ADC Maps Research Articles

An MR-based radiomics nomogram including information from the peritumoral region to predict deep myometrial invasion in stage I endometrioid adenocarcinoma: a preliminary study.

To develop and validate an MR-based radiomics nomogram combining different imaging sequences (ADC mapping and T2 weighted imaging (T2WI)), different tumor regions (combined intra- and peritumoral regions), and different parameters (clinical features, tumor morphological features, and radiomics features) while considering different MR field strengths in predicting deep myometrial invasion (MI) in Stage I endometrioid adenocarcinoma (EEA). A total of 202 patients were retrospectively analyzed and divided into two cohorts (training cohort, 1.5 T MR, n = 131; validation cohort, 3.0 T MR, n = 71). Axial ADC mapping and T2WI were conducted. Radiomics features were extracted from intra- and peritumoral regions. Least absolute shrinkage and selection operator regression, univariate analysis, and multivariate logistic regression were used to select radiomics features and tumor morphological and clinical parameters. The area under the receiver operator characteristic curve (AUC) was calculated to evaluate the performance of the prediction model and radiomics nomogram. Ten radiomics features, 4 morphological parameters and 1 clinical characteristic were selected. The radiomics nomogram achieved good discrimination between the superficial and deep MI cohorts. The AUC was 0.927 (95% confidence interval [CI]: 0.865, 0.967) in the training cohort and 0.921 (95% CI: 0.872, 0.948) in the validation cohort. The specificity and sensitivity were 92.0 and 78.9% in the training cohort and 83.0 and 77.8% in the validation cohort, respectively. The radiomics nomogram showed good performance in predicting the depth of MI in Stage I EEA before surgery and might be useful for surgical patient management. An MR-based radiomics nomogram was useful for predicting deep MI in Stage I EEA patients (AUCtrain = 0.927, AUCvalidation = 0.921). The intra- and peritumoral radiomics features complemented each other. The nomogram was developed and validated with different MR field strengths, suggesting that the model demonstrates good generalizability.

Differences in apparent diffusion coefficient histogram analysis according to EGFR mutation status in brain metastasis due to lung adenocarcinoma.

The etiology, clinicopathological features, and prognosis of cancer in cases with EGFR mutations are different from those without mutations.OBJECTİVE:This study aims to evaluate the differences in ADC histogram analysis in brain metastases with EGFR mutation status in lung adenocarcinoma cases and the relationship between ADC histogram analysis differences and overall survival. In this retrospective case-control study, 30 patients (8 EGFR+/22 EGFR-) and 51 brain metastases (15 EGFR+/36 EGFR-) were included. ROI markings are first performed from each section, including metastasis in ADC mapping using FIREVOXEL software. Next, ADC histogram parameters are calculated. Overall survival analysis after brain metastasis (OSBM) is defined as the time from initial brain metastasis diagnosis to the time of death or last follow-up. Patient-based (by evaluating the largest lesion) and lesion-based (by evaluating all measurable lesions) statistical analyses are then performed. In the lesion-based analysis, skewness values are lower in EGFR+ patients, which is statistically significant (p = 0.012). The two groups have no significant difference regarding other ADC histogram analysis parameters, mortality, and overall survival (p > 0.05). In the ROC analysis, the most appropriate skewness cut-off value is determined as 0.321 to distinguish the EGFR mutation difference, and this value is statistically significant (sensitivity: 66.7%, specificity: 80.6%, AUC: 0.730) (p = 0.006).CONCLUSİON:The findings of this study provide valuable insights into the differences in ADC histogram analysis according to EGFR mutation status in brain metastases due to lung adenocarcinoma. The identified parameters, especially skewness, are potentially non-invasive biomarkers for predicting mutation status. Incorporating these biomarkers into routine clinical practice may aid treatment decision-making and prognostic assessment for patients. Further validation studies and prospective investigations are warranted to confirm the clinical utility of these findings and establish their potential for personalized therapeutic strategies and patient outcomes.

