The prognostic value of mean apparent diffusion coefficient measured with diffusion-weighted magnetic resonance image in patients with prostate cancer treated with definitive radiotherapy

PURPOSE

To assess the correlation between initial tumor apparent diffusion coefficient (ADC) values and clinicopathological parameters in prostate cancer (PCa) patients treated with definitive radiotherapy (RT). Additionally, the prognostic factors for freedom from biochemical failure (FFBF) and progression-free survival (PFS) in this patient cohort were analyzed.

MATERIALS AND METHODS

The clinical data of 503 patients with biopsy-confirmed PCa were evaluated retrospectively. All patients had clearly evident tumors on diffusion-weighted magnetic resonance imaging (DW-MRI) for ADC values. Univariable and multivariable analyses were used to determine prognostic factors for FFBF and PFS.

RESULTS

The median follow-up was 72.9 months. The 5-year FFBF and PFS rates were 93.2% and 86.2%, respectively. Significantly lower ADC values were found in patients with a high PSA level; advanced clinical stage; higher ISUP score, and higher risk group than their counterparts. Receiver operating characteristic (ROC) curve analysis revealed an ADC cut-off value of 0.737 × 10^-3 mm²/sec for tumor recurrence. Patients who progressed had a lower mean ADC value than those who did not (0.712±0.158 vs. 1.365±0.227 × 10^-3 mm²/sec; p<0.001). There was a significant difference in 5-year FFBF (96.3% vs. 90%; p<0.001) and PFSrates (83.8% vs. 73.5%; p=0.002) between patients with higher and lower mean ADC values. The FFBF and PFS were found to be correlated with tumor ADC value and ISUP grades in multivariable analysis. Additionally, older age was found to be a significant predictor of worse PFS.

CONCLUSIONS

Lower ADC values were found in patients with high-risk characteristics such as a high serum PSA level, stage or grade of tumor, or high-risk disease, implying that ADC values could be used to predict prognosis. Lower ADC values and higher ISUP grades were associated with an increased risk of BF and progression, implying that treatment intensification may be required in these patients.

Whole lesion histogram analysis of apparent diffusion coefficient predicts therapy response in locally advanced rectal cancer

BACKGROUND

Whole-tumor apparent diffusion coefficient (ADC) histogram analysis is relevant to predicting the neoadjuvant chemoradiation therapy (nCRT) response in patients with locally advanced rectal cancer (LARC).

AIM

To evaluate the performance of ADC histogram-derived parameters for predicting the outcomes of patients with LARC.

METHODS

This is a single-center, retrospective study, which included 48 patients with LARC. All patients underwent a pre-treatment magnetic resonance imaging (MRI) scan for primary tumor staging and a second restaging MRI for response evaluation. The sample was distributed as follows: 18 responder patients (R) and 30 non-responders (non-R). Eight parameters derived from the whole-lesion histogram analysis (ADCmean, skewness, kurtosis, and ADC10^th, 25^th, 50^th, 75^th, 90^th percentiles), as well as the ADCmean from the hot spot region of interest (ROI), were calculated for each patient before and after treatment. Then all data were compared between R and non-R using the Mann-Whitney U test. Two measures of diagnostic accuracy were applied: the receiver operating characteristic curve and the diagnostic odds ratio (DOR). We also reported intra- and interobserver variability by calculating the intraclass correlation coefficient (ICC).

RESULTS

Post-nCRT kurtosis, as well as post-nCRT skewness, were significantly lower in R than in non-R (both P < 0.001, respectively). We also found that, after treatment, R had a larger loss of both kurtosis and skewness than non-R (∆%kurtosis and ∆skewness, P < 0.001). Other parameters that demonstrated changes between groups were post-nCRT ADC10^th, ∆%ADC10^th, ∆%ADCmean, and ROI ∆%ADCmean. However, the best diagnostic performance was achieved by ∆%kurtosis at a threshold of 11.85% (Area under the receiver operating characteristic curve [AUC] = 0.991, DOR = 376), followed by post-nCRT kurtosis = 0.78 × 10^-3 mm²/s (AUC = 0.985, DOR = 375.3), ∆skewness = 0.16 (AUC = 0.885, DOR = 192.2) and post-nCRT skewness = 1.59 × 10^-3 mm²/s (AUC = 0.815, DOR = 168.6). Finally, intraclass correlation coefficient analysis showed excellent intraobserver and interobserver agreement, ensuring the implementation of histogram analysis into routine clinical practice.

CONCLUSION

Whole-tumor ADC histogram parameters, particularly kurtosis and skewness, are relevant biomarkers for predicting the nCRT response in LARC. Both parameters appear to be more reliable than ADCmean from one-slice ROI.

Changes and clinical correlation of diffusion tensor imaging parameters of compressed spinal cord and nerve root in patients with cervical spondylosis

BACKGROUND

Diffusion tensor imaging (DTI) was used to quantitatively study the characteristics of the related spinal cord and nerve root compression parameters in patients with cervical spondylosis (CS), and diffusion tensor tractography (DTT) was used to visualize the spinal cord and nerve root and analyze their relevance to clinical evaluation.

METHODS

A total of 67 patients with CS and 30 healthy volunteers received 3.0 T magnetic resonance imaging. Cervical DTI and DTT were performed in all the participants, where the b value of DTI was set at 800 s/mm2. Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values of the spinal cord and cervical nerve roots were measured by using DTI. Patients with CS were scored according to the modified Japanese Orthopedic Association (mJOA) score.

