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

To examine possible differences between the evolution of cerebral watershed infarction (WI) and that of territorial thromboembolic infarction (TI) by using diffusion-weighted (DW) and T2-weighted magnetic resonance (MR) images and apparent diffusion coefficient (ADC) maps. Fourteen patients with TI and nine with WI underwent MR imaging from the acute to chronic infarction stages. ADC maps were derived from DW images. Lesion-to-normal tissue signal intensity ratios on ADC maps (rADC), echo-planar T2-weighted images, and DW images were calculated. Lesion volumes at acute or early subacute infarction stages were measured on DW images, and final lesion volumes were estimated on fluid-attenuated inversion-recovery images. Analysis of variance revealed a significant difference in temporal evolution patterns of rADC between WI and TI (P <.001). rADC pseudonormalization following TI began about 10 days after symptom onset, but that following WI did not occur until about 1 month after symptom onset. The Pearson correlation coefficient between final and initial infarct volumes was 0.9899 for both infarction subtypes, indicating that the initial ischemic injury volume measured at the acute or early subacute stage predicted the final lesion volume fairly well. The evolution time of ADC is faster for TI than for WI. This difference, which likely originates from the different pathophysiologic and hemodynamic features of the two infarction types, might account for the relatively large range of ADC values reported for the time course of ischemic strokes.

In this paper, several radiomics-based predictive models of response to induction chemotherapy (IC) in sinonasal cancers (SNCs) are built and tested. Models were built as a combination of radiomic features extracted from three types of MRI images: T1-weighted images, T2-weighted images and apparent diffusion coefficient (ADC) maps. Fifty patients (aged 54 ± 12 years, 41 men) were included in this study. Patients were classified according to their response to IC (25 responders and 25 nonresponders). Not all types of images were acquired for all of the patients: 49 had T1-weighted images, 50 had T2-weighted images and 34 had ADC maps. Only in a subset of 33 patients were all three types of image acquired. Eighty-nine radiomic features were extracted from the MRI images. Dimensionality reduction was performed by using principal component analysis (PCA) and by selecting only the three main components. Different algorithms (trees ensemble, K-nearest neighbors, support vector machine, naïve Bayes) were used to classify the patients as either responders or nonresponders. Several radiomic models (either monomodality or multimodality obtained by a combination of T1-weighted, T2-weighted and ADC images) were developed and the performance was assessed through 100 iterations of train and test split. The area under the curve (AUC) of the models ranged from 0.56 to 0.78. Trees ensemble, support vector machine and naïve Bayes performed similarly, but in all cases ADC-based models performed better. Trees ensemble gave the highest AUC (0.78 for the T1-weighted+T2-weighted+ADC model) and was used for further analyses. For trees ensemble, the models based on ADC features performed better than those models that did not use those features (P < 0.02 for one-tail Hanley test, AUC range 0.68-0.78 vs 0.56-0.69) except the T1-weighted+ADC model (AUC 0.71 vs 0.69, nonsignificant differences). The results suggest the relevance of ADC-based radiomics for prediction of response to IC in SNCs.

To investigate and determine the optimal b-values for Diffusion Weighted Imaging(DWI) for Apparent Diffusion Coefficient(ADC) maps in differentiating normal, benign and malignant breast tissue. Twenty-five patients underwent diffusion-weighted magnetic resonance imaging (DWI-MRI) and conventional breast MRI with suspicious breast fmdings(BIRADS >4). Breast lesions were defined by pathology. The DWI was acquired with different b-values ranging from 0,500, 600,750 and 1000s/mm2 . Apparent Diffusion Coefficient(ADC) maps of breast tissue were constructed using different b-values, e.g., using either 2 b-values(0- 1000) or combinations of 3 or more (0,500,1000 or 0,600,750,1000). Quantitative analyses of the ADC maps of glandular, fatty and lesion tissue were obtained. Ratios of lesion to glandular tissue(L/GT) and signal to noise(SNR) were assessed. Paired t-tests were performed for statistical significance. Eighteen patients had invasive ductal carcinoma and 7 had benign breast lesions. The mean ADC value for malignant lesions using all b values was 1.17±0.16×10-3mm2 /s with a lesion to glandular(L/GT) ratio=0.65. The benign lesions, ADC map value was 1.86±0.03×10-3mm2 /s with L/GT=0.98. There was a significant difference(P<0.05) between benign and malignant lesions ADC map value.The lowest SNR(12±6) was with single b-values. There was a significant difference(P<0.05) in SNR with multiple b-values(34±6) compared to single b-values. The highest SNR was given by using two b values greater than 500. Finally, the background noise for all combinations was surprising stable and ranged between 60±20%. This is the first study to investigate the effect of changing different b values in DWI breast imaging. There were significant differences in the SNR between single and multiple b values. Our data show suggest that the recommended b-values for DWI in breast are 0, and two that are 500 or greater. Therefore use of at least 3 b-values in DWI/ADC mapping of breast lesions are needed for better characterization of benign and malignant breast tissue. P50CA88843, Avon Foundation for Women:01-2008-012, U01CA070095, andU01CA140204.