P05.02.A PRELIMINARY FINDINGS FROM THE MRI QUALITY ASSURANCE PROGRAMME FOR THE PROSPECTIVE MULTI-SITE AUSTRALIAN FIG ([FET-PET IN GLIOBLASTOMA) TROG 18.06 STUDY

Abstract BACKGROUND The O-(2-[18F]-fluoroethyl)-L-tyrosine positron emission tomography (FET-PET) in Glioblastoma (FIG) study is a prospective study evaluating the management impact of FET-PET imaging in up to 210 newly diagnosed adult glioblastoma patients across 10 Australian sites. Patients undergo contrast MRI and FET-PET at 3 timepoints: pre-chemoradiotherapy (CRT), one-month post-CRT and at suspected progression. MRI QA programme components, approach and integrated workflows are described here. MATERIAL AND METHODS The FIG MRI protocol comprised 3D-T1, 3D-FLAIR, Axial 2D DWI (including derived ADC map), DCE (including T1 mapping), DSC, Axial T2 and 3D T1-post-contrast (T1C) sequences, with SWI and 3D DIR sequences optional. QA components included data acquisition quality and completeness (standard and advanced), motion artefact, low contrast to noise or signal to noise, series description and susceptibility artifacts. Suitability of T1/T1C using modified RANO (mRANO) criteria was assessed. An optimised workflow involved site upload from local PACS systems to centralised database via TROG Central Quality Management System (CQMS) platform and collation into a central imaging database utilising MIM software. The MRI QA workflow encompassed automated anonymisation of DICOM data, data completeness and reconstruction evaluation, then review by two expert neuroimaging analysts. Sites received feedback with request for resubmission where required. RESULTS Between December 2021 and February 2023, MRI data in 74 patients across 9 sites with median of 6 patients (range 2-17) per site was submitted. A total of 141 MRI datasets across all timepoints were collected (per site range:4-35, median:13), with 43 imaging time-points selected for QA (per site range:3-12, median: 4). Importantly, 41/43 (95%) of initial datasets were deemed suitable for mRANO assessment, but only 13/43 (30%) were suitable for advanced MRI analysis. Very few datasets had motion or susceptibility artifacts, low contrast to noise or signal to noise, or incorrect series description. Technical issues identified included incomplete data (DCE - missing T1 maps, DWI - missing ADC maps), incorrect sequence reconstruction (DCE split series and Axial/Sagittal/Coronal reformats for 3D images) and lack of non-mandatory SWI sequences. Feedback to sites resulted in improvements in DCE sequence acquisition from split series in 11/45 (24%) to preferred single series in 18/24 (75%) and similar increases in T1 maps completeness from 7/19 (37%) to 7/7 (100%). CONCLUSION Despite challenges in multisite workflow and substantial multi-modality site and central data management, a robust MRI QA program has confirmed 95% compliance for mRANO assessment. Site specific feedback resulted in increased compliance with advanced MRI sequences to enable detailed future analysis.

Repeatability of quantitative individual lesion and total disease multiparametric whole-body MRI measurements in prostate cancer bone metastases.

To assess the repeatability of quantitative multiparametric whole-body MRI (mpWB-MRI) parameters in advanced prostate cancer (APC) bone metastases. 1.5T MRI was performed twice on the same day in 10 APC patients. MpWB-MRI-included diffusion weighted imaging (DWI) and T1-weighted gradient-echo 2-point Dixon sequences. ADC and relative fat-fraction percentage (rFF%) maps were calculated, respectively. A radiologist delineated up to 10 target bone metastases per study. Means of ADC, b900 signal intensity(SI), normalised b900 SI, rFF% and maximum diameter (MD) for each target lesion and overall parameter averages across all targets per patient were recorded. The total disease volume (tDV in ml) was manually delineated on b900 images and mean global (g)ADC was derived. Bland-Altman analyses were performed with calculation of 95% repeatability coefficients (RC). Seventy-three individual targets (median MD 26 mm) were included. Lesion mean ADC RC was 12.5%, mean b900 SI RC 137%, normalised mean b900 SI RC 110%, rFF% RC 3.2 and target MD RC 5.5 mm (16.3%). Patient target lesion average mean ADC RC was 6.4%, b900 SI RC 104% and normalised mean b900 SI RC 39.6%. Target average rFF% RC was 1.8, average MD RC 1.3 mm (4.8%). tDV segmentation RC was 6.4% and mean gADC RC 5.3%. APC bone metastases' ADC, rFF% and maximum diameter, tDV and gADC show good repeatability. APC bone metastases' mean ADC and rFF% measurements of single lesions and global disease volumes are repeatable, supporting their potential role as quantitative biomarkers in metastatic bone disease.