RESULTS

In all the participants, the spinal cord and cervical nerve roots were clearly visible by DTT. Compared to the healthy volunteers, the FA values were significantly decreased and ADC values were significantly increased in patients with CS. mJOA score was significantly correlated with the DTI index (ADC and FA) values. Receiver operator characteristic curve analysis revealed that FA and ADC could identify mild, moderate, and severe CS.

CONCLUSIONS

DTI parameters of cervical spinal cord and nerve root compression are associated with the clinical evaluation of patients with CS and may be helpful in assessing the severity of CS.

Muscle-invasive bladder cancer: pretreatment prediction of response to neoadjuvant chemotherapy with diffusion-weighted MR imaging

PURPOSE

To investigate the usefulness of diffusion-weighted MR imaging with ADC value and histogram analysis of ADC in the prediction of response to neoadjuvant chemotherapy (NAC) in patients with muscle-invasive bladder cancer (MIBC).

METHODS

Fifty-eight consecutive patients with clinical T2-4aN0M0 MIBC who underwent MRI before and after NAC were enrolled in the prospective study. The evaluation of response to NAC was based on the pathologic T (pT) stage after surgery. Patients with non-muscle-invasive residual cancer (pTa, pTis, pT1) were defined as responders, while those with muscle-invasive residual cancer (≥ pT2) were defined as non-responders. The ADC value measured from a single-section region of interest and ADC histogram parameters derived from whole-tumor volume of interest in responder and non-responder were compared using the Mann-Whitney U test or independent samples t test. ROC curve analysis was used to evaluate the diagnostic performance of ADC value and ADC histogram parameters in predicting the response to NAC.

RESULTS

The pretreatment ADC value of responders ([1.33 (± 0.21)] × 10^-3mm²/s) was significantly higher than that of non-responders ([1.09 (± 0.08)] × 10^-3mm²/s) (P < .001). Most of the pretreatment ADC histogram parameters (Mean, 10th, 25th, 50th, 75th, and 90th percentiles) of responders were significantly higher than that of non-responders (P < .001). The AUC was highest for the pretreatment ADC value (0.88; 95% confidence interval: 0.77, 0.95; P < .001).

CONCLUSION

Diffusion-weighted MR imaging with ADC value and histogram analysis of ADC are useful to predict NAC response in patients with MIBC.

A case of solid-type pancreatic hamartoma presenting high apparent diffusion coefficient value: histopathological correlation and literature review

Pancreatic hamartoma is a rare tumor-like malformation and could mimic other pancreatic tumors including malignant neoplasm. Due to its rarity, magnetic resonance (MR) imaging findings including those diffusion-weighted (DW) imaging have not been clarified. We present a curious case of pancreatic hamartoma presenting high apparent diffusion coefficient (ADC) value with histopathological correlation. A 49-year-old woman with a pancreatic mass found incidentally on ultrasonography for medical checkup was referred to our institution for further examination and treatment because it slightly enlarged in the follow-up examination. Contrast-enhanced computed tomography (CT) and gadoxetic acid disodium-enhanced MR imaging revealed a well-demarcated solid mass of 13 mm in diameter in the pancreas body, which was gradually and homogeneously enhanced in the delayed/transient phase. It showed hyper intensity on T2-weight and DW images, and the mean ADC value was high (1.86 × 10^-3mm²/s). Laparoscopic distal pancreatectomy was conducted with suspicious preoperative diagnosis of pancreatic hypovascular neuroendocrine neoplasm (NEN). Histologically, the mass consisted of many disarranged small ducts without atypia embedded in abundant fibrous stroma and contained scant fatty tissue in the periphery, which was not identified on CT and MR images. There were no islets and peripheral nerves throughout the mass. Finally, it was pathologically diagnosed as a solid-type pancreatic hamartoma. Based on radiological-pathological correlation, it was considered that the abundant fibrous stroma and both the widely distributed myxomatous periductal stroma and scattered edematous stroma corresponded with delayed homogenous enhancement on CT/MR images and high ADC value, respectively. Although it is difficult to distinguish solid-type pancreatic hamartoma from other solid pancreatic neoplasms including hypovascular NEN on the basis of usual radiological findings, the high ADC value reflecting the specific pathology may be helpful for the differential diagnosis.

The Classification Power of Classical and Intra-voxel Incoherent Motion (IVIM) Fitting Models of Diffusion-weighted Magnetic Resonance Images: An Experimental Study

This study aims at investigating the classification power of different b-values of the diffusion-weighted magnetic resonance images (DWI) as indicator of prostate cancer. This paper investigates several techniques for analyzing data from DWI acquired at a range of b-values for the purpose of detecting prostate cancer. In the first phase of experiments, we analyze the available data by producing two main parametric maps using two common models, namely: the intra-voxel incoherent motion (IVIM) model and the mono-exponential ADC model. Accordingly, we evaluated the benign/malignant tissue classification potential of several parametric maps produced using different combinations of b-values and fitting models. In the second phase, we utilized the maps that performed best in the first phase of experiments to design a machine learning-based computer-assisted diagnosis system for the detection of early stage prostate cancer. The system performance was cross-validated using data from 20 patients. On a fivefold cross-validation scheme, a maximum accuracy and an area under the receiver operating characteristic (AUC) of 90% and 0.978, respectively, was achieved by a system that uses ADC maps fitted using the mono-exponential model at 11 different b-values. The results suggest that the proposed machine learning-based diagnosis system is potentially powerful in differentiating between malignant and benign prostate tissues when combined with carefully generated ADC maps.