Diffusion-weighted imaging (DWI) has shown high sensitivity in the diagnosis of acute arterial strokes. The pathophysiology of cerebral venous thrombosis with associated venous stroke appears to differ from that of arterial strokes. The purpose of this study was to describe DWI findings in venous strokes. The authors reviewed 3 adults with superior sagittal sinus thrombosis who underwent DWI and magnetic resonance imaging within 24 hours of symptom onset. DWI was obtained at 1.5 T using the multishot echo planar technique (TR = 8000, TE = 97, field of view = 30 x 19 cm, slice thickness = 6.0 mm, interslice gap = 0.5 mm, matrix 128 x 128, NEX = 1). The diffusion gradients were applied in 3 orthogonal directions with 3 increasing b values (0-1000 s/mm2) to create average (trace) DWI images. Apparent diffusion coefficient (ADC) values were calculated on a pixel-by-pixel basis and displayed as ADC maps. DWI showed hyperintensities in patients 1 and 2 and hypointensity in patient 3 in corresponding to parenchymal lesions on conventional images. As compared to the homologous uninvolved location in the contralateral hemisphere, ADC values were decreased (0.53 x 10(-3) mm2/s [patient 1] and 0.68 x 10(-3) mm2/s [patient 2]) and increased (1.1 x 10(-3) mm2/s [patient 3]). The ADC ratio of the lesion in the involved to uninvolved side was 88% (patient 1), 81% (patient 2), and 120% (patient 3). Acute cerebral venous strokes may contain cytotoxic edema and/or vasogenic edema on DWI scans. DWI may be helpful in diagnosing cerebral venous thrombosis in cases with cryptic presentations.

The aim of this study was to evaluate an advanced magnetic resonance imaging (MRI) method, apparent diffusion coefficient (ADC) mapping, in the functional assessment of carious teeth. 38 extracted human teeth with scores of 0, 3 and 6 according to International Caries Detection and Assessment System (ICDAS) criteria were screened and subsequently analyzed by MRI at 2.35 T. Histology sectioning of teeth was used for the gold standard by analyzing two extreme cases (intact and severely decayed). ADC maps of the same teeth were calculated from corresponding diffusion-weighted images and used to obtain ADC distributions along dental pulp as functions of the relative pulp length measured from the occlusal pulp side. The measured distributions were analyzed for the best fit by a four-parameter three-segment linear regression model for ADC distribution along the pulp. MRI results were in good agreement with findings in histological sections of identical teeth. The best fit model parameters, relative decayed region depth, relative transition region width and ADC values of intact and decayed pulp tissue, showed statistically significant differences between the ADC values of intact and decayed pulp tissue (1.0 × 10^-9 m²/s vs. 0.74-0.89 × 10^-9 m²/s) and the relative decayed region depth progressing with ICDAS score (3 vs. 46% with ICDAS 3 vs. ICDAS 6). The results of this feasibility study confirmed relevance of ADC mapping for the discrimination and localization of intact and decayed regions in dental pulps of carious teeth.