Correlation between ADC Values and Split Renal Function by Renal Isotope Scan in Assessment of Nephropathy

Background Early assessment of renal structure and function is crucial for the proper management and treatment of renal disease. DW-MRI show the Brownian motion of water molecules using the ADC values yielding information on water diffusion in the extracellular and extravascular space and capillary perfusion. Aim of the Work to study the ability of DWI in assessing and evaluating the individual renal function by correlating the ADC values calculated with split GFR obtained from dynamic renal scan among cases and healthy control group. Patients and Methods This prospective study was conducted on 30 patients and 30 control healthy group. Both MR examination and dynamic renal scan were performed on 30-case group, only MR examination was done on 30-healthy control group. DWI (b = 0, 200, 800 s/mm2) was done on 1.5T MRI unit. ADC map was calculated for every kidney. Split GFRs were measured from 99-Tcm DTPA scintigraphy. Results our study revealed that ADC values of normal control group is lower than that of case group. Cut off value of ADC, that discriminate between case and control group (using average ADC value of both kidneys), was ≤2.1 x10-3 mm2/s with Sensitivity = 87% and Specificity = 47%. We classified renal dysfunction, according to the total GFR measured from the dynamic renal scan and comparing it with the standard normalized GFR ranges according to age and sex in to 3 groups : suboptimal, decompensated and deteriorated renal dysfunction, the mean ADC values were 2.07 ± 0.21, 2.01 ± 0.13 and 1.45 ± 0.19 x10-3 mm2/s respectively. Conclusion There is a positive relationship between the ADC values and split GFR. DWI has a potential value for evaluating individual renal function, and may provide functional and morphological information at the same time.

Usefulness of Diffusion Weighted MRI in Differentiating Benign from Malignant Bone Lesions

Background Bone tumors represent a wide spectrum of disease in children & adult ranging from benign lesions to an aggressive malignancy, so finding a noninvasive diagnostic tool was needed. Objective We aimed to differentiate between benign and malignant bone lesions and to limits the number of patient with benign lesion who undergo biopsy. Patients and Methods Our study was carried out on 32 patients were diagnosed with bone tumors (21 benign & 11 malignant), at the MRI unit of the Radiology Department Ain Shams University, outpatient clinics and private centers after getting approval from each ethical committee. They were assessed with conventional & diffusion MR images, DW EPI carried out using different b values including 0 s/ mm², 500 s/ mm², and 800 s/ mm². ADC map were automatically calculated. Results The mean ADC value for benign tumors was 1.6±0.4 x 10 -3 mm2/s. while malignant tumors were 1.2±0.3 x 10 -3 mm2/s. ROC analysis was used to define the best cut off mean ADC value for detecting malignancy which was <1.3, with sensitivity of 81.8% specificity of 81%. Conclusion The present study showed high specificity and sensitivity of DWI as a complementary sequence with conventional MRI and ADC value measurements in discrimination between benign and malignant bone tumors with significant cut-off value, making it a noninvasive tool for increasing the accuracy in identifying bone lesions.

AKURASI DIAGNOSTIK NILAI ADC MRI DALAM MEMPREDIKSI TUMOR JARINGAN LUNAK JINAK DAN GANAS BERDASARKAN HASIL PEMERIKSAAN HISTOPATOLOGI

ABSTRACT
 DWI with ADC mapping adds value to conventional MRI imaging by offering assessment of cellularity functionality with the potential to help differentiate between benign and malignant lesions. The aim of this study is to assess the accuracy of the ADC value MRI in predicting soft tissue tumors benign and malignant. This study was carried out by the Radiology Installation of Wahidin Sudirohusodo Hospital from February 2020 to August 2022 with a sample of 36 people and an age range of 9-74 years. The statistical method used was to calculate the value of sensitivity, specificity, Positive Predictive Value and Negative Predictive Value to assess accuracy as well as ROC and Mann Whitney tests. The results show that it consists of 36 samples with soft tissue tumors (malignant: 25 samples and benign : 11 samples). The mean value of ADC for malignant soft tissue tumors is 0.998 ± 0.526 x 10-3mm2/s and benign is 1.705 ± 0.665 x 10-3mm2/s. With a Cut Off value to distinguish malignant and benign soft tissue tumors of 1,265 x 10-3 mm2/s, with a sensitivity of 81.8%, specificity of 80%, Positive Predictive Value 64.3%, Negative Predictive Value 90.9%, Accuracy 80.5% and AUC 0.81. The accuracy of the ADC MRI value is 80.5%, indicating that the ADC value can be used as an additional modality to help predict benign and malignant soft tissue tumors.
 