Histogram Analysis Based on Apparent Diffusion Coefficient Maps of Bone Marrow in Multiple Myeloma: An Independent Predictor for High-risk Patients Classified by the Revised International Staging System

RATIONALE AND OBJECTIVES

The revised International Staging System (R-ISS) is the current risk stratifier for patients with newly diagnosed multiple myeloma (NDMM). We used histogram analysis based on apparent diffusion coefficient (ADC) maps of bone marrow to predict high-risk NDMM patients staged as R-ISS stage III.

MATERIAL AND METHODS

Sixty-one NDMM patients were recruited prospectively and underwent whole-body diffusion-weighted MRI. Mean ADC and four ADC-based histogram parameters of representative background bone marrow were quantified with TexRAD software, including ADC entropy, ADC standard deviation (SD), ADC skewness and ADC kurtosis. Diagnostic performance to discriminate R-ISS III from I/II disease was evaluated by receiver-operating characteristics curve (ROC). Univariate and multivariate analysis using stepwise logistic regression model was performed to identify predictors for R-ISS III.

RESULTS

ADC entropy of background marrow showed the highest areas under the ROC (0.784, sensitivity = 93.3%, specificity = 63.0%) for the detection of R-ISS stage III disease. Multivariate analysis showed that increased ADC entropy (>3.612) of background marrow can independently predict R-ISS stage III disease in the overall patients (Model 1 corrected for diffuse infiltration [DI] pattern: odds ratio [OR], 10.647; p = 0.008; Model 2 corrected for mean ADC: OR, 10.485; p = 0.010) and in the subgroup with DI pattern (OR, 7.043; p = 0.037).

CONCLUSION

ADC entropy of background marrow may serve as a sensitive imaging biomarker facilitating the detection of high-risk NDMM patients staged as R-ISS stage III. Increased ADC entropy of background marrow can independently predict R-ISS stage III in the overall patients and in the subgroup with DI pattern.

Added-value of texture analysis of ADC in predicting the survival of patients with 2018 FIGO stage IIICr cervical cancer treated by concurrent chemoradiotherapy

PURPOSE

To investigate the value of texture analysis of ADC in predicting the survival of patients with 2018 International Federation of Gynecology and Obstetrics (FIGO) stage IIICr cervical squamous cell cancer (CSCC) treated with concurrent chemoradiotherapy (CCRT).

METHODS

A total of 91 patients with stage IIICr CSCC treated by CCRT between January 2014 and December 2018 were retrospectivelyenrolled in this study. Clinical variables and 21 first-order texture features extracted from ADC maps were collected. Univariate and multivariate Cox hazard regression analyses were performed to evaluate these parameters in predicting progression-free survival (PFS) and overall survival (OS). The independent variables were combined to build a prediction model and compared with the 2018 FIGO staging system. Survival curves were generated using the Kaplan-Meier method, and the log-rank test was used for comparison.

RESULTS

Mean Absolute Deviation (MAD), T stage, and the number of lymph node metastasis (LNM) were independently associated with PFS, while MAD, energy, T stage, number of LNM, and tumor grade were independently associated with OS. The C-index values of the combined models for PFS and OS, which were respectively 0.750 and 0.832, were significantly higher compared to 2018 FIGO staging system values of 0.629 and 0.630, respectively (P < 0.05).

CONCLUSIONS

The texture analysis of the ADC maps could be used along with clinical prognostic biomarkers to predict PFS and OS in patients with stage IIICr CSCC treated by CCRT.

Differentiation of intracranial solitary fibrous tumor/hemangiopericytoma from atypical meningioma using apparent diffusion coefficient histogram analysis

This study aimed to investigate the value of apparent diffusion coefficient (ADC) histogram analysis in differentiating intracranial solitary fibrous tumor/hemangiopericytoma (SFT/HPC) from atypical meningioma (ATM). Retrospective analyzed the clinical, magnetic resonance imaging, and pathological data of 20 and 25 patients with SFT/HPC and ATM, respectively. Histogram analysis was performed on the axial ADC images using MaZda software, and nine histogram parameters were obtained, including mean, variance, skewness, kurtosis, and the 1^st (ADC1), 10^th (ADC10), 50^th (ADC50), 90^th (ADC90), and 99^th (ADC99) percentile ADC. Differences in ADC histogram parameters between SFT/HPC and ATM were compared by an independent t test or Mann-Whitney U test, while the statistically significant histogram parameters were further analyzed by drawing receiver operating characteristic (ROC) curves to evaluate the differential diagnostic performance. Among the nine ADC histogram parameters we extracted, the mean, ADC1, ADC10, ADC50, and ADC90 in the SFT/HPC group were greater than those of ATM, and significant differences were observed (all P < 0.05). ROC analysis showed that the ADC1 generated the highest area under the curve (AUC) value of 0.920 in distinguishing the two tumors, when using 91.00 as the optimal threshold. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value in distinguishing between SFT/HPC and ATM were 84.00%, 85.00%, 84.44%, 87.50%, and 81.00%, respectively. ADC histogram analysis can be a reliable tool to differentiate between SFT/HPC and ATM, with the ADC1 being the most promising potential parameter.

MRI-based radiomics signature and clinical factor for predicting H3K27M mutation in pediatric high-grade gliomas located in the midline of the brain

OBJECTIVE

To develop a nomogram based on MRI radiomics and clinical features for preoperatively predicting H3K27M mutation in pediatric high-grade gliomas (pHGGs) with a midline location of the brain.