Background Magnetic Resonance Imaging (MRI) is a highly effective tool for the detection of prostate cancer (PCa). Diffusion-weighted MRI (DW-MRI) is a sensitive technique that depends on the b value and apparent diffusion coefficient (ADC) value for the diagnosis of PCa. The main objective of this study was to determine the optimal b-value and apparent diffusion coefficient (ADC) value in DW-MRI for the diagnosis of prostate cancer. Methods A prospective study including 26 male participants were conducted. MRI examinations were performed with T2 fat saturation sequences, and Diffusion weighted imaging (DWI) sequences with b-values (800, 1000, 1500, and 2000 mm2/s) were used, and the corresponding ADC maps were calculated. Qualitative and quantitative analyses were conducted. Results According to the present study, a b-value of 0,1500 mm2/s exhibited the highest Signal-to-Noise Ratio (SNR), Signal Intensity Ratio (SIR), and Contrast-to-Noise Ratio (CNR). Area Under the Curve (AUC) for 0,1500 mm2/s was 0.80, indicating a high diagnostic accuracy for prostate cancer. Conclusion DWI with a b-value of 1500 mm2/s provides good diagnostic accuracy for differential diagnosis of prostate lesions. DWI is a crucial sequence in multiparametric MRI of the prostate and offers detailed information that enhances the accuracy of prostate cancer diagnosis and management.

To examine the clinicopathological features and identify the predictors of pathological upgrading in low-risk prostate cancer (PCa) patients without hypointense lesions on the apparent diffusion coefficient (ADC) map calculated from multiparametric magnetic resonance imaging. We reviewed the medical records of 1905 PCa patients who underwent radical prostatectomy between 2007 and 2015. All ADC images were graded using the five-grade Likert scale; the positive hypointense lesions were graded 4-5. We analyzed 256 patients with low-risk classifications according to D'Amico criteria. Patients were classified into two groups according to the pathologic upgrading in the surgical specimens. The predictive factors for pathologic upgrading were evaluated using amultivariate logistic regression analysis. In 256 patients with low-risk PCa, the percentage of positive cores [odds ratio (OR) 1.09; 95% confidence interval (CI) 1.02-1.16], the percentage of cancer in the positive cores (OR 1.07, 95% CI 1.03-1.12), and the presence of hypointensity on an ADC map (OR 2.28; 95% CI 1.23-4.22) were independent predictors of pathologic upgrading. Notably, 138 of low-risk patients (53.9%) had no hypointense lesions on an ADC map. Of these 138 patients, the percentage of positive cores (OR 1.09; 95% CI 1.01-1.18) and the percentage of cancer in the positive cores (OR 1.06; 95% CI 1.01-1.12) remained independent predictors of pathologic upgrading. In low-risk PCa patients without hypointense lesions on an ADC map, biopsy-related parameters such as the percentage of positive cores and the percentage of cancer in the positive cores were independent predictors of pathological upgrading following radical prostatectomy.

This study aims for targeted biopsy validation of magnetic resonance fingerprinting (MRF) and diffusion mapping for characterizing peripheral zone (PZ) prostate cancer and noncancers. One hundred four PZ lesions in 85 patients who underwent magnetic resonance imaging were retrospectively analyzed with apparent diffusion coefficient (ADC) mapping, MRF, and targeted biopsy (cognitive or in-gantry). A radiologist blinded to pathology drew regions of interest on targeted lesions and visually normal peripheral zone on MRF and ADC maps. Mean T1, T2, and ADC were analyzed using linear mixed models. Generalized estimating equations logistic regression analyses were used to evaluate T1 and T2 relaxometry combined with ADC in differentiating pathologic groups. Targeted biopsy revealed 63 cancers (low-grade cancer/Gleason score 6 = 10, clinically significant cancer/Gleason score ≥7 = 53), 15 prostatitis, and 26 negative biopsies. Prostate cancer T1, T2, and ADC (mean ± SD, 1660 ± 270 milliseconds, 56 ± 20 milliseconds, 0.70 × 10 ± 0.24 × 10 mm/s) were significantly lower than prostatitis (mean ± SD, 1730 ± 350 milliseconds, 77 ± 36 milliseconds, 1.00 × 10 ± 0.30 × 10 mm/s) and negative biopsies (mean ± SD, 1810 ± 250 milliseconds, 71 ± 37 milliseconds, 1.00 × 10 ± 0.33 × 10 mm/s). For cancer versus prostatitis, ADC was sensitive and T2 specific with comparable area under curve (AUC; (AUCT2 = 0.71, AUCADC = 0.79, difference between AUCs not significant P = 0.37). T1 + ADC (AUCT1 + ADC = 0.83) provided the best separation between cancer and negative biopsies. Low-grade cancer T2 and ADC (mean ± SD, 75 ± 29 milliseconds, 0.96 × 10 ± 0.34 × 10 mm/s) were significantly higher than clinically significant cancers (mean ± SD, 52 ± 16 milliseconds, 0.65 ± 0.18 × 10 mm/s), and T2 + ADC (AUCT2 + ADC = 0.91) provided the best separation. T1 and T2 relaxometry combined with ADC mapping may be useful for quantitative characterization of prostate cancer grades and differentiating cancer from noncancers for PZ lesions seen on T2-weighted images.