 Keywords: Soft tissue tumor, MRI, DWI, ADC, Histopathology

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

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

MRI-based radiomics analysis of bladder cancer: prediction of pathological grade and histological variant

To develop magnetic resonance imaging (MRI)-based radiomics models for the prediction of the pathological grade and histological variant of bladder cancer. A total of 227 patients who underwent bladder MRI and had histopathologically confirmed grades and variants were included retrospectively from January 2017 to March 2022. They were assigned to a training set (n=131) and a testing set (n=96) based on the MRI system. MRI-based radiomics features were extracted from manually segmented volumes of interest from high-b-value DWI images and ADC maps. The radiomics models were trained with all possible pipelines in the training set. One optimal model was selected using the fivefold cross-validation method and verified by the testing set according to the pathological results. Univariate and multivariate analyses were performed to identify the significant clinical and imaging factors for developing clinical-radiomics models. The radiomics model for grade prediction had area under the curve (AUC) values of 0.784, 0.786, and 0.733 in the training, cross-validation, and testing sets, respectively. The radiomics model for variant prediction had AUC values of 0.748, 0.757, and 0.789 in the training, cross-validation, and testing sets, respectively. The performance of the clinical-radiomics model was significantly improved compared with the radiomics models alone for the total dataset (AUC for grade: 0.846 versus 0.756; AUC for variant: 0.810 versus 0.757, p<0.05). MRI-based radiomics models could be used to predict the pathological grade and histological variants of bladder cancer with relatively good performance.

A temperature-controlled cooling system for accurate quantitative post-mortem MRI.

To develop a temperature-controlled cooling system to facilitate accurate quantitative post-mortem MRI and enable scanning of unfixed tissue. A water cooling system was built and integrated with a 7T scanner to minimize temperature drift during MRI scans. The system was optimized for operational convenience and rapid deployment to ensure efficient workflow, which is critical for scanning unfixed post-mortem samples. The performance of the system was evaluated using a 7-h diffusion MRI protocol at 7T with a porcine tissue sample. Quantitative T1 , T2 , and ADC maps were interspersed with the diffusion scans at seven different time points to investigate the temperature dependence of MRI tissue parameters. The impact of temperature changes on biophysical model fitting of diffusion MRI data was investigated using simulation. Tissue T1 , T2 , and ADC values remained stable throughout the diffusion MRI scan using the developed cooling system, but varied substantially using a conventional scan setup without temperature control. The cooling system enabled accurate estimation of biophysical model parameters by stabilizing the tissue temperature throughout the diffusion scan, while the conventional setup showed evidence of significantly biased estimation. A temperature-controlled cooling system was developed to tackle the challenge of heating in post-mortem imaging, which shows potential to improve the accuracy and reliability of quantitative post-mortem imaging and enables long scans of unfixed tissue.

Low-parameter supervised learning models can discriminate pseudoprogression and true progression in non-perfusion-based MRI.

Discrimination of pseudoprogression and true progression is one challenge to the treatment of malignant gliomas. Although some techniques such as circulating tumor DNA (ctDNA) and perfusion-weighted imaging (PWI) demonstrate promise in distinguishing PsP from TP, we investigate robust and replicable alternatives to distinguish the two entities based on more widely-available media. In this study, we use low-parametric supervised learning techniques based on geographically-weighted regression (GWR) to investigate the utility of both conventional MRI sequences as well as a diffusion-weighted sequence (apparent diffusion coefficient or ADC) in the discrimination of PsP v TP. GWR applied to MRI modality pairs is a unique approach for small sample sizes and is a novel approach in this arena. From our analysis, all modality pairs involving ADC maps, and those involving post-contrast T1 regressed onto T2 showed potential promise. This work on ADC data adds to a growing body of research suggesting the predictive benefits of ADC, and suggests further research on the relationships between post-contrast T1 and T2.Clinical relevance- Few studies have investigated predictive potential of conventional MRI and ADC to detect PsP. Our study adds to the growing research on the topic and presents a new perspective to research by exploiting the utility of ADC in PsP v TP distinction. In addition, our GWR methodology for low-parametric supervised computer vision models demonstrates a unique approach for image processing of small sample sizes.

Enhanced Image Processing Using Complex Averaging in Diffusion-Weighted Imaging of the Prostate: The Impact on Image Quality and Lesion Detectability.

