Nomogram for Predicting Microvascular Invasion in Hepatocellular Carcinoma Using Gadoxetic Acid-Enhanced MRI and Intravoxel Incoherent Motion Imaging

RATIONALE AND OBJECTIVES

Microvascular invasion (MVI) is an important risk factor in hepatocellular carcinoma (HCC), but it can only be determined through histopathological results. The aim of this study was to develop and validate a nomogram for preoperative prediction MVI in HCC using gadoxetic acid-enhanced magnetic resonance imaging (MRI) and intravoxel incoherent motion imaging (IVIM).

MATERIALS AND METHODS

From July 2017 to September 2022, 148 patients with surgically resected HCC who underwent preoperative gadoxetic acid-enhanced MRI and IVIM were included in this retrospective study. Clinical indicators, imaging features, and diffusion parameters were compared between the MVI-positive and MVI-negative groups using the chi-square test, Mann-Whitney U test, and independent sample t test. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance in predicting MVI. Univariate and multivariate analyses were conducted to identify the significant clinical-radiological variables associated with MVI. Subsequently, a predictive nomogram that integrates clinical-radiological risk factors and diffusion parameters was developed and validated.

RESULTS

Serum alpha-fetoprotein level, tumor size, nonsmooth tumor margin, peritumoral hypo-intensity on hepatobiliary phase (HBP), apparent diffusion coefficient value and D value were statistically significant different between MVI-positive group and MVI-negative group. The results of multivariate analysis identified tumor size (odds ratio [OR], 0.786; 95% confidence interval [CI], 0.675-0.915; P < .01), nonsmooth tumor margin (OR, 2.299; 95% CI, 1.005-5.257; P < .05), peritumoral hypo-intensity on HBP (OR, 2.786; 95% CI, 1.141-6.802; P < .05) and D (OR, 0.293; 95% CI,0.089-0.964; P < .05) was the independent risk factor for the status of MVI. In ROC analysis, the combination of peritumoral hypo-intensity on HBP and D demonstrated the highest area under the curve value (0.902) in prediction MVI status, with sensitivity 92.8% and specificity 87.7%. The nomogram exhibited excellent predictive performance with C-index of 0.936 (95% CI 0.895-0.976) in the patient cohort, and had well-fitted calibration curve.

CONCLUSION

The nomogram incorporating clinical-radiological risk factors and diffusion parameters achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.

Role of multiparametric MRI in predicting muscle invasiveness in urinary bladder neoplasms with pathological correlations

Urinary bladder cancer (BC) is one of the most frequent malignancies and the ninth most common malignancy worldwide. The objective of this study is to assess the role of multiparametric magnetic resonance imaging (mp-MRI) in predicting the invasiveness of urinary bladder space occupying lesions. Thirty-five patients diagnosed with bladder masses underwent an mp-MRI study. The results of three image sets were analysed and compared with the histopathological results as a reference standard: T2-weighted image (T2WI) plus dynamic contrast-enhanced (DCE), T2WI plus diffusion-weighted images (DWI), and mp-MRI, including T2WI plus DWI and DCE. The diagnostic accuracy of mp-MRI was evaluated using receiver operating characteristic curve analysis. We discovered a highly significant correlation between muscle invasiveness as staged by HPE (Histopathological examination) and mp-MRI utilising a VI-RADS score >3 (p 0.001) with a sensitivity of 100% and a specificity of 85.7%. With a diagnostic accuracy of 77.14%, a sensitivity of 92.31%, a specificity of 72.72%, a positive predictive value of 66.67%, and a negative predictive value of 94.11%, In terms of muscle invasiveness, there is good concordance between HPE staging and mp-MRI utilising the VI-RADS score. The mean apparent diffusion coefficient (ADC) values were higher in low grades than in high grades. The ROC curve study revealed a very strong correlation between HPE grade and ADC (p = 0.045). In 77.14% of patients, Mp-MRI correctly identified the local T stage. Mp-MRI is imaging biomarker for invasiveness and grade of tumour. The tumours with high grade are more invasive. However, the diagnostic accuracy of mp-MRI in determining muscle invasiveness is not very high and it overstages the disease in some cases (33.3%). Its clinical usefulness in determining muscle invasiveness before TURBT and histopathological examination can be questioned.

Diffusion weighted imaging with reverse encoding distortion correction: Improvement of image quality and distortion for accurate ADC evaluation in in vitro and in vivo studies

PURPOSE

The purpose of this in vivo study was to determine the effect of reverse encoding direction (RDC) on apparent diffusion coefficient (ADC) measurements and its efficacy for improving image quality and diagnostic performance for differentiating malignant from benign tumors on head and neck diffusion-weighted imaging (DWI).

METHODS

Forty-eight patients with head and neck tumors underwent DWI with and without RDC and pathological examinations. Their tumors were then divided into two groups: malignant (n = 21) and benign (n = 27). To determine the utility of RDC for DWI, the difference in the deformation ratio (DR) between DWI and T2-weighted images of each tumor was determined for each tumor area. To compare ADC measurement accuracy of DWIs with and without RDC for each patient, ADC values for tumors and spinal cord were determined by using ROI measurements. To compare DR and ADC between two methods, Student's t-tests were performed. Then, ADC values were compared between malignant and benign tumors by Student's t-test on each DWI. Finally, sensitivity, specificity and accuracy were compared by means of McNemar's test.

RESULTS

DR of DWI with RDC was significantly smaller than that without RDC (p < 0.0001). There were significant differences in ADC between malignant and benign lesions on each DWI (p < 0.05). However, there were no significant difference of diagnostic accuracy between the two DWIs (p > 0.05).

CONCLUSION

RDC can improve image quality and distortion of DWI and may have potential for more accurate ADC evaluation and differentiation of malignant from benign head and neck tumors.