METHODS

The institutional database was reviewed to identify patients with pHGGs with a midline location of the brain who underwent tumor biopsy with preoperative MRI scans between June 2016 and June 2021. A total of 107 patients with pHGGs, including 79 patients with H3K27M mutation, were consecutively included and randomly divided into training and test sets. Radiomics features were extracted from fluid-attenuated inversion recovery (FLAIR), diffusion-weighted (DW) and post-contrast T1-weighted images, and apparent diffusion coefficient (ADC) maps. The minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression were performed for radiomics signature construction. Clinical and radiological features were analyzed to select clinical predictors. A nomogram was then developed by incorporating the radiomics signature and selected clinical predictors.

RESULTS

Nine radiomics features were selected to construct the radiomics signature, which showed a favorable discriminatory ability in training and test sets with an area under the curve (AUC) of 0.95 and 0.92, respectively. Ring enhancement was identified as an independent clinical predictor (p < 0.01). The nomogram, constructed with radiomics signature and ring enhancement, showed good calibration and discrimination in training and testing sets (AUC: 0.95 and 0.90 respectively).

CONCLUSIONS

The nomogram which combined radiomics signature and ring enhancement had a satisfactory ability to predict H3K27M mutation in pHGGs with a midline of the brain.

KEY POINTS

• Conventional MRI features were not powerful enough to predict H3K27M mutation status in pediatric high-grade gliomas (pHGGs) with a midline location of the brain. • An MRI-based radiomics signature showed satisfactory ability to predict H3K27M mutation status of pHGGs located in the midline of the brain. • Associating the radiomics signature with clinical factors improved predictive performance.

Improving interobserver agreement and performance of deep learning models for segmenting acute ischemic stroke by combining DWI with optimized ADC thresholds

OBJECTIVES

To examine the role of ADC threshold on agreement across observers and deep learning models (DLMs) plus segmentation performance of DLMs for acute ischemic stroke (AIS).

METHODS

Twelve DLMs, which were trained on DWI-ADC-ADC combination from 76 patients with AIS using 6 different ADC thresholds with ground truth manually contoured by 2 observers, were tested by additional 67 patients in the same hospital and another 78 patients in another hospital. Agreement between observers and DLMs were evaluated by Bland-Altman plot and intraclass correlation coefficient (ICC). The similarity between ground truth (GT) defined by observers and between automatic segmentation performed by DLMs was evaluated by Dice similarity coefficient (DSC). Group comparison was performed using the Mann-Whitney U test. The relationship between the DSC and ADC threshold as well as AIS lesion size was evaluated by linear regression analysis. A p < .05 was considered statistically significant.

RESULTS

Excellent interobserver agreement and intraobserver repeatability in the manual segmentation (all ICC > 0.98, p < .001) were achieved. The 95% limit of agreement was reduced from 11.23 cm² for GT on DWI to 0.59 cm² for prediction at an ADC threshold of 0.6 × 10^-3 mm²/s combined with DWI. The segmentation performance of DLMs was improved with an overall DSC from 0.738 ± 0.214 on DWI to 0.971 ± 0.021 on an ADC threshold of 0.6 × 10^-3 mm²/s combined with DWI.

CONCLUSIONS

Combining an ADC threshold of 0.6 × 10^-3 mm²/s with DWI reduces interobserver and inter-DLM difference and achieves best segmentation performance of AIS lesions using DLMs.

KEY POINTS

• Higher Dice similarity coefficient (DSC) in predicting acute ischemic stroke lesions was achieved by ADC thresholds combined with DWI than by DWI alone (all p < .05). • DSC had a negative association with the ADC threshold in most sizes, both hospitals, and both observers (most p < .05) and a positive association with the stroke size in all ADC thresholds, both hospitals, and both observers (all p < .001). • An ADC threshold of 0.6 × 10^-3 mm²/s eliminated the difference of DSC at any stroke size between observers or between hospitals (p = .07 to > .99).

Longitudinal evaluation of apparent diffusion coefficient values as a predictor of Prostate Cancer Research International Active Surveillance reclassification

PURPOSE

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.

METHODS

We included 55 patients who fulfilled the PRIAS criteria and underwent ≥ 2 magnetic resonance imaging (MRI) including diffusion-weighted imaging with an interval of ≤ 3 years 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 2000 s/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.

RESULTS

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.

CONCLUSION

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.

Diffusion-Weighted Imaging of Different Breast Cancer Molecular Subtypes: A Systematic Review and Meta-Analysis

Background

Magnetic resonance imaging can be used to diagnose breast cancer (BC). Diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) can be used to reflect tumor microstructure.

Objectives

This analysis aimed to compare ADC values between molecular subtypes of BC based on a large sample of patients.

Method

The MEDLINE library and Scopus database were screened for the associations between ADC and molecular subtypes of BC up to April 2020. The primary end point of the systematic review was the ADC value in different BC subtypes. Overall, 28 studies were included.

Results

The included studies comprised a total of 2,990 tumors. Luminal A type was diagnosed in 865 cases (28.9%), luminal B in 899 (30.1%), human epidermal growth factor receptor (Her2)-enriched in 597 (20.0%), and triple-negative in 629 (21.0%). The mean ADC values of the subtypes were as follows: luminal A: 0.99 × 10^-3 mm²/s (95% CI 0.94-1.04), luminal B: 0.97 × 10^-3 mm²/s (95% CI 0.89-1.05), Her2-enriched: 1.02 × 10^-3 mm²/s (95% CI 0.95-1.08), and triple-negative: 0.99 × 10^-3 mm²/s (95% CI 0.91-1.07).

