASSESSMENT

Patients in center 1 were assigned to a training and internal validation cohort at a 2:1 ratio. Intratumoral and peritumoral features were extracted from T1CE and ADC. After dimensionality reduction, the radiomics signatures (RS) of two tasks were developed using features from T1CE (RS-T1CE), ADC (RS-ADC) alone and T1CE + ADC combination (RS-Com).

STATISTICAL TESTS

Mann-Whitney U tests, the least absolute shrinkage and selection operator, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

RESULTS

For task 1, RS-ADC yielded higher area under the ROC curve (AUC) in the training, internal, and external validation of 0.767/0.725/0.746 than RS-T1CE (AUC = 0.733/0.674/0.641). For task 2, RS-T1CE yielded higher AUC of 0.765/0.755/0.678 than RS-ADC (AUC = 0.706/0.608/0.630). For both of task 1 and task 2, RS-Com achieved the best performance with AUC of 0.793/0.778/0.760 and 0.820/0.776/0.711, respectively, and obtained higher clinical benefit in DCA compared with RS-T1CE and RS-ADC. The calibration curves of all RS demonstrated a good fitness.

DATA CONCLUSION

Multiparametric MRI radiomics could noninvasively and robustly distinguish HER2-positive from HER2-negative breast cancers and further distinguish HER2-low from HER2-negative breast cancers.

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 2.

High-resolution Diffusion-weighted Imaging of the Prostate Using Multiplexed Sensitivity-encoding: Comparison with the Conventional and Reduced Field-of-view Techniques

PURPOSE

To compare objective and subjective image quality, lesion conspicuity, and apparent diffusion coefficient (ADC) of high-resolution multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) with conventional DWI (c-DWI) and reduced FOV DWI (rFOV-DWI) in prostate MRI.

METHODS

Forty-seven patients who underwent prostate MRI, including c-DWI, rFOV-DWI, and MUSE-DWI, were retrospectively evaluated. SNR and ADC of normal prostate tissue and contrast-to-noise ratio (CNR) and ADC of prostate cancer (PCa) were measured and compared between the three sequences. Image quality and lesion conspicuity were independently graded by two radiologists using a 5-point scale and compared between the three sequences.

RESULTS

The SNR of normal prostate tissue was significantly higher with rFOV-DWI than with the other two DWI techniques (P ≤ 0.01). The CNR of the PCa was significantly higher with rFOV-DWI than with MUSE-DWI (P < 0.05). The ADC of normal prostate tissue measured by rFOV-DWI was lower than that measured by MUSE-DWI and c-DWI (P < 0.01), while there was no difference in the ADC of cancers. In the qualitative analysis, MUSE-DWI showed significantly higher scores than rFOV-DWI and c-DWI for visibility of anatomy and overall image quality in both readers, and significantly higher scores for distortion in one of the two readers (P < 0.001). There was no difference in lesion conspicuity between the three sequences.

CONCLUSION

High-resolution MUSE-DWI showed higher image quality and reduced distortion compared to c-DWI, while maintaining a wide FOV and similar ADC quantification, although no difference in lesion conspicuity was observed.

Apparent Diffusion Coefficient Metrics to Differentiate between Treatment-Related Abnormalities and Tumor Progression in Post-Treatment Glioblastoma Patients: A Retrospective Study

Distinguishing treatment-related abnormalities (TRA) from tumor progression (TP) in glioblastoma patients is a diagnostic imaging challenge due to the identical morphology of conventional MR imaging sequences. Diffusion-weighted imaging (DWI) and its derived images of the apparent diffusion coefficient (ADC) have been suggested as diagnostic tools for this problem. The aim of this study is to determine the diagnostic accuracy of different cut-off values of the ADC to differentiate between TP and TRA. In total, 76 post-treatment glioblastoma patients with new contrast-enhancing lesions were selected. Lesions were segmented using a T1-weighted, contrast-enhanced scan. The mean ADC values of the segmentations were compared between TRA and TP groups. Diagnostic accuracy was compared by use of the area under the curve (AUC) and the derived sensitivity and specificity values from cutoff points. Although ADC values in TP (mean = 1.32 × 10-3 mm2/s; SD = 0.31 × 10-3 mm2/s) were significantly different compared to TRA (mean = 1.53 × 10-3 mm2/s; SD = 0.28 × 10-3 mm2/s) (p = 0.003), considerable overlap in their distributions exists. The AUC of ADC values to distinguish TP from TRA was 0.71, with a sensitivity and specificity of 65% and 70%, respectively, at an ADC value of 1.47 × 10-3 mm2/s. These findings therefore indicate that ADC maps should not be used in discerning between TP and TRA at a certain timepoint without information on temporal evolution.