The objective of our study was to investigate the comparative effectiveness of different MRI sequences for the estimation of index lesion volume in patients with prostate cancer (PCa) compared with ground truth volume measured on whole-mount pathology. Patients with PCa underwent multiparametric MRI (mpMRI) on a 3-T MRI scanner before radical prostatectomy. Forty PCa index lesions were identified and outlined on histology by a pathologist. Two radiologists who were informed about the presence of PCa but were not aware of lesion outlines on histology worked in consensus to delineate PCa lesions on T2-weighted imaging, apparent diffusion coefficient (ADC) maps, and early-phase dynamic contrast-enhanced MRI (DCE-MRI). The lesion volumes from different mpMRI sequences and the percentage of volume underestimation compared with pathology were calculated and correlated with volume at pathology. The repeated-measures ANOVA with the posthoc Bonferroni test was performed to evaluate whether the difference between the estimated tumor volumes was statistically significant. The mean PCa lesion volume estimated from pathology, T2-weighted imaging, DWI (ADC maps), and DCE-MRI were 4.61 ± 4.99 (SD) cm3, 2.03 ± 2.96 cm3, 1.81 ± 2.76 cm3, and 3.48 ± 4.06 cm3, respectively. The lesion volumes on T2-weighted images (p = 0.000002), ADC maps (p = 0.000003), and DCE-MR images (p = 0.004412) were significantly lower than those from pathology. PCa lesion volume was significantly underestimated on T2-weighted images, ADC maps, and DCE-MR images compared with pathology by 54.98% ± 22.60% (mean ± SD), 58.59% ± 18.58%, and 18.33% ± 30.11%, respectively; underestimation using T2-weighted imaging (p = 1.01 × 10-11) and DWI (p = 2.94 × 10-11) was significantly higher than underestimation using DCE-MRI. Correlations between lesion volume estimated on T2-weighted images, ADC maps, and DCE-MR images with pathology were 0.91 (p = 9.03 × 10-16), 0.86 (p = 7.32 × 10-13), and 0.93 (p = 8.22 × 10-18), respectively. DCE-MRI performed better than T2-weighted imaging and DWI for estimation of index PCa volume and therefore can be preferred over these other two sequences for volume estimation.

We aimed to identify the radiologic features of uterine cervical adenocarcinoma associated with lobular endocervical glandular hyperplasia (LEGH). We retrospectively analyzed magnetic resonance (MR) images and pathologic findings of eight patients who underwent preoperative MR imaging followed by surgical resection and who were pathologically diagnosed with adenocarcinoma (except for adenocarcinoma in situ) associated with LEGH. We assessed the following MR findings: multicystic component (MC), solid component (SC), signal intensity of SC on diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) map, and radiological stage (r-stage) based on the FIGO classification. A pathologist reevaluated the pathological stage (p-stage) according to the FIGO classification. We correlated the MR findings with the pathologic features. Eight patients were classified into the following three types based on the MR findings: type A, MC and SC; type B, only SC; and type C, only MC. In the five patients with type A, diffusion restriction (DR) was seen on DWI and the ADC map. In 80% of type A cases, the r-stage matched the p-stage. In the one patient with type B, DR was not seen on DWI or the ADC map, and the r-stage matched the p-stage. In the remaining type C cases, DR was not seen on DWI or the ADC map, and the r-stage was underestimated compared with the p-stage. On MR imaging, the most common type of adenocarcinoma with LEGH is type A; type C is difficult to diagnose as carcinoma.