The magnetic resonance imaging and age-adjusted matrix metalloproteinase-7 assist the diagnosis of biliary atresia

BACKGROUND

We investigated the utilities of the liver-to-psoas apparent diffusion coefficient ratios (LTPAR) yielded by diffusion-weighted magnetic resonance imaging (DWMRI) and the age-adjusted serum matrix metalloproteinase-7 (MMP-7) for the diagnosis of biliary atresia (BA) in cholestatic infants.

METHODS

In total, 170 cholestatic infants were recruited, of whom 50 (29.41%) were diagnosed with BA after cholestatic workups. The LTPAR and MMP7 levels were assessed.

RESULTS

The LTPAR was significantly lower in BA infants, and the age-adjusted MMP7 ratio was significantly higher, compared to other cholestatic infants (both p < 0.001). Receiver operating characteristic curve analysis yielded a cutoff > 0.1 ng/mL.day for the age-adjusted MMP-7 ratio, and an LTPAR < 1.01 for the optimal prediction of BA (both p < 0.001). Univariate logistic regression analysis revealed that both an age-adjusted MMP-7 ratio > 0.1 ng/mL.day and an LTPAR < 1.01 were significant predictors of BA among cholestatic infants (odds ratio = 30.98 and 13.28; p < 0.001 and < 0.001, respectively). The significance of the age-adjusted MMP-7 ratio and the LTPAR persisted on multivariate logistic regression analysis after adjusting for sex and the serum gamma-glutamyl transferase level (p < 0.001 and < 0.001, respectively). The negative predictive values (NPVs) for BA were 91.49% and 94.17%, respectively, for the LTPAR and age-adjusted MMP-7 ratio.

CONCLUSION

The age-adjusted MMP-7 ratio and the LTPAR are both significant non-invasive predictors of BA. The consideration of both serum and imaging parameters may enhance BA diagnostic performance in cholestatic infants.

Paediatric supratentorial tumours do not cause microstructural alterations in contralateral white matter: a preliminary study

BACKGROUND AND PURPOSE

Intracranial tumours in children can exhibit different characteristics compared to those in adults. Understanding the microstructural changes in the contralateral normal-appearing white matter (NAWM) in children with primary intracranial masses is essential for optimizing treatment strategies. This study aimed to investigate the apparent diffusion coefficient (ADC) changes in contralateral NAWM using fully automated methods and deep learning algorithms.

METHODS

We included 22 paediatric patients with primary supratentorial intracranial masses (23% high-grade) in the study. ADC values of the contralateral NAWM in the patient group were compared to those of a control group. Deep learning algorithms were utilized to analyse diffusion changes in NAWM.

RESULTS

The mean ADC values of contralateral NAWM in the patient group were 0.80 ± 0.03 × 10-3 mm2/s, while the control group had a mean ADC value of 0.81 ± 0.03 × 10-3 mm2/s. There was no statistically significant difference between the groups (p = 0.39). Our findings indicate that there are no significant diffusion changes in the contralateral white matter of children with supratentorial intracranial masses.

CONCLUSION

Primary supratentorial intracranial masses in children do not cause microstructural changes in contralateral normal-appearing white matter. This could be attributed to the less infiltrative nature and different biochemical profile of these tumour groups in the paediatric population. Further studies using advanced imaging techniques could provide additional insights into the distinct characteristics of paediatric intracranial tumours and improve patient management.

The Relationship between Breast Cancer Subtypes, Prognostic Factors, and Apparent Diffusion Coefficient Histogram Analysis

BACKGROUND

Diffusion Magnetic Resonance Imaging (MRI) is a useful method to evaluate tumor biology and tumor microstructure. The apparent diffusion coefficient (ADC) value correlates negatively with the cellular density of the tumor.

OBJECTIVE

This study aimed to investigate the effectiveness of the ADC histogram analysis in showing the relationship between breast cancer prognostic factors and ADC parameters.

METHODS

This study is a retrospective observational descriptive study. ADC histogram parameters were evaluated in all tumor volumes of 67 breast cancer patients. Minimum, 5, 10, 25, 50, 75, 90, 95 percentiles, maximum, mean, median ADC values, kurtosis, and skewness were calculated. Breast MRI examinations were performed on a 3T MR scanner. We evaluated the fibroglandular tissue density of bilateral breasts, background enhancement, localization of masses, multifocality-multicentricity, shape, rim, internal contrast enhancement, and kinetic curve on breast MRI. BIRADS scoring was performed according to breast MRI. Pathologically, histologic type, histologic grade, HER 2, Ki 67, ER-, and PR status were evaluated.

RESULTS

A significant correlation was found between tumor volume and ADC scores. There is a significant correlation between min ADC values (p< 0.031), max ADC (p< 0.001), and skewness (p< 0.019). A significant correlation was found between tumor kurtosis and lymph nodes (p< 0.029). There was a significant difference in ADC values depending on ER-and PRstatus. (for ER p = 0.004, p = 0.018, p = 0.010, p = 0.008, p = 0.004, p = 0.004, p = 0.02, p = 0.02 and p = 0.038, for PR p < 0.001, p = 0.028, p = 0.011, p = 0.001, p < 0.001, p =<0.001, p < 0.001, and p < 0.001, respectively; p < 0.05). These values were lower in ER-and PR-positive status than in ER-and PR-negative receptor status. According to HER2 status, there was a statistically significant difference in ADC

CONCLUSION

Our study found a significant difference between tumor volume, ER- and, PR status, HER2, and lymph node involvement, and some ADC values among prognostic factors for breast cancer. Furthermore, ADC histogram analysis can provide additional value in predicting some prognostic factors.

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Key Words

• ADC histogram
• ADC values.
• Apparent diffusion coefficient
• Breast cancer
• Histogram analysis
• Magnetic resonance imaging

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MESH Headings

• Humans
• Breast Neoplasms/diagnostic imaging
• Female
• Diffusion Magnetic Resonance Imaging/methods
• Retrospective Studies
• Middle Aged
• Prognosis
• Adult
• Aged
• Tumor Burden
• Breast/diagnostic imaging
• Breast/pathology

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Two resected cases of benign adenomyoepithelioma

BACKGROUND

Adenomyoepithelioma (AME) of the breast is an uncommon tumor characterized by the proliferation of ductal epithelial and myoepithelial cells with the heterogeneity. Although benign AME is relatively easy to differentiate from breast cancer by core needle biopsy (CNB) alone, a definitive diagnosis is often difficult. The imaging findings of AME are also variable, and there are particularly few reports about radiological features, including contrast-enhanced magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) values in AME.