Conclusions

ADC values cannot be used to discriminate between molecular subtypes of BC.

Kinetic control concept for the diffusion processes of paracetamol active molecules across affinity polymer membranes from acidic solutions

Background

Paracetamol compound remains the most used pharmaceutical as an analgesic and antipyretic for pain and fever, often identified in aquatic environments. The elimination of this compound from wastewater is one of the critical operations carried out by advanced industries. Our work objective was to assess studies based on membrane processes by using two membranes, polymer inclusion membrane and grafted polymer membrane containing gluconic acid as an extractive agent for extracting and recovering paracetamol compound from aqueous solutions.

Result

The elaborated membrane characterizations were assessed using Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). Kinetic and thermodynamic models have been applied to determine the values of macroscopic (P and J₀), microscopic (D* and K_ass), activation and thermodynamic parameters (E_a, ΔH^#, ΔS^#, ΔH^#_diss, and ΔH^#_th). All results showed that the PVA–GA was more performant than its counterpart GPM–GA, with apparent diffusion coefficient values (10⁷D*) of 41.807 and 31.211 cm² s⁻¹ respectively, at T = 308 K. In addition, the extraction process for these membranes was more efficient at pH = 1. The relatively low values of activation energy (Ea), activation association enthalpy (ΔH^≠_ass), and activation dissociation enthalpy (ΔH^≠_diss) have indicated a kinetic control for the oriented processes studied across the adopted membranes much more than the energetic counterpart.

Conclusion

The results presented for the quantification of oriented membrane process ensured clean, sustainable, and environmentally friendly methods for the extraction and recovery of paracetamol molecule as a high-value substance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13065-021-00794-7.

Primary Osseous Low-grade Myxofibrosarcoma of Metatarsal Masquerading as Enchondroma: A Case Report

Introduction

Low-grade myxofibrosarcoma (LGMFS) is a neoplasm of soft tissues. According to the World Health Organization, LGMFS is a malignant myofibroblastic tumor arising from deep soft tissues with potential for recurrence and late metastatic spread. The incidence estimates are 0.18/million, accounting for 0.6% of all soft-tissue sarcomas. It can directly invade the bone and metastasize to bone; however, primary osseous low-grade myxofibrosarcoma is a rare entity. Thus, recognizing atypical presentations of uncommon neoplasms are a pertinent skill for the radiologist due to significant implications for management.

Case Report

A 40-year-old male with complaints of midfoot pain was referred to radiology department for imaging. Radiographs of the foot showed a lytic mildly expansile lesion in the first metacarpal with wide zone of transition and no sclerotic margin or matrix calcification. Magnetic resource imaging (MRI) and computed tomography (CT) examination demonstrated well defined lobulated lesion which appeared heterogeneously hyperintense on T2W images with cortical breach, extraosseous soft-tissue component, and early rapid progressive enhancement. The radiological diagnosis of enchondroma with pathological fracture was considered. Atypical findings of early progressive enhancement and extraosseous soft-tissue component were, however, incongruous with enchondroma and possibility of an aggressive/malignant etiology was also considered. Hence, the lesion was biopsied and diagnosis of LGMFS was made and subsequently confirmed on excised specimen. Follow-up CT scan post 6 months of surgical resection demonstrated no recurrence.

Conclusion

Primary osseous LGMFS is a rare entity and radiologically mimics enchondromas. Both LGMFS and enchondromas show T2W hyperintensity. MRI features that distinguish LGMFS from enchondroma include low apparent diffusion coefficient values and slow progressive enhancement in dynamic contrast-enhanced MRI.

Machine learning to differentiate small round cell malignant tumors and non-small round cell malignant tumors of the nasal and paranasal sinuses using apparent diffusion coefficient values

OBJECTIVE

We used radiomics feature-based machine learning classifiers of apparent diffusion coefficient (ADC) maps to differentiate small round cell malignant tumors (SRCMTs) and non-SRCMTs of the nasal and paranasal sinuses.

MATERIALS

A total of 267 features were extracted from each region of interest (ROI). Datasets were randomized into two sets, a training set (∼70%) and a test set (∼30%). We performed dimensional reductions using the Pearson correlation coefficient and feature selection analyses (analysis of variance [ANOVA], relief, recursive feature elimination [RFE]) and classifications using 10 machine learning classifiers. Results were evaluated with a leave-one-out cross-validation analysis.

RESULTS

We compared the AUC for all the pipelines in the validation dataset using FeAture Explorer (FAE) software. The pipeline using RFE feature selection and Gaussian process classifier yielded the highest AUCs with ten features. When the "one-standard error" rule was used, FAE produced a simpler model with eight features, including Perc.01%, Perc.10%, Perc.90%, Perc.99%, S(1,0) SumAverg, S(5,5) AngScMom, S(5,5) Correlat, and WavEnLH_s-2. The AUCs of the training, validation, and test datasets achieved 0.995, 0.902, and 0.710, respectively. For ANOVA, the pipeline with the auto-encoder classifier yielded the highest AUC using only one feature, Perc.10% (training/validation/test datasets: 0.886/0.895/0.809, respectively). For the relief, the AUCs of the training, validation, and test datasets that used the LRLasso classifier using five features (Perc.01%, Perc.10%, S(4,4) Correlat, S(5,0) SumAverg, S(5,0) Contrast) were 0.892, 0.886, and 0.787, respectively. Compared with the RFE and relief, the results of all algorithms of ANOVA feature selection were more stable with the AUC values higher than 0.800.