Diffusion-weighted images of the prostate can suffer from a "hazy" background in low signal-intensity areas. We hypothesize that enhanced image processing (EIP) using complex averaging reduces artifacts, noise, and distortion in conventionally acquired diffusion-weighted images and synthesized high b-value images, thus leading to higher image quality and better detection of potentially malignant lesions. Conventional DWI trace images with a b-value of 1000 s/mm2 (b1000), calculated images with a b-value of 2000 s/mm2 (cb2000), and ADC maps of 3T multiparametric prostate MRIs in 53 patients (age 68.8 ± 10 years) were retrospectively evaluated. Standard images were compared to images using EIP. In the standard images, 36 lesions were detected in the peripheral zone and 20 in the transition zone. In 13 patients, EIP led to the detection of 8 additional lesions and the upgrading of 6 lesions; 6 of these patients were diagnosed with prostate carcinoma Gleason 7 or 8. EIP improved qualitative ratings for overall image quality and lesion detectability. Artifacts were significantly reduced in the cb2000 images. Quantitative measurements for lesion detectability expressed as an SI ratio were significantly improved. EIP using complex averaging led to image quality improvements in acquired and synthesized DWI, potentially resulting in elevated diagnostic accuracy and management changes.

Magnetic resonance imaging in naso-oropharyngeal carcinoma: role of texture analysis in the assessment of response to radiochemotherapy, a preliminary study

ObjectiveIdentifying MRI texture parameters able to distinguish inflammation, fibrosis, and residual cancer in patients with naso-oropharynx carcinoma after radiochemotherapy (RT-CHT).Material and methodsIn this single-centre, observational, retrospective study, texture analysis was performed on ADC maps and post-gadolinium T1 images of patients with histological diagnosis of naso-oropharyngeal carcinoma treated with RT-CHT. An initial cohort of 99 patients was selected; 57 of them were later excluded. The final cohort of 42 patients was divided into 3 groups (inflammation, fibrosis, and residual cancer) according to MRI, 18F-FDG-PET/CT performed 3–4 months after RT-CHT, and biopsy. Pre-RT-CHT lesions and the corresponding anatomic area post-RT-CHT were segmented with 3D slicer software from which 107 textural features were derived. T-Student and Wilcoxon signed-rank tests were performed, and features with p-value < 0.01 were considered statistically significant. Cut-off values—obtained by ROC curves—to discriminate post-RT-CHT non-tumoural changes from residual cancer were calculated for the parameters statistically associated to the diseased status at follow-up.ResultsTwo features—Energy and Grey Level Non-Uniformity—were statistically significant on T1 images in the comparison between ‘positive’ (residual cancer) and ‘negative’ patients (inflammation and fibrosis). Energy was also found to be statistically significant in both patients with fibrosis and residual cancer. Grey Level Non-Uniformity was significant in the differentiation between residual cancer and inflammation. Five features were statistically significant on ADC maps in the differentiation between ‘positive’ and ‘negative’ patients. The reduction in values of such features between pre- and post-RT-CHT was correlated with a good response to therapy.ConclusionsTexture analysis on post-gadolinium T1 images and ADC maps can differentiate residual cancer from fibrosis and inflammation in early follow-up of naso-oropharyngeal carcinoma treated with RT-CHT.

Deep learning for assessing image quality in bi-parametric prostate MRI: A feasibility study

BackgroundAlthough systems such as Prostate Imaging Quality (PI-QUAL) have been proposed for quality assessment, visual evaluations by human readers remain somewhat inconsistent, particularly among less-experienced readers. ObjectivesTo assess the feasibility of deep learning (DL) for the automated assessment of image quality in bi-parametric MRI scans and compare its performance to that of less-experienced readers. MethodsWe used bi-parametric prostate MRI scans from the PI-CAI dataset in this study. A 3-point Likert scale, consisting of poor, moderate, and excellent, was utilized for assessing image quality. Three expert readers established the ground-truth labels for the development (500) and testing sets (100). We trained a 3D DL model on the development set using probabilistic prostate masks and an ordinal loss function. Four less-experienced readers scored the testing set for performance comparison. ResultsThe kappa scores between the DL model and the expert consensus for T2W images and ADC maps were 0.42 and 0.61, representing moderate and good levels of agreement. The kappa scores between the less-experienced readers and the expert consensus for T2W images and ADC maps ranged from 0.39 to 0.56 (fair to moderate) and from 0.39 to 0.62 (fair to good). ConclusionsDeep learning (DL) can offer performance comparable to that of less-experienced readers when assessing image quality in bi-parametric prostate MRI, making it a viable option for an automated quality assessment tool. We suggest that DL models trained on more representative datasets, annotated by a larger group of experts, could yield reliable image quality assessment and potentially substitute or assist visual evaluations by human readers.