CASE PRESENTATION

We present two cases of benign AME. Case 1 is a 30-year-old woman with a history of asthma. The cystic tumor shows smooth borders, and the intracystic solid component is irregular in shape and high vascularity. The pathological findings of the tumor were benign on CNB. The MRI scan showed a decreased ADC value. Case 2 is a 60-year-old woman with only a history of arrhythmia. The tumor shows a lobulated mass with cystic space and coarse calcifications. The pathological findings of the tumor were found to be benign by CNB. Dynamic MRI scan showed a fast washout pattern with a decreased ADC value. Both patients underwent excisional biopsy to confirm the diagnosis, and the pathological diagnosis was benign AME in both cases.

CONCLUSIONS

The AME of the breast has little specific imaging information, so it can be difficult to diagnose based on pathological findings of biopsy specimen. In our case, the ADC values were exceptionally low, contrary to previous reports. It is essential to carefully diagnose AME, considering the discrepancies in imaging findings observed in this case.

Clinical Value of a Nomogram Model Based on Apparent Diffusion Coefficient Values Within 1 cm of the Tumor Cavity to Predict Postoperative Progression of Glioma

OBJECTIVE

To explore the clinical value of constructing a nomogram model based on apparent diffusion coefficient values within 1 cm of the residual tumor cavity to predict the postoperative progression of gliomas.

METHODS

Clinical data of patients with glioma who underwent surgery were retrospectively retrieved from the First Hospital of Qinhuangdao. The mean apparent diffusion coefficient (mADC) was measured using a picture archiving and communication system. The Kaplan-Meier survival curve was constructed with the optimal mADC threshold determined by the X-tile. A nomogram was developed based on the independent risk factors determined using the Cox proportional hazards model (Cox regression model) to predict the progression of postoperative glioma. A receiver operating characteristic curve was drawn to evaluate the prediction accuracy of the model, and decision curve analysis was performed to assess the clinical value of the nomogram.

RESULTS

There was good agreement between the mADC values of the 2 repeated measurements before and after, with a consistency correlation coefficient of 0.83. Multivariate Cox regression analysis showed that peritumoral mADC values, degree of peritumoral enhancement, age, pathological grading, and degree of tumor resection were independent risk factors for predicting postoperative progression of glioma (all P < 0.05). The receiver operating characteristic curves of the nomogram predicting 1, 2, and 3 years postoperative progression were 0.86, 0.82, and 0.91, respectively. The calibration curve showed good consistency between the observed and predicted values in the model. The curve showed that the nomogram model has a good clinical application value.

CONCLUSIONS

The peritumoral mADC values, degree of peritumoral enhancement, age, pathological grade, and degree of tumor resection were independent factors affecting the postoperative progression of glioma. The nomogram model established for the first time based on mADC values within 1 cm of the tumor can predict the postoperative condition of patients with glioma intuitively and comprehensively. It can provide a relatively accurate prediction tool for neurosurgeons to individualize the evaluation of survival and prognosis, and formulate treatment plans for patients.

Apparent diffusion coefficient histogram analysis for differentiating fibroblastic meningiomas from non-fibroblastic WHO grade 1 meningiomas

PURPOSE

To investigate the role of apparent diffusion coefficient (ADC) histogram analysis in differentiating fibroblastic meningiomas (FM) from non-fibroblastic WHO grade 1 meningiomas (nFM).

METHODS

This retrospective study analyzed the histopathological and diagnostic imaging data of 220 patients with histopathologically confirmed FM and nFM. The whole tumors were delineated on axial ADC images, and histogram parameters (mean, variance, skewness, kurtosis, as well as the 1st, 10th, 50th, 90th, and 99th percentile ADC [ADCp1, ADCp10, ADCp50, ADCp90, and ADCp99, respectively]) were obtained. Multivariate logistic regression analysis was used to identify the most valuable variables for discriminating FM from nFM WHO grade 1 meningiomas, and their diagnostic efficacy in differentiating FM from nFM before surgery was assessed using receiver operating characteristic (ROC) curves.

RESULTS

The mean, variance, ADCp50, ADCp90, and ADCp99 of the FM group were all lower than those of the nFM group (P < 0.05), there was significant difference in location and sex (P < 0.05). Multivariate logistic regression showed ADCp99 (P < 0.001) and location (P = 0.007) were the most valuable parameters in the discrimination of FM and nFM WHO grade 1 meningiomas. The diagnostic efficacy was achieved an AUC of 0.817(95% CI, 0.759-0.866), the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 66.4%, 83.6%, 75.0%, 80.2%, and 71.3%, respectively.

CONCLUSION

ADC histogram analysis is helpful in noninvasive differentiation of FM and nFM WHO grade 1 meningiomas, and combined ADCp99 and location have the best diagnostic efficacy.

The Added Value of ADC-based Nomogram in Assessing the Depth of Myometrial Invasion of Endometrial Endometrioid Adenocarcinoma

RATIONALE AND OBJECTIVES

To explore the potential value of the apparent diffusion coefficient (ADC)-based nomogram models in preoperatively assessing the depth of myometrial invasion of endometrial endometrioid adenocarcinoma (EEA).

MATERIALS AND METHODS

Preoperative magnetic resonance imaging (MRI) of 210 EEA patients were retrospectively analyzed. ADC histogram metrics derive from the whole-tumor regions of interest. Univariate and multivariate analyses were used to screen the ADC histogram metrics and clinical characteristics for nomogram model building. The diagnostic sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of two radiologists without and with the assistance of models were calculated and compared.