Previous studies on the value of radiomics for diagnosing clinically significant prostate cancer (csPCa) only utilized intralesional features. However, the role of tumor microenvironment is important in tumor generation and progression. The aim of this study is to build and validate a nomogram based on perilesional and intralesional radiomics features and clinical factors for csPCa. This is a retrospective study, which included 140 patients who underwent prostate magnetic resonance imaging (MRI). This study used 3.0T T2-weighted imaging, apparent diffusion coefficient maps (derived from diffusion-weighted images), and dynamic contrast-enhanced MRI. Region of interest (ROI)s were segmented by two radiologists. Intralesional and combined radiomics signatures were built based on radiomics features extracted from intralesional and the combination of radiomics features extracted from intralesional and perilesional volumes. Serum total prostate-specific antigen level and combined radiomics signature scores were used to construct a diagnostic nomogram. Intraclass correlation efficient analysis was used to test intra- and inter-rater agreement of radiomics features. Min-max scalar was used for normalization. One-way analysis of variance or the Mann-Whitney U-test was used for univariate analysis. Receiver operating characteristic curve analysis, accuracy, balanced accuracy, and F1-score were used to evaluate radiomics signatures and the nomogram. Also, the nomogram was evaluated using decision curve analysis in testing cohort. Delong test was used to compare area under the curves (AUCs). Statistical significance was set at p < 0.05. In testing cohort, AUC, accuracy, balanced accuracy, and F1-score of combined radiomics signature (0.94, 0.83, 0.80, and 0.87, respectively) were all higher than that of intralesional radiomics signature (0.90, 0.77, 0.74, and 0.83, respectively). The difference between AUCs was insignificant (p of 0.19). AUC, accuracy, balanced accuracy, and F1-score of the nomogram were 0.96, 0.94, 0.95, and 0.95, respectively. Nomogram was clinically useful when threshold probability of a patient is higher than 0.06. Perilesional radiomics features improved the discrimination ability of the radiomics signature. Diagnostic nomogram had a good performance. LEVEL OF EVIDENCE: 3. TECHNICAL EFFICACY STAGE: 2.

Correlation between apparent diffusion coefficient value on diffusion-weighted MR imaging and Gleason score in prostate cancer

Background: Osteoporosis is a systemic skeletal disorder marked by reduced bone density and microarchitectural deterioration, leading to increased fracture risk. While the dual-energy X-ray absorptiometry (DEXA) scan is the World Health Organization (WHO)-recommended diagnostic standard, its limitations necessitate alternative methods. Emerging magnetic resonance imaging (MRI) techniques, radiomics, and machine learning promise to enhance osteoporosis diagnosis through detailed analysis of lumbar MRI apparent diffusion coefficient (ADC) maps, potentially revolutionizing early detection and treatment strategies. Objectives: In this study, we are going to evaluate the performance of machine learning (ML) models using radiomics features of lumbar MRI ADC map for osteoporosis detection, and to identify significant features and their diagnostic thresholds. Specific performance metrics such as accuracy, sensitivity, specificity, and Area Under the receiver operating characteristic (ROC) Curve (AUC) were assessed. Patients and Methods: This retrospective study employed a cross-sectional design, with a total of 140 cases, including 21 with osteoporosis. The study's inclusion criteria consisted of concurrent lumbar MRI and DEXA within a year, while exclusion criteria included infectious or neoplastic lumbar lesions, fractures, instrumentation, significant osteodegenerative changes, cases where the first four lumbar vertebrae were not included in the imaging field, and absence of diffusion-weighted imaging. Manual segmentation of lumbar vertebrae from ADC maps was performed to create a comprehensive dataset, comprising 5,580 radiomics features per case. Subsequently, the top five features selected by fast correlation-based filter (FCBF) were used to test the performance of seven Machine Learning algorithms (k-Nearest neighbors, decision tree, random forest, logistic regression, support vector machine, naive bayes, and neural network). Statistical tests and ROC curve analysis were conducted to determine the significance and thresholds of these features. Results: The study included 140 cases, with 132 females (94.3%) and 8 males (5.7%), and a mean age of 65.32 ± 8.50 years. The mean BMI was 31.43 ± 5.53 kg/m² for females and 26 ± 3.59 kg/m² for males. In terms of demographic differences, no significant age difference was found between the osteoporotic and non-osteoporotic groups (P = 0.889). However, the osteoporotic group had significantly lower mean body weight (64.90 ± 10.13 kg vs. 74.68 ± 13.94 kg, P = 0.003) and BMI (27.40 ± 4.38 kg/m² vs. 31.77 ± 5.52 kg/m², P = 0.001) compared to the non-osteoporotic group. The median interval between DEXA and lumbar MRI was 1 month (range 0.1 - 11.87 months). The Neural Network model demonstrated the highest performance with an AUC of 0.616 and a classification accuracy of 0.764 using all features. The Naive Bayes model, using the top five features selected by FCBF, showed the highest performance with an AUC of 0.913, accuracy of 0.907, sensitivity of 0.667, and specificity of 0.95. All ML models’ performance were elevated by feature selection. Independent t-tests and Mann-Whitney U tests identified 521 and 670 significant features, respectively (P &lt; 0.05). ROC analysis revealed 58 features with AUC values above 0.70. Conclusion: This study's findings suggest that ML models, particularly the Naive Bayes algorithm, can effectively use lumbar ADC map radiomics to diagnose osteoporosis. These findings could enhance early detection and treatment strategies, potentially improving patient outcomes and reducing the burden of osteoporotic fractures. This study also established threshold values for significant features.