MRI as a Diagnostic Tool for the Determination of Acute Ischaemic Stroke: A Study in a Tertiary Care Hospital

Background: Stroke is a prevalent and potentially fatal medical condition that affects individuals worldwide. Ischemic strokes, caused by arterial blockages, are the most common type, accounting for about 80% of all cases. Hemorrhagic strokes, on the other hand, are less frequent but can have more severe consequences. Accurate and timely diagnosis of stroke is critical for effective treatment and optimal patient outcomes. In this context, diffusion-weighted imaging (DWI) has emerged as a valuable tool for identifying and monitoring ischemic stroke. This article provides an overview of the role of DWI in stroke assessment and management, with a focus on early detection and intervention. The aim of this study is to investigate the reliability of diffusion-weighted MRI (DWI) as an imaging modality in the evaluation of acute ischemic stroke. Material &amp; Methods: This prospective cross-sectional study was conducted at the Department of Radiology and Imaging at the Combined Military Hospital (CMH) in Dhaka, Bangladesh, between June 2020 and June 2021. The study aimed to examine 120 patients clinically diagnosed with acute ischemic stroke. Prior to the study, verbal consent was obtained from all patients. The Study subjects were identified in the emergency and casualty department and had undergone an MRI of the brain in the Department of Radiology and Imaging at CMH, Dhaka. . Data were collected on pre-designed forms, and the relevant information was compiled on a master chart for statistical analysis using SPSS software. Descriptive analysis and frequency of results were presented in the form of tables, pie charts, and bar graphs. The ethical clearance of this study was obtained from the Institutional Ethics Committee of CMH, Dhaka, Bangladesh. Results: The study included 64 males and 56 females with a mean age of 65.2 ± 7.83 years. The majority of patients had an acute ischemic lesion (88.30%) in the MCA (50.0%) with small-sized lesions (41.7%) and low ADC values (90.0%). The most common clinical presentation was hemiplegia (100%), and diffusion-weighted MRI had a high diagnostic accuracy in detecting acute ischaemic lesions (96.3%). Overall, these findings highlight the importance of MRI in the evaluation of stroke patients and can guide clinical decision-making. Conclusion: The present study concluded that DWI in conjunction with ADC map MR imaging is a gold standard diagnostic modality in the evaluation and management of acute ischaemic stroke.

Dynamic contrast-enhanced MRI and Apparent diffusion coefficient mapping in the characterization of Palpable breast lesions: A prospective observational study

BackgroundBreast MRI is the imaging modality of choice in patients with palpable breast lesions unequivocal on mammography and ultrasonography. This study aims to evaluate the role of dynamic contrast-enhanced MRI (DCE-MRI) and apparent diffusion coefficient mapping in the characterization and differentiation of various palpable breast lesions. This prospective observational study was conducted in a tertiary care hospital between July 2019 and June 2021. Sixty-six patients with palpable breast lesions were undergone MRI scans of the breasts. The various palpable breast lesions were categorized according to the 5th edition BI-RADS lexicon. The sensitivity of ADC mapping and DCE-MRI was determined for differentiation of various palpable breast lesions according to the BI-RADS category and gold standard histopathological findings.ResultsOf 66 patients with palpable breast lesions, 36 patients (54.5%) were benign and 30 patients (45.5%) were malignant lesions. Malignant palpable breast lesions had a mean ADC value of 0.939 ± 0.166[SD] × 10−3 mm2/s, and benign lesions had 1.891 ± 0.524[SD] × 10−3 mm2/s where unpaired Student t-test showed statistically significant difference of P value 0.0005. BI-RADS 2 lesions had a mean ADC value of 2.056 ± 0.471[SD] × 10−3 mm2/s, BI-RADS 3 had 1.314 ± 0.151[SD] × 10−3 mm2/s, BI-RADS 4 had 0.935 ± 0.119[SD] × 10−3 mm2/s, and BI-RADS 5 had 0.930 ± 0.943[SD] × 10−3 mm2/s. BI-RADS 2 category showed optimal cutoff mean ADC of 1.508 × 10−3 mm2/s with a sensitivity of 85.7% and specificity of 94.7%, BI-RADS 3 lesions had 1.208 × 10−3 mm2/s with a sensitivity of 75% and specificity of 55.2%, BI-RADS 4 lesions had 1.064 × 10−3 mm2/s with a sensitivity 80% and specificity of 67.9%, and BI-RADS 5 lesions had 1.013 × 10−3 mm2/s with a sensitivity of 80% and specificity of 82.6%.ConclusionsBreast MRI is superior to the other imaging modalities for the characterization and differentiation of various palpable breast lesions. The combined use of ADC mapping and DCE-MRI had more sensitivity than conventional MRI, ADC mapping or DCE-MRI alone.