RESULTS

Two nomogram models were developed for predicting no myometrial invasion (NMI) and deep myometrial invasion (DMI) with area under the curves of 0.85 and 0.82, respectively. With the assistance of models, the overall accuracies were significantly improved [radiologist_1, 73.3% vs 86.2% (p = 0.001); radiologist_2, 80.0% vs 91.0% (p = 0.002)]. In determining NMI, the sensitivity and PPV were greatly improved but not significant for radiologist_1 (51.9% vs 77.8% and 46.7% vs 75.0%, p = 0.229 and 0.511), and under/near the significance level for radiologist_2 (59.3% vs 88.9% and 57.1% vs 82.8%, p = 0.041 and 0.065), while the specificity, accuracy, and NPV were significantly improved (all p < 0.001). In determining DMI, all sensitivity, specificity, accuracy, PPV, and NPV were significantly improved (all p < 0.001).

CONCLUSION

The ADC-based nomogram models can improve the diagnostic performance of radiologist in preoperatively assessing the depth of myometrial invasion and facilitate optimizing clinical individualized treatment decisions.

A Study of Diffusion Tensor Imaging in Central Post-Stroke Pain: Traveling Beyond the Pain Pathways

Introduction

Central post-stroke pain (CPSP), seen in the aftermath of a stroke, is an underdiagnosed entity but quite a disabling complication. All the postulated theories regarding the pathogenesis of CPSP point to its origin in the central pain pathways. However, this study attempts to demonstrate the role of other contributing areas in the generation of CPSP.

Materials and Methods

In this single-center tertiary care hospital-based study, 24 patients with both ischemic and hemorrhagic strokes of variable durations were recruited, and Magnetic Resonance Imaging (MRI) imaging with diffusion tensor imaging (DTI) acquisition was done. Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values of the spinothalamic tract (STT), corticospinal tract (CST), superior thalamic radiation (STR), basal ganglia (BG), and primary somatosensory cortex (SSC) were compared between normal and abnormal sides and also in extrathalamic lesions separately.

Results

Significant differences with lower FA were noted in STT, CST, STR, and SSC and higher ADC values in BG, STR, CST, and SSC on comparison between the normal and lesion sides. On individual sub-analysis, ischemic stroke had significant changes in the FA value of CST and the ADC value of STR and CST, while hemorrhagic stroke had significant changes in the FA and ADC values of STR and SSC, as well as the FA value of STT. In the analysis of the extrathalamic strokes, significance persisted in all the studied parameters except the BG. The CST abnormalities were evident even in patients with clinical motor improvement. On multivariate analysis, visual analogue scale score severity was correlated with thalamic lesions.

Conclusion

Contrary to the belief that STT is solely responsible for CPSP, the role of CST, STR, BG, and SSC as contributing areas is evident from this study and may be more well established if studied in a larger population.

Clinical feasibility of deep learning reconstruction in liver diffusion-weighted imaging: Improvement of image quality and impact on apparent diffusion coefficient value

PURPOSE

Diffusion-weighted imaging (DWI) of the liver suffers from low resolution, noise, and artifacts. This study aimed to investigate the effect of deep learning reconstruction (DLR) on image quality and apparent diffusion coefficient (ADC) quantification of liver DWI at 3 Tesla.

METHOD

In this prospective study, images of the liver obtained at DWI with b-values of 0 (DWI0), 50 (DWI50) and 800 s/mm2 (DWI800) from consecutive patients with liver lesions from February 2022 to February 2023 were reconstructed with and without DLR (non-DLR). Image quality was assessed qualitatively using Likert scoring system and quantitatively using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and liver/parenchyma boundary sharpness from region-of-interest (ROI) analysis. ADC value of lesion were measured. Phantom experiment was also performed to investigate the factors that determine the effect of DLR on ADC value. Qualitative score, SNR, CNR, boundary sharpness, and apparent diffusion coefficients (ADCs) for DWI were compared using paired t-test and Wilcoxon signed rank test. P < 0.05 was considered statistically significant.

RESULTS

A total of 85 patients with 170 lesions were included. DLR group showed a higher qualitative score than the non-DLR group. for example, with DWI800 the score was 4.77 ± 0.52 versus 4.30 ± 0.63 (P < 0.001). DLR group also showed higher SNRs, CNRs and boundary sharpness than the non-DLR group. DLR reduced the ADC of malignant tumors (1.105[0.904, 1.340] versus 1.114[0.904, 1.320]) (P < 0.001), but there was no significant difference in the diagnostic value of malignancy for DLR and non-DLR groups (P = 57.3). The phantom study confirmed a reduction of ADC in images with low resolution, and a stronger reduction of ADC in heterogeneous structures than in homogeneous ones (P < 0.001).

CONCLUSIONS

DLR improved image quality of liver DWI. DLR reduced the ADC value of lesions, but did not affect the diagnostic performance of ADC in distinguishing malignant tumors on a 3.0-T MRI system.

Accurate, repeatable, and geometrically precise diffusion-weighted imaging on a 0.35 T magnetic resonance imaging-guided linear accelerator

Background and purpose

Diffusion weighted imaging (DWI) allows for the interrogation of tissue cellularity, which is a surrogate for cellular proliferation. Previous attempts to incorporate DWI into the workflow of a 0.35 T MR-linac (MRL) have lacked quantitative accuracy. In this study, accuracy, repeatability, and geometric precision of apparent diffusion coefficient (ADC) maps produced using an echo planar imaging (EPI)-based DWI protocol on the MRL system is illustrated, and in vivo potential for longitudinal patient imaging is demonstrated.

Materials and methods

Accuracy and repeatability were assessed by measuring ADC values in a diffusion phantom at three timepoints and comparing to reference ADC values. System-dependent geometric distortion was quantified by measuring the distance between 93 pairs of phantom features on ADC maps acquired on a 0.35 T MRL and a 3.0 T diagnostic scanner and comparing to spatially precise CT images. Additionally, for five sarcoma patients receiving radiotherapy on the MRL, same-day in vivo ADC maps were acquired on both systems, one of which at multiple timepoints.