CONCLUSIONS

We demonstrated the feasibility of combining artificial intelligence with the radiomics from ADC values in the differential diagnosis of SRCMTs and non-SRCMTs and the potential of this non-invasive approach for clinical applications.

KEY POINTS

• The parameter with the best diagnostic performance in differentiating SRCMTs from non-SRCMTs was the Perc.10% ADC value. • Results of all the algorithms of ANOVA feature selection were more stable and the AUCs were higher than 0.800, as compared with RFE and relief. • The pipeline using RFE feature selection and Gaussian process classifier yielded the highest AUC.

Non-timely clinically applicable ADC ratio in prostate mpMRI: a comparison with fusion biopsy results

PURPOSE

The purpose of the study was to assess the diagnostic accuracy of ADC ratio and to evaluate its efficacy in reducing the number of false positives in prostatic mpMRI.

MATERIALS AND METHODS

All patients who underwent an mpMRI and a targeted fusion biopsy in our institution from 2016 to 2021 were retrospectively selected. Two experienced readers (R1 and R2) independently evaluated the images, blindly to biopsy results. The radiologists assessed the ADC ratios by tracing a circular 10 mm² ROI on the biopsied lesion and on the apparently benign contralateral parenchyma. Prostate cancers were divided into non-clinically significant (nsPC, Gleason score = 6) and clinically significant (sPC, Gleason score ≥ 7). ROC analyses were performed.

RESULTS

167 patients and188 lesions were included. Concordance was 0.62 according to Cohen's K. ADC ratio showed an AUC for PCAs of 0.78 in R1 and 0.8 in R2. The AUC for sPC was 0.85 in R1 and 0.84 in R2. The 100% sensitivity cut-off for sPCs was 0.65 (specificity 25.6%) in R1 and 0.66 (specificity 27.4%) in R2. Forty-three benign or not clinically significant lesions were above the 0.65 threshold in R1; 46 were above the 0.66 cut-off in R2. This would have allowed to avoid an equal number of unnecessary biopsies at the cost of 2 nsPCs in R1 and one nsPC in R2.

CONCLUSION

In our sample, the ADC ratio was a useful and accurate tool that could potentially reduce the number of false positives in mpMRI.

Differentiating atypical meningioma from anaplastic meningioma using diffusion weighted imaging

PURPOSE

To explore the value of MRI conventional features and apparent diffusion coefficient (ADC) on the differential diagnosis of atypical meningioma (AtM) and anaplastic meningioma (AnM).

MATERIALS AND METHODS

This retrospective study analyzed the preoperative clinical data, MRI conventional features, and DWI data of 55 AtM and 25 AnM confirmed by pathology in our hospital. The clinical features, MRI conventional features, ADCmean, ADCmin, and relative ADC (rADC) values were compared between the two tumors by Chi-square test or an independent sample t-test. Receiver operating characteristic curve (ROC) and binary logistic regression analysis were used to evaluate the diagnostic efficacy of each parameter to differentiate between these tumors.

RESULTS

The MRI conventional features had a certain ability to distinguish AnM and AtM, with an area under the curve value (AUC) of 0.824 (95% CI, 0.723-0.900). The ADCmean, ADCmin, and rADC values were significantly higher in AtM compared to AnM (all P < 0.05). ADCmean had the best identification effect with an AUC of 0.867 (95% CI, 0.772-0.933) among them, at an cut-off of 0.817 × 10^-3 mm²/s, the sensitivity and specificity of distinguishing AtM from AnM were 78.18% and 88.00%, respectively. A combination of ADCmean and MRI conventional features showed the optimum discrimination ability for the two tumors, the AUC, sensitivity, specificity, and accuracy were 0.918 (95% CI, 0.835-0.967), 80.00%, 94.55%, and 90.00%, respectively.

CONCLUSION

MRI conventional features combined with ADCmean, as a non-invasive method, has potential clinical value in the preoperative diagnosis of AtM and AnM.

Percent change in apparent diffusion coefficient and plasma EBV DNA after induction chemotherapy identifies distinct prognostic response phenotypes in advanced nasopharyngeal carcinoma

BACKGROUND

To evaluate the prognostic value of the apparent diffusion coefficient (ADC) derived from diffusion-weighted magnetic resonance imaging (MRI) and monitor the early treatment response to induction chemotherapy (IC) with plasma EBV DNA in locoregionally advanced nasopharyngeal carcinoma (LA-NPC).

RESULTS

A total of 307 stage III-IVb NPC patients were prospectively enrolled. All patients underwent MRI examinations to calculate ADC and plasma EBV DNA measurements pretreatment and post-IC. The participants' ADC value of 92.5% (284/307) increased post-IC. A higher percent change in ADC value (ΔADC%_high group) post-IC was associated with a higher 5-year OS rate (90.7% vs 74.9%, p < 0.001) than those in the ΔADC%_low group. Interestingly, ΔADC% was closely related to the response measured by RECIST 1.1 (p < 0.001) and plasma EBV DNA level (p = 0.037). The AUC significantly increased when post-IC plasma EBV DNA was added to ΔADC% to predict treatment failure. Thus, based on ΔADC% and plasma EBV DNA, we further divided the participants into three new prognostic response phenotypes (early response, intermediate response, and no response) that correlated with disparate risks of death (p = 0.001), disease progression (p < 0.001), distant metastasis (p < 0.001), and locoregional relapse (p < 0.001).

CONCLUSION

The percentage change in ADC post-IC is indicative of treatment response and clinical outcome. ΔADC% and plasma EBV DNA-based response phenotypes may provide potential utility for early termination of treatment and allow guiding risk-adapted therapeutic strategies for LA-NPC.