Radiomics in colorectal cancer patients.

The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.

Detecting Double Expression Status in Primary Central Nervous System Lymphoma Using Multiparametric MRI Based Machine Learning.

Double expression lymphoma (DEL) is a subtype of primary central nervous system lymphoma (PCNSL) that often has a poor prognosis. Currently, there are limited noninvasive ways to detect protein expression. To detect DEL in PCNSL using multiparametric MRI-based machine learning. Retrospective. Forty PCNSL patients were enrolled in the study among whom 17 were DEL (9 males and 8 females, 61.29 ± 14.14 years) and 23 were non-DEL (14 males and 9 females, 55.57 ± 14.16 years) with 59 lesions (28 DEL and 31 non-DEL). ADC map derived from DWI (b = 0/1000 s/mm2 ), fast spin echo T2WI, T2FLAIR, and contrast-enhanced T1 weighted imaging (T1CE) were collected at 3.0 T. Two raters manually segmented lesions by ITK-SNAP on ADC, T2WI, T2FLAIR and T1CE. A total of 2234 radiomics features from the tumor segmentation area were extracted. The t-test was conducted to filter the features, and elastic net regression algorithm combined with recursive feature elimination was used to calculate the essential features. Finally, 12 groups with combinations of different sequences were fitted to 6 classifiers, and the optimal models were selected. Continuous variables were assessed by the t-test, while categorical variables were assessed by the non-parametric test. Interclass correlation coefficient tested variables' consistency. Sensitivity, specificity, accuracy F1-score, and area under the curve (AUC) were used to evaluate model performance. DEL status could be identified to varying degrees with 72 models based on radiomics, and model performance could be improved by combining different sequences and classifiers. Both SVMlinear and logistic regression (LR) combined with four sequence group had similar largest AUCmean (0.92 ± 0.09 vs. 0.92 ± 0.05), and SVMlinear was considered as the optimal model in this study since the F1-score of SVMlinear (0.88) was higher than that of LR (0.83). Multiparametric MRI-based machine learning is promising in DEL detection. 4 TECHNICAL EFFICACY STAGE: 2.

Cerebral Malaria and Cytotoxic Lesions of the Corpus Callosum.