Results

Phantom ADC quantification was accurate on the 0.35 T MRL with significant discrepancies only seen at high ADC. Average geometric distortions were 0.35 (±0.02) mm and 0.85 (±0.02) mm in the central slice and 0.66 (±0.04) mm and 2.14 (±0.07) mm at 5.4 cm off-center for the MRL and diagnostic system, respectively. In the sarcoma patients, a mean pretreatment ADC of 910x10-6 (±100x10-6) mm2/s was measured on the MRL.

Conclusions

The acquisition of accurate, repeatable, and geometrically precise ADC maps is possible at 0.35 T with an EPI approach.

Correlation between synthetic MRI relaxometry and apparent diffusion coefficient in breast cancer subtypes with different neoadjuvant therapy response

BACKGROUND

To evaluate the correlation between synthetic MRI (syMRI) relaxometry and apparent diffusion coefficient (ADC) maps in different breast cancer subtypes and treatment response subgroups.

METHODS

Two hundred sixty-three neoadjuvant therapy (NAT)-treated breast cancer patients with baseline MRI were enrolled. Tumor annotations were obtained by drawing regions of interest (ROIs) along the lesion on T1/T2/PD and ADC maps respectively. Histogram features from T1/T2/PD and ADC maps were respectively calculated, and the correlation between each pair of identical features was analyzed. Meanwhile, features between different NAT treatment response groups were compared, and their discriminatory power was evaluated.

RESULTS

Among all patients, 20 out of 27 pairs of features weakly correlated (r = - 0.13-0.30). For triple-negative breast cancer (TNBC), features from PD map in the pathological complete response (pCR) group (r = 0.60-0.86) showed higher correlation with ADC than that of the non-pCR group (r = 0.30-0.43), and the mean from the ADC and PD maps in the pCR group strongly correlated (r = 0.86). For HER2-positive, few correlations were found both in the pCR and non-pCR groups. For luminal HER2-negative, T2 map correlated more with ADC than T1 and PD maps. Significant differences were seen in T2 low percentiles and median in the luminal-HER2 negative subtype, yielding moderate AUCs (0.68/0.72/0.71).

CONCLUSIONS

The relationship between ADC and PD maps in TNBC may indicate different NAT responses. The no-to-weak correlation between the ADC and syMRI suggests their complementary roles in tumor microenvironment evaluation.

CRITICAL RELEVANCE STATEMENT

The relationship between ADC and PD maps in TNBC may indicate different NAT responses, and the no-to-weak correlation between the ADC and syMRI suggests their complementary roles in tumor microenvironment evaluation.

KEY POINTS

• The relationship between ADC and PD in TNBC indicates different NAT responses. • The no-to-weak correlations between ADC and syMRI complementarily evaluate tumor microenvironment. • T2 low percentiles and median predict NAT response in luminal-HER2-negative subtype.

Whole-tumor histogram analysis of multi-parametric MRI for differentiating brain metastases histological subtypes in lung cancers: relationship with the Ki-67 proliferation index

This study aims to investigate the predictive value of preoperative whole-tumor histogram analysis of multi-parametric MRI for histological subtypes in patients with lung cancer brain metastases (BMs) and explore the correlation between histogram parameters and Ki-67 proliferation index. The preoperative MRI data of 95 lung cancer BM lesions obtained from 73 patients (42 men and 31 women) were retrospectively analyzed. Multi-parametric MRI histogram was used to distinguish small-cell lung cancer (SCLC) from non-small cell lung cancer (NSCLC), and adenocarcinoma (AC) from squamous cell carcinoma (SCC), respectively. The T1-weighted contrast-enhanced (T1C) and apparent diffusion coefficient (ADC) histogram parameters of the volumes of interest (VOIs) in all BMs lesions were extracted using FireVoxel software. The following histogram parameters were obtained: maximum, minimum, mean, standard deviation (SD), variance, coefficient of variation (CV), skewness, kurtosis, entropy, and 1st-99th percentiles. Then investigated their relationship with the Ki-67 proliferation index. The skewness-T1C, kurtosis-T1C, minimum-ADC, mean-ADC, CV-ADC and 1st - 90th ADC percentiles were significantly different between the SCLC and NSCLC groups (all p < 0.05). When the 10th-ADC percentile was 668, the sensitivity, specificity, and accuracy (90.80%, 76.70% and 86.32%, respectively) for distinguishing SCLC from NSCLC reached their maximum values, with an AUC of 0.895 (0.824 - 0.966). Mean-T1C, CV-T1C, skewness-T1C, 1st - 50th T1C percentiles, maximum-ADC, SD-ADC, variance-ADC and 75th - 99th ADC percentiles were significantly different between the AC and SCC groups (all p < 0.05). When the CV-T1C percentiles was 3.13, the sensitivity, specificity and accuracy (75.00%, 75.60% and 75.38%, respectively) for distinguishing AC and SCC reached their maximum values, with an AUC of 0.829 (0.728-0.929). The 5th-ADC and 10th-ADC percentiles were strongly correlated with the Ki-67 proliferation index in BMs. Multi-parametric MRI histogram parameters can be used to identify the histological subtypes of lung cancer BMs and predict the Ki-67 proliferation index.

Analysis of Pancreatobiliary and Intestinal Type Periampullary Carcinomas Using Volumetric Apparent Diffusion Coefficient Histograms

RATIONALE AND OBJECTIVES

Magnetic resonance imaging plays an important role in the evaluation of patients with known or suspected periampullary masses. The utilization of volumetric apparent diffusion coefficient (ADC) histogram evaluation for the entire lesion eradicates the potential for subjectivity in the region of interest placement, thus guaranteeing the accuracy of computation and repeatability.

PURPOSE

To investigate the value of volumetric ADC histogram analysis in the differentiation of intestinal-type (IPAC) and pancreatobiliary-type periampullary adenocarcinomas (PPAC).