A retrospective evaluation of diffusion-weighted imaging, apparent diffusion coefficient (ADC) maps, and T2-weighted images in patients with cervical compression myelopathy. To correlate high signal intensity on ADC maps and T2-weighted images to neurologic severity and radiologic spinal cord compression. Previous studies indicated that the ADC map was more sensitive in detection of cervical compressive myelopathy than T2-weighted imaging. The relationship to neurologic severity has not been previously published. In 100 patients with or without cervical compressive myelopathy, the magnetic resonance appearance of the spinal cord on ADC maps and T2-weighted images was evaluated independently by 2 readers. On the basis of the presence or absence of abnormality, patients were categorized into 4 groups by type of intramedullary change. The degree of canal compression and the Japanese Orthopedic Association (JOA) score for cervical myelopathy were compared among the 4 types. The type I group consisted of 32 patients without signal change on either ADC maps or T2-weighted images. The type II group had 33 patients with high signal intensity only on the ADC map. The type III group contained 28 patients with high signal intensity on both the ADC map and the T2-weighted images. The type IV group comprised 7 patients with high signal intensity only on T2-weighted images. The degree of canal compression and the JOA scores were significantly different among the 4 groups (P < 0.005). Most type III (25 of 28) and type IV (7 of 7) patients had severe cord compression. Average JOA scores in each type were (I) 16.0, (II) 14.7, (III) 11.7, and (IV) 8.7. ADC maps demonstrated internal changes in the early stages of chronic spinal cord compression, but had limitations for the detection of intramedullary changes in late-stage myelopathy.

This study aimed to evaluate the effectiveness of apparent diffusion coefficient (ADC) parameters in distinguishing between Prostate Cancer Research International Active Surveillance (PRIAS) non-reclassification and reclassification groups during active surveillance (AS) of prostate cancer. We included 55 patients who fulfilled the PRIAS criteria and underwent ≥ 2 magnetic resonance imaging (MRI) including diffusion-weighted imaging with an interval of ≤ 3years between baseline and second MRI. A mono-exponential fitting model was used to automatically create ADC maps with minimum b-values of 0 and maximum of 2000s/mm2. For detectable lesions on ADC maps, the lesions were manually segmented on each slice of the ADC maps. For undetectable lesions, the corresponding normal-appearing zone of the lobe on each slice of ADC maps was segmented. The ADC data for each slice were summed to obtain the 25th, 50th, and 75th percentile ADC values of the histogram at baseline and second MRI. These ADC parameters at baseline and second MRI, and the changes of ADC parameters from baseline to second MRI were compared between PRIAS non-reclassification and reclassification groups. The PRIAS reclassification group had significantly lower 25th, 50th, and 75th percentile ADC values at second MRI compared to the non-reclassification group. The non-reclassification group had significantly lower changes in ADC values in these percentiles compared to the reclassification group. The ADC parameters at second MRI and the changes from baseline to second MRI may be effective distinguishing factors between PRIAS non-reclassification and reclassification groups.