[Predictive value of magnetic resonance imaging characteristics before and after radiotherapy for the occurrence of severe radiation-induced late rectal injury in patients with rectal cancer]

Objective: Severe radiation-induced late rectal injury (sRLRI) directly affects the quality of life of patients with rectal cancer. Effective prediction of sRLRI before surgery may provide important information for the selection of surgical strategies and perioperative managements. The purpose of this study is to evaluate the feasibility of predicting sRLRI based on magnetic resonance imaging (MRI) features before and after radiotherapy for rectal cancer. Methods: This was a diagnostic study. Clinical and imaging data of 90 patients with rectal cancer receiving long-term radiotherapy from June 2013 to July 2018 in the Sixth Affiliated Hospital of Sun Yat-sen University were collected retrospectively. Case inclusion criteria: (1) rectal cancer was diagnosed by pathology and age of ≥ 18 years old; (2) patients received neoadjuvant chemoradiotherapy and anterior rectal resection; (3) follow up time ≥ 3 years; (4) patients had no history of other neoplasm. Exclusion criteria: (1) patients did not receive MRI examination in our hospital within 2 weeks before and/or 8 weeks after radiotherapy; (2) images were not good enough for evaluation; (3) medical records were incomplete; (4) patients had severe gastrointestinal diseases. According to the RTOG/EORTC classification criteria for radiation reactions, severe complications of grade 3-4 requiring surgical management were defined as sRLRI. T2WI and DWI images before and after radiotherapy were evaluated. The rectal wall thickness, bladder wall thickness, rectal sacral spacing and apparent diffusion coefficient (ADC) were measured. The receiver operating characteristic (ROC) curve was used to evaluate the predictive value of the above indicators for sRLRI. Results: Among the 90 patients with rectal cancer, 34 (37.8%) developed sRLRI. Before radiotherapy, the median rectal wall thickness of sRLRI and non-sRLRI patients was 4.530 mm and 4.355 mm, respectively; the median bladder wall thickness was 3.962 mm and 3.868 mm, respectively; the median rectal sacral spacing was 15.557 mm and 12.433 mm, respectively; the median ADC value of rectal wall was 1.620 ×10(-3) mm(2)/s and 1.653 ×10(-3) mm(2)/s, respectively. There were no significant differences in above indicators between sRLRI and non-sRLRI patients (all P>0.05). After radiotherapy, compared with non-sRLRI patients, sRLRI patients had increased rectal wall thickness (median: 8.239 mm vs. 6.223 mm, Z=-3.512, P=0.001), rectal sacral spacing (median: 17.728 mm vs. 13.885 mm, Z=-2.247, P=0.025), and change of rectal wall thickness after radiotherapy (median: 98.106% vs. 49.584%, Z=-4.169, P<0.001). After radiotherapy, there were no significant differences in the bladder wall thickness and its change value, the ADC value of rectal wall and its change rate before and after radiotherapy between the two groups (all P>0.05). The area under the curve (AUC) of the change rates of rectal wall thickness after radiotherapy, rectal wall thickness and rectal sacral spacing after radiotherapy for predicting sRLRI was 0.763, 0.722 and 0.642, respectively, while the sensitivity was 85.3%, 70.6% and 76.5%, respectively, and the specificity was 64.3%, 71.4% and 57.1%, respectively. Conclusion: Based on MRI examinations, assessments of rectal wall thickness after radiotherapy, the change rate of rectal wall thickness after radiotherapy, and rectal sacral spacing after radiotherapy are helpful for evaluating the risk of sRLRI after radiotherapy for patients with rectal cancer.

3.0 Tesla normative diffusivity in 3rd trimester fetal brain

PURPOSE

Apparent diffusion coefficient (ADC) values in the developing fetus provide valuable information on the diagnosis and prognosis of prenatal brain pathologies. Normative ADC data has been previously established in 1.5 T MR scanners but lacking in 3.0 T scanners. Our objective was to measure ADC values in various brain areas in a cohort of normal singleton fetuses scanned in a 3.0 T MR scanner.

METHODS

DWI (diffusion-weighted imaging) was performed in 47 singleton fetuses with normal or questionably abnormal results on sonography followed by normal structural MR imaging. ADC values were measured in cerebral lobes (frontal, parietal, temporal lobes), basal ganglia, and pons. Regression analysis was used to examine gestational age-related changes in regional ADC.

RESULTS

Median gestational age was 30.1 weeks (range, 26-34 weeks). There was a significant effect of region on ADC values, whereby ADC values were highest in cerebral lobes (parietal > frontal > temporal lobes), compared with basal ganglia. The lowest values were found in the pons. On regression analysis, there was a decrease in ADC values in basal ganglia and pons with increasing gestational age. ADC values in frontal, parietal, and temporal lobes were stable in our cohort.

CONCLUSION

Regional brain ADC values in 3.0 T scanners are comparable with previously reported values in 1.5 T scanners, with similar changes over gestational age. Using 3.0 T scanners is increasing worldwide. For fetal imaging, establishing normal ADC values is critical as DWI enables a sensitive and quantitative technique to evaluate normal and abnormal brain development.

MRI-based radiomics and ADC values are related to recurrence of endometrial carcinoma: a preliminary analysis

Background

To identify predictive value of apparent diffusion coefficient (ADC) values and magnetic resonance imaging (MRI)-based radiomics for all recurrences in patients with endometrial carcinoma (EC).