MATERIALS AND METHODS

This retrospective study included 69 patients with histopathologically confirmed periampullary adenocarcinoma (54 PPAC and 15 IPAC). Diffusion-weighted imaging was obtained at b values of 1000 mm²/s. The histogram parameters of ADC values, comprising the mean, minimum, maximum, 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles, as well as skewness, kurtosis, and variance, were calculated independently by two radiologists. Using the interclass correlation coefficient, the interobserver agreement was evaluated.

RESULTS

The ADC parameters for the PPAC group were all lower than those of the IPAC group. The PPAC group had higher variance, skewness, and kurtosis than the IPAC group. However, the difference between the kurtosis (P = .003), the 5th (P = .032), 10th (P = .043), and 25th (P = .037) percentiles of ADC values was statistically significant. The area under the curve (AUC) of the kurtosis was the highest (AUC=0.752; cut-off value=-0.235; sensitivity=61.1%; specificity=80.0%).

CONCLUSION

Volumetric ADC histogram analysis with b values of 1000 mm²/s can discriminate subtypes noninvasively before surgery.

Association of quantifiable prostate MRI parameters with any and large cribriform pattern in prostate cancer patients undergoing radical prostatectomy

PURPOSE

Cribriform pattern has recently been recognized as an important independent risk factor for prostate cancer (PCa) outcome. This study aimed to identify the association of quantifiable prostate magnetic resonance imaging (MRI) parameters with any and large cribriform pattern at radical prostatectomy (RP) specimens.

METHODS

Preoperative prostate MRI's from 188 men undergoing RP between 2010 and 2018 were retrospectively acquired. RP specimens of the patients were revised for Gleason score (GS), and presence of any and large cribriform pattern. MRI parameters such as MRI visibility, PI-RADS score, lowest apparent diffusion coefficient (ADC) value, lesion size, and radiologic extra-prostatic extension (EPE) were reviewed. The association of prostate MRI parameters for presence of any and large cribriform pattern at RP was analysed using logistic regression.

RESULTS

116/188 (61.7%) PCa patients had any cribriform and 36/188 (19.1%) large cribriform pattern at RP. 171/188 (91.0%) men had MRI-visible lesions; 111/116 (95.7%) tumours with any and 36/36 (100%) with large cribriform pattern were visible at MRI. PCa with any and large cribriform pattern both had lower ADC values than those without (p < 0.001). In adjusted analysis, lowest ADC value was as an independent predictor for any cribriform (Odds Ratio (OR) 0.2, 95% Confidence Interval (CI) 0.1-0.8; p = 0.01) and large cribriform pattern (OR 0.2, 95% CI 0.1-0.7; p = 0.01), while other parameters were not.

CONCLUSIONS

The majority of PCa with cribriform pattern at RP were visible at MRI, and lowest ADC value was an independent predictor for both any and large cribriform pattern.

Relationship between apparent diffusion coefficient and survival as a function of distance from gross tumor volume on radiation planning MRI in newly diagnosed glioblastoma

PURPOSE

To investigate the changes in apparent diffusion coefficient (ADC) within incrementally-increased margins beyond the gross tumor volume (GTV) on post-operative radiation planning MRI and their prognostic utility in glioblastoma.

METHODS

Radiation planning MRIs of adult patients with newly diagnosed glioblastoma from 2017 to 2020 were assessed. The ADC values were normalized to contralateral normal white matter (nADC). Using 1 mm isotropic incremental margin increases from the GTV, the nADC values were calculated at each increment. Age, ECOG performance status, extent of resection and MGMT promoter methylation status were obtained from medical records. Using univariate and multivariable Cox regression analysis, association of nADC to progression-free and overall survival (PFS, OS) was assessed at each increment.

RESULTS

Seventy consecutive patients with mean age of 53.6 ± 10.3 years, were evaluated. The MGMT promoter was methylated in 31 (44.3%), unmethylated in 36 (51.6%) and unknown in 3 (4.3%) patients. 11 (16%) underwent biopsy, 41 (44%) subtotal resection and 18 (26%) gross total resection. For each 1 mm increase in distance from GTV, the nADC decreased by 0.16% (p < 0.0001). At 1-5 mm increment, the nADC was associated with OS (p < 0.01). From 6 to 11 mm increment the nADC was associated with OS with the p-value gradually increasing from 0.018 to 0.046. nADC was not associated with PFS.

CONCLUSION

The nADC values at 1-11 mm increments from the GTV margin were associated with OS. Future prospective multicenter studies are needed to validate the findings and to pave the way for the utilization of ADC for margin reduction in radiation planning.

Recommendations for improved reproducibility of ADC derivation on behalf of the Elekta MRI-linac consortium image analysis working group

BACKGROUND AND PURPOSE

The apparent diffusion coefficient (ADC), a potential imaging biomarker for radiotherapy response, needs to be reproducible before translation into clinical use. The aim of this study was to evaluate the multi-centre delineation- and calculation-related ADC variation and give recommendations to minimize it.

MATERIALS AND METHODS

Nine centres received identical diffusion-weighted and anatomical magnetic resonance images of different cancerous tumours (adrenal gland, pelvic oligo metastasis, pancreas, and prostate). All centres delineated the gross tumour volume (GTV), clinical target volume (CTV), and viable tumour volume (VTV), and calculated ADCs using both their local calculation methods and each of the following calculation conditions: b-values 0-500 vs. 150-500 s/mm2, region-of-interest (ROI)-based vs. voxel-based calculation, and mean vs. median. ADC variation was assessed using the mean coefficient of variation across delineations (CVD) and calculation methods (CVC). Absolute ADC differences between calculation conditions were evaluated using Friedman's test. Recommendations for ADC calculation were formulated based on observations and discussions within the Elekta MRI-linac consortium image analysis working group.

RESULTS

The median (range) CVD and CVC were 0.06 (0.02-0.32) and 0.17 (0.08-0.26), respectively. The ADC estimates differed 18% between b-value sets and 4% between ROI/voxel-based calculation (p-values < 0.01). No significant difference was observed between mean and median (p = 0.64). Aligning calculation conditions between centres reduced CVC to 0.04 (0.01-0.16). CVD was comparable between ROI types.