To the Editors: Malaria is a common cause of morbidity and mortality worldwide. According to the WHO, 229 million cases and 409,000 deaths were reported worldwide in 2020.1 Children account for a high burden of malaria-related mortality and are at a higher risk of developing cerebral malaria (CM).1 A 12-year-old, previously healthy female from Guinea-Bissau was admitted 1 day after arriving in Portugal with a 2-day history of headache, generalized myalgia, and high-grade fever. Physical examination was unremarkable, except for pallor, icteric sclerae, and nontender hepatomegaly (4 cm). The patient was hemodynamically stable (BP, 101/52 mmHg; HR, 111 bpm), but shortly after admission, vomiting, lethargy, and impaired consciousness were observed (Glasgow coma score 11). She presented with anemia (hemoglobin, 10.0 g/dL; normal reference [NR]: 12.0–16.0), thrombocytopenia (78 × 109/L; NR: 150–450) and significant elevation of inflammatory markers (C-reactive protein, 301.0 mg/L; NR, <5; and procalcitonin, >100 ng/mL; NR, <0.1). No electrolyte disturbances, hypoglycemia, or acidosis. Plasmodium falciparum blood smear was positive, and severe malaria was diagnosed due to high parasitemia (28.5%/erythrocytes–11.872.000 parasites/µL) and central nervous system (CNS) involvement other infections were excluded. The cerebrospinal fluid revealed pleocytosis (15 cells), generalized slowing on the electroencephalogram, and a lesion in the splenium of the corpus callosum with T2 hypersignal and restricted diffusion (Fig. 1), compatible with a cytotoxic lesion of the corpus callosum (CLOCCs) on brain magnetic resonance imaging (MRI). CM was diagnosed based on severely impaired consciousness, CLOCCs, and no other identifiable causes of coma.FIGURE 1.: MRI axial images T2 weighted (A), DWI (B) and ADC map (C): oval lesion in the splenium of the corpus callosum (arrow) with T2 hypersignal and diffusion restriction with DWI hypersignal and ADC map hyposignal, corresponding to cytotoxic edema.The patient was treated with parenteral quinine and clindamycin, due to the immediate unavailability of artesunate, plus ceftriaxone. She became afebrile with progressive improvement in neurological status after 48 h and complete parasite clearance on day 3. Artemether-lumefantrine was initiated on day 5, and the patient was discharged on day 8. CM is defined as a disturbance of consciousness in a patient with Plasmodium parasitemia and no other identifiable cause.2 Despite the reported low mortality rate of pediatric imported malaria in Europe (<1%), CM entails a risk of life-threatening acute CNS effects with diffuse encephalopathy and often rapidly progressive coma, as observed in our patient.3 Distinctive brain MRI features involving both white and gray matter structures have been reported in a large cohort of Malawian children with CM. Increased volume, T2 hyperintensity, and Diffusion-weighted imaging (DWI) abnormalities affecting the cortical area (61.7%), subcortical and periventricular white matter (71.7%), basal ganglia (84.2%) and the corpus callosum (49.2%) were the most frequent features.4 CLOCCS was a common MRI finding in CM, as suggested by DWI/Apparent diffusion coefficient mismatch with DWI increased signal and Apparent diffusion coefficient low signal,4 as observed in our patient (Fig. 1). The corpus callosum, mostly the splenium, is a region vulnerable to inflammatory cytokinopathy, driven by malaria, seizures or other causes.5 The higher density of cytokines and glutamate receptors in this area leads to high extracellular glutamate levels, inducing cytotoxic edema upon infection, which is frequently reversible.5 CM in children is the most severe complication of P. falciparum infection and needs a high suspicion index in nonendemic countries. Altered MRI and CLOCCs, although unspecific, are associated with CM.

Prediction of prognostic factors in breast cancer: A noninvasive method utilizing histogram parameters derived from Adc maps

ObjectiveThe aim of this study is to investigate the relationship between histogram parameters and prognostic factors of breast cancer and to reveal the diagnostic performance of histogram parameters in predicting prognostic factors status. Materials and methodsNinety-two patients with a confirmed histopathological diagnosis of breast cancer were included in the study. Magnetic resonance imaging (MRI) was performed using a 1.5T scanner and two different b values were used for diffusion-weighted imaging (DWI) (b values: 0 s/mm2, b: 800 s/mm2). For 3D histogram analysis, regions of interest (ROI) were drawn each slice of the lesion on apparent diffusion coefficient (ADC) maps. The following data were derived from the histogram analysis data: percentiles, skewness, kurtosis, and entropy. The relationship between prognostic factors and histogram analysis data was investigated using the Kolmogorov−Smirnov test, Shapiro−Wilk test, skewness-kurtosis test, independent t-test, Mann−Whitney U test, and Kruskal−Wallis test. Receiver operator characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the histogram parameters. ResultsADCmax, kurtosis, and entropy parameters were statistically significantly correlated with tumor diameter (p = 0.002, p = 0.008, and p = 0.001, respectively). There was a significant difference in ADC90% and ADCmax values, depending on estrogen receptor (ER) and progesterone receptor (PR) status. These values were lower in ER- and PR-positive than ER- and PR-negative patients (p = 0.02 and p = 0.001 vs. p = 0.018, p = 0.008). All ADC percentage values were lower in patients with a positive Ki-67 proliferation index as compared with those with a negative Ki-67 proliferation index (all p = 0.001). The entropy value was high in high-grade lesions and lesions with axillary involvement (p = 0.039 and p = 0.048, respectively). The highest area under the curve (AUC) for ER and PR status was calculated for the ADC90% value with ROC curve analysis. The highest AUC for Ki-67 proliferation index was found for the ADC50%. ConclusionHistogram analysis parameters derived from of ADC maps of whole lesions can reflect histopathological features of the tumors. Based on our study, it was concluded that histogram analysis parameters were related to the prognostic factors of the tumor.