Methods

One hundred and seventy-four EC patients who were treated with operation and followed up in our institution were retrospectively reviewed, and the patients were divided into training and test group. Baseline clinicopathological features and mean ADC (ADC_mean), minimum ADC (ADC_min), and maximum ADC (ADC_max) were analyzed. Radiomic parameters were extracted on T2 weighted images and screened by logistic regression, and then a radiomics signature was developed to calculate the radiomic score (radscore). In training group, Kaplan–Meier analysis was performed and a Cox regression model was used to evaluate the correlation between clinicopathological features, ADC values and radscore with recurrence, and verified in the test group.

Results

ADC_mean showed inverse correlation with recurrence, while radscore was positively associated with recurrence. In univariate analyses, FIGO stage, pathological types, myometrial invasion, ADC_mean, ADC_min and radscore were associated with recurrence. In the training group, multivariate Cox analysis showed that pathological types, ADC_mean and radscore were independent risk factors for recurrence, which were verified in the test group.

Conclusions

ADC_mean value and radscore were independent predictors of recurrence of EC, which can supplement prognostic information in addition to clinicopathological information and provide basis for individualized treatment and follow-up plan.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08988-x.

The relationship between the apparent diffusion coefficient and the Ki-67 proliferation index in intracranial solitary fibrous tumor/hemangiopericytoma

This study evaluated the value of the apparent diffusion coefficient (ADC) in distinguishing grade II and III intracranial solitary fibrous tumors/hemangiopericytomas and explored the correlation between ADC and Ki-67. The preoperative MRIs of 37 patients treated for solitary fibrous tumor/hemangiopericytoma (grade II, n = 15 and grade III, n = 22) in our hospital from 2011 to October 2020 were retrospectively analyzed. We compared the difference between the minimum, average, maximum, and relative ADCs based on tumor grade and examined the correlation between ADC and Ki-67. Receiver operating characteristic curve analysis was used to analyze the diagnostic efficiency of the ADC. There were significant differences in the average, minimum, and relative ADCs between grade II and III patients. The optimal cutoff value for the relative ADC value to differentiate grade II and III tumors was 0.998, which yielded an area under the curve of 0.879. The Ki-67 proliferation indexes of grade II and III tumors were significantly different, and the average (r =  - 0.427), minimum (r =  - 0.356), and relative (r =  - 0.529) ADCs were significantly negatively correlated with the Ki-67 proliferation index. ADC can be used to differentiate grade II and III intracranial solitary fibrous tumors/hemangiopericytomas. Our results can be used to formulate a personalized surgical treatment plan before surgery.

Whole brain in vivo neuropathology: Imaging site-specific changes in brain structure over time following trimethyltin exposure in rats

Presented is a diffusion weighted imaging protocol with measures of apparent diffusion coefficient which when registered to a 3D MRI rat brain atlas provides site-specific information on 173 different brain areas. This protocol coined "in vivo neuropathology" was used to follow the progressive neurotoxic effects of trimethyltin on global gray matter microarchitecture. Four rats were given an IP injection of 7 mg/kg of the neurotoxin trimethyltin and imaged for changes in water diffusivity at 3- and 7-days post injections. At 3 days, there was a significant decrease in apparent diffusion coefficient, a proxy for cytotoxic edema, in several cortical areas and cerebellum. At 7 days the level of injury expanded to include most of the cerebral cortex, hippocampus, olfactory system, and cerebellum/brainstem corroborating much of the work done with traditional histopathology. Analysis is achieved with a minimum number of rats adhering to the laws and regulations around the humane care and use of laboratory animals, providing an alternative to the traditional tests for assessing drug neurotoxicity. "In vivo neuropathology" can minimize the cost, expedite the process, and identify subtle changes in site-specific brain microarchitecture across the entire brain.

Reliability of the freehand region-of-interest method in quantitative cerebral diffusion tensor imaging

Background

Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique used for evaluating changes in the white matter in brain parenchyma. The reliability of quantitative DTI analysis is influenced by several factors, such as the imaging protocol, pre-processing and post-processing methods, and selected diffusion parameters. The region-of-interest (ROI) method is most widely used of the post-processing methods because it is found in commercial software. The focus of our research was to study the reliability of the freehand ROI method using various intra- and inter-observer analyses.

Methods

This study included 40 neurologically healthy participants who underwent diffusion MRI of the brain with a 3 T scanner. The measurements were performed at nine different anatomical locations using a freehand ROI method. The data extracted from the ROIs included the regional mean values, intra- and inter-observer variability and reliability. The used DTI parameters were fractional anisotropy (FA), the apparent diffusion coefficient (ADC), and axial (AD) and radial (RD) diffusivity.

Results

The average intra-class correlation coefficient (ICC) of the intra-observer was found to be 0.9 (excellent). The single ICC results were excellent (> 0.8) or adequate (> 0.69) in eight out of the nine regions in terms of FA and ADC. The most reliable results were found in the frontobasal regions. Significant differences between age groups were also found in the frontobasal regions. Specifically, the FA and AD values were significantly higher and the RD values lower in the youngest age group (18–30 years) compared to the other age groups.

Conclusions

The quantitative freehand ROI method can be considered highly reliable for the average ICC and mostly adequate for the single ICC. The freehand method is suitable for research work with a well-experienced observer. Measurements should be performed at least twice in the same region to ensure that the results are sufficiently reliable. In our study, reliability was slightly undermined by artifacts in some regions such as the cerebral peduncle and centrum semiovale. From a clinical point of view, the results are most reliable in adults under the age of 30, when age-related changes in brain white matter have not yet occurred.