CONCLUSION

Overall, calculation methods had a larger impact on ADC reproducibility compared to delineation. Based on the results, significant sources of variation were identified, which should be considered when initiating new studies, in particular multi-centre investigations.

Prospective evaluation of in vivo and phantom repeatability and reproducibility of diffusion-weighted MRI sequences on 1.5 T MRI-linear accelerator (MR-Linac) and MR simulator devices for head and neck cancers

INTRODUCTION

Diffusion-weighted imaging (DWI) on MRI-linear accelerator (MR-linac) systems can potentially be used for monitoring treatment response and adaptive radiotherapy in head and neck cancers (HNC) but requires extensive validation. We performed technical validation to compare six total DWI sequences on an MR-linac and MR simulator (MR sim) in patients, volunteers, and phantoms.

METHODS

Ten human papillomavirus-positive oropharyngeal cancer patients and ten healthy volunteers underwent DWI on a 1.5 T MR-linac with three DWI sequences: echo planar imaging (EPI), split acquisition of fast spin echo signals (SPLICE), and turbo spin echo (TSE). Volunteers were also imaged on a 1.5 T MR sim with three sequences: EPI, BLADE (vendor tradename), and readout segmentation of long variable echo trains (RESOLVE). Participants underwent two scan sessions per device and two repeats of each sequence per session. Repeatability and reproducibility within-subject coefficient of variation (wCV) of mean ADC were calculated for tumors and lymph nodes (patients) and parotid glands (volunteers). ADC bias, repeatability/reproducibility metrics, SNR, and geometric distortion were quantified using a phantom.

RESULTS

In vivo repeatability/reproducibility wCV for parotids were 5.41%/6.72%, 3.83%/8.80%, 5.66%/10.03%, 3.44%/5.70%, 5.04%/5.66%, 4.23%/7.36% for EPIMR-linac, SPLICE, TSE, EPIMR sim, BLADE, RESOLVE. Repeatability/reproducibility wCV for EPIMR-linac, SPLICE, TSE were 9.64%/10.28%, 7.84%/8.96%, 7.60%/11.68% for tumors and 7.80%/9.95%, 7.23%/8.48%, 10.82%/10.44% for nodes. All sequences except TSE had phantom ADC biases within ± 0.1x10-3 mm2/s for most vials (EPIMR-linac, SPLICE, and BLADE had 2, 3, and 1 vials out of 13 with larger biases, respectively). SNR of b = 0 images was 87.3, 180.5, 161.3, 171.0, 171.9, 130.2 for EPIMR-linac, SPLICE, TSE, EPIMR sim, BLADE, RESOLVE.

CONCLUSION

MR-linac DWI sequences demonstrated near-comparable performance to MR sim sequences and warrant further clinical validation for treatment response assessment in HNC.

Application value of simultaneous multislice readout-segmented echo-planar imaging for diffusion-weighted MRI in differentiation of rectal cancer grade

OBJECTIVE

To analyze the association of apparent diffusion coefficient (ADC) values measured by readout-segmented echo-planar imaging (rs-EPI) using different simultaneous multislice (SMS) acceleration factors and the differentiation of rectal cancer grade.

MATERIALS AND METHODS

Patients with non-mucinous rectal adenocarcinoma diagnosed by biopsy (endoscope-guided biopsy or surgical resection) were retrospectively collected, and each patient underwent an MRI examination. ADC values of rs-EPI, 2 × SMS rs-EPI, and 3 × SMS rs-EPI were recorded as ADC1, ADC2, and ADC3, respectively.

RESULTS

The scanning time of 2 × SMS rs-EPI was 60 s, 56.2% shorter than 137 s of rs-EPI sequence, while that of 3 × SMS rs-EPI was 51 s, 72.8% less than that of rs-EPI time. The ADC value of the three groups dropped with the decrease in cancer grade (p < 0.05). The AUC values of ADC1, ADC2, and ADC3 in predicting highly differentiated rectal cancer were 0.74, 0.729, and 0.687, respectively. The difference in AUC values between ADC1 and ADC2 was not statistically significant (p = 0.889).

DISCUSSION

SMS technology with an acceleration factor of 2 could be applied clinically to evaluate the pathological differentiation of rectal cancer grade.

Same-day repeatability and Between-Sequence reproducibility of Mean ADC in PI-RADS lesions

PURPOSE

This study aimed to assess repeatability after repositioning (inter-scan), intra-rater, inter-rater and inter-sequence variability of mean apparent diffusion coefficient (ADC) measurements in MRI-detected prostate lesions.

METHOD

Forty-three patients with suspicion for prostate cancer were included and received a clinical prostate bi-/multiparametric MRI examination with repeat scans of the T2-weighted and two DWI-weighted sequences (ssEPI and rsEPI). Two raters (R1 and R2) performed single-slice, 2D regions of interest (2D-ROIs) and 3D-segmentation-ROIs (3D-ROIs). Mean bias, corresponding limits of agreement (LoA), mean absolute difference, within-subject coefficient of variation (CoV) and repeatability/reproducibility coefficient (RC/RDC) were calculated. Bradley & Blackwood test was used for variance comparison. Linear mixed models (LMM) were used to account for multiple lesions per patient.

RESULTS

Inter-scan repeatability, intra-rater and inter-sequence reproducibility analysis of ADC showed no significant bias. 3D-ROIs demonstrated significantly less variability than 2D-ROIs (p < 0.01). Inter-rater comparison demonstrated small significant systematic bias of 57 × 10-6 mm2/s for 3D-ROIs (p < 0.001). Intra-rater RC, with the lowest variation, was 145 and 189 × 10-6 mm2/s for 3D- and 2D-ROIs, respectively. For 3D-ROIs of ssEPI, RCs and RDCs were 190-198 × 10-6 mm2/s for inter-scan, inter-rater and inter-sequence variation. No significant differences were found for inter-scan, inter-rater and inter-sequence variability.

CONCLUSIONS

In a single-scanner setting, single-slice ADC measurements showed considerable variation, which may be lowered using 3D-ROIs. For 3D-ROIs, we propose a cut-off of ∼ 200 × 10-6 mm2/s for differences introduced by repositioning, rater or sequence effects. The results suggest that follow-up measurements should be possible by different raters or sequences.

Objective Value of the Apparent Diffusion Coefficient (ADC) Map from Ultrahigh b-value Diffusion-weighted Imaging (DWI) in 3T MRI could be a Non-invasive Specific Biomarker for Prostate Cancer

OBJECTIVE

This article aims to explore the ADC value of ultrahigh b-value DWI and the diagnostic cutoff point in prostate cancer.

METHODS

A total of 78 patients were included in this study. T2 weighted imaging (T2WI), conventional diffusion-weighted imaging (DWI) (1000 s/mm2), and DWI with ultrahigh b-values of 2000 s/mm2 and 3000 s/mm2 were performed in each patient. With reference biopsy as the gold standard, the apparent diffusion coefficient (ADC)s of each b-value DWI image were analyzed. According to different b-value receiver operating characteristic (ROC) curves, the ADC diagnostic cutoff point for prostate cancer was determined.

RESULTS

A total of 154 lesions were identified as prostate cancer. The ADC values for conventional DWI and ultrahigh b-value DWI with 2000 s/mm2 and 3000 s/mm2 were 1.097×10-3 mm2/s (1.040-1.153), 0.809×10-3 mm2/s (0.766-0.851) and 0.622×10-3 mm2/s (0.591-0.652), respectively, in the peripheral zone and 1.085×10-3 mm2/s (1.022-1.147), 0.815×10-3 mm2/s (0.770-0.861) and 0.651×10-3 mm2/s (0.617-0.685) in the transition zone. The area under the curve (AUC)s of the ADC values from ultrahigh b-value DWI (2000 s/mm2 and 3000 s/mm2) were 0.824 and 0.852 in the peripheral zone and 0.905 for the ADC values from ultrahigh b-value DWI (3000 s/mm2) in the transition zone. In the peripheral zone, the ADC diagnostic cutoff values for prostate cancer were 0.75×10-3 mm2/s and 0.685×10-3 mm2/s in DWI at 2000 s/mm2 and 3000 s/mm2, respectively, and the diagnosis of transition zone cancer was 0.8×10-3 mm2/s and 0.634×10-3 mm2/s, respectively.

CONCLUSION

The ADC values from ultrahigh b-value DWI demonstrated better consistency and diagnostic efficacy in the diagnosis of prostate cancer.

Variabilities in apparent diffusion coefficient (ADC) measurements of the spleen and the paraspinal muscle: A single center large cohort study

Purpose

Evaluation of the variabilities in apparent diffusion coefficient (ADC) measurements of the spleen (ADC_spleen) and the paraspinal muscles (ADC_muscle) to identify the reference organ for normalizing the ADC from the abdominal diffusion weighted imaging (DWI).

Methods

Two MRI scanners, with 314 abdominal exams on the GE and 929 on the Siemens system, were used for MRI examinations including DWI (b-values, 50 and 800 s/mm²). For a subset of 73 exams on the Siemens system a second exam was conducted. Four regions of interest (ROIs) in each exam were placed to measure the ADC_spleen and the bilateral ADC_muscle. ADC variability between patients (on each scanner separately), ADC variability due to ROI placement between the two ROIs in each organ, and variability in the subset between the first and second exams were assessed.

Results

The ADC_spleen was more scattered and variable than the ADC_muscle in the comparability (n = 929 and 314 for two MRI scanners, respectively) and repeatability (n = 73) datasets. The Bland-Altmann bias and limits of agreement (LoAs) for the ADC_spleen (ICC, 0.47; CV, 0.070) and ADC_muscle (ICC, 0.67; CV, 0.023) in the repeatability datasets (n = 73) were -0.1 (-25.7%-25.6%) and -0.3 (-8.8%-8.1%), respectively. For the Siemens system, the Bland-Altmann bias and LoAs for the ADC_spleen (ICC, 0.72; CV, 0.061) and ADC_muscle (ICC, 0.53; CV, 0.030) in the comparability datasets (n = 929) were 2.1 (-20.0%-24.2%) and 0.7 (-10.0%-11.4%), respectively. Similar findings have been found in the GE system (n = 314). The CVs for the ADC_muscle measurements were lower than those of the ADC_spleen both in the repeatability and the comparability analyses (all p < 0.001).

Conclusion

Paraspinal muscles demonstrate better reference characteristics than the spleen in estimating ADC variability of abdominal DWI.

Utility of apparent diffusion coefficient histogram analysis in differentiating microcystic meningioma from intracranial solitary fibrous tumor

PURPOSE

To investigate the possibility of histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating microcystic meningioma (MM) from intracranial solitary fibrous tumor (SFT).

MATERIALS AND METHODS

Eighteen patients with MM and 23 patients with SFT were enrolled in this retrospective study. Conventional magnetic resonance imaging (MRI) features and nine ADC histogram parameters (including mean, 1st (ADC1), 10th (ADC10), 50th (ADC50), 90th (ADC90), and 99th (ADC99) percentiles ADC, as well as variance, skewness, and kurtosis) between MM and SFT were compared. The diagnostic performance of the optimal parameter was determined by the receiver operating characteristic analysis.

RESULTS

SFT showed a significantly lower mean, ADC1, ADC10, ADC50, ADC90, and ADC99 than MM (all P < 0.05). while no significant difference was found in conventional MRI features or other ADC histogram parameters (all P > 0.05). ADC1 was identified as the optimal parameter in differentiating between MM and SFT, which achieved an area under the curve of 0.861, with sensitivity, specificity, and accuracy of 78.26%, 88.89%, and 82.93%, respectively.

CONCLUSIONS

MM and SFT show overlapping conventional MRI features. ADC histogram analysis helps to differentiate between MM and SFT, with ADC1 being the optimal parameter with the best discrimination performance.