Apparent Diffusion Coefficient Maps Research Articles

Sample strategies for the assessment of the apparent diffusion coefficient in single large intracranial space-occupying lesions of dogs and cats

Diffusion-weighted imaging is increasingly available for brain investigation. Image interpretation of intracranial space-occupying lesions often includes the derived apparent diffusion coefficient (ADC) analysis. In human medicine, ADC can help discriminate between benign and malignant lesions in intracranial tumors. This study investigates the difference in ADC values depending on the sample strategies of image analysis. MRI examination, including diffusion-weighted images of canine and feline patients presented between 2015 and 2020, were reviewed retrospectively. Patients with single, large intracranial space-occupying lesions were included. Lesions homogeneity was subjectively scored. ADC values were calculated using six different methods of sampling (M1–M6) on the ADC map. M1 included as much as possible of the lesion on a maximum of five consecutive slices; M2 included five central and five peripheral ROIs; M3 included a single ROI on the solid part of the lesion; M4 included three central ROIs on one slice; M5 included three central ROIs on different slices; and M6 included one large ROI on the entire lesion. A total of 201 animals of various breeds, genders, and ages were analyzed. ADC values differed significantly between M5 against M2 (peripheral) (p < 0.001), M5 against M6 (p = 0.009), and M4 against M2 (peripheral) (p = 0.005). When lesions scored as homogeneous in all sequences were excluded, an additional significant difference in three further sampling methods was present (p < 0.005). ADC of single, large, intracranial space-occupying lesions differed significantly in half of the tested methods of sampling. Excluding homogeneous lesions, additional significant differences among the sampling methods were present. The obtained results should increase awareness of the variability of the ADC, depending on the sample strategies used.

Analysis of the clinical diagnostic capabilities of kidney imaging biomarkers based on diffusion-weighted imaging and ASL-perfusion data

Acute kidney failure is a polyetiological disease that is often occurs in patients, which requires a rapid therapeutic response to avoid progression of the condition. Magnetic resonance imaging of the kidneys (MRI) improves the understanding and assessment of pathological processes, supplementing the mandatory diagnostic minimum information in the study of kidney pathology.Objective. To evaluate the clinical diagnostic capabilities of kidney imaging biomarkers using diffusion-weighted imaging (DWI) and ASL-perfusion data for intensive care unit patients.Materials and methods. At the first Clinical Hospital in Smolensk 54 patients were examined who were inpatient treatment and in the intensive care unit. All patients underwent ultrasound examination of the kidneys with assessment of blood flow in the main kidney vessels, MRI of the kidneys, with DWI of the kidney parenchyma and ASL-perfusion included in the study protocol. MRI was carried with assessed b-factor 800 and was drawn apparent diffusion coefficient (ADC) maps. When carried ASL perfusion, the field of view covered the area of the parenchyma of both kidneys. The reference method was glomerular filtration rate for all of the patients. The control group was represented by healthy people (n=59). Consent to participate in the study was obtained from patients or their legal representatives. All stages of the study comply with the normative and regulatory documentation of Ministry of Health ща the Russian Federation.Results. It was concluded that there is a significant difference in ADC for prerenal causes and in ASL for renal causes. In 9 (16.7 %) patients, kidney ADC was 1.5±0.3 mm2/sec, ASL – perfusion – ≤ 250 ml/100g/min, compared with the control group. In this group, negative dynamics were observed within a week, тnegative laboratory dynamics correlates with radiation-based kidney biomarkers (r=0,965).Conclusion. The criteria of DWI and ASL-perfusion allow us to suspect or confirm the cause of acute kidney failure. A high correlation has been established between radiation biomarkers and laboratory criteria of functional damages (r=0.998), direct correlation between imaging biomarkers and organic kidney disease (r=0,901). These biomarkers make it possible to predict the first stage of acute kidney failure (AUROC 0,995, DI 0,867–0,999).

Abstract PO5-07-03: Integrating mechanism-based and data-driven modeling to predict the response of triple negative breast cancer to therapy

Abstract Introduction Neoadjuvant chemotherapy (NAC) is the standard-of-care (SOC) for patients with locally advanced triple-negative breast cancer (TNBC) [1]. Unfortunately, only about half of TNBC patients achieve a pathological complete response (pCR) at the completion of NAC [2]. With the recent FDA approval of immunotherapy, the response rate improved by about 8% [3]. To try to further improve NAC response rates, we build upon methods that predict treatment response to optimize interventions and outcomes. Our previous studies have demonstrated that excellent predictive accuracy of TNBC response to NAC was achieved through calibrating mechanism-based models to both pre- and on- treatment imaging data [4]. However, by requiring on-treatment imaging data, therapy optimization can only occur after a patient begins NAC. Here, our goal is to relax the requirement of on-treatment imaging data such that we can predict, and potentially optimize, a patient’s response to NAC before initiating it. We will accomplish this through combining data-driven approaches with mechanism-based modeling to obtain spatiotemporal predictions of tumor response prior to NAC. More specifically, we integrate a reaction-diffusion equation based mathematical model with a U-Net based convolutional neural network (CNN). Methods The reaction-diffusion equation models the development of tumor cellularity over time as the sum of tumor cell diffusion, controlled by a fixed diffusivity, and tumor cell proliferation, controlled by a calibrated net proliferation rate. The net proliferation rate implicitly accounts for both proliferative and drug induced death effects. We calibrated the net proliferation rates in this model on a patient-specific basis for 128 patients from the ARTEMIS trial (NCT02276443) using three imaging timepoints – pre- (V1), post two cycles (V2), and post four cycles (V3) of A/C. We then employed a CNN to characterize the relationship between the calibrated net proliferation rates and the pre-treatment imaging data. The CNN takes pretreatment imaging inputs of the apparent diffusion coefficient map, percent enhancement maps from the dynamic contrast enhanced MRI timecourse, and precontrast T1 map. The CNN outputs an estimate of the calibrated net proliferation rate which can be used to run the mathematical model forward in time to predict cellularity maps at V2/3. In training the CNN, our loss considers both the concordance correlation coefficient (CCC) and the normalized root mean square error (NRMSE). We calculated loss on the net proliferation rate predictions as NRMSE with a CCC penalty to aid in characterizing the non-Gaussian distribution of the net proliferation rate. We calculated loss on the cellularity predictions as the CCC between predicted and measured voxel-wise cellularity. The CNN was trained using the Adam optimizer and a fivefold cross validation with a withheld test set of 26 patients. Results Using the CNN predictions to forward run the mechanism-based model in time to V3 we calculated change in total tumor cellularity and volume from V1 to V3. In the test cohort, we obtained CCC values between the predicted and measured changes of 0.95 and 0.91 for cellularity and volume, respectively. Discussion and summary Through integrating mechanistic modeling and deep learning, we can accurately predict the spatiotemporal development of TNBC response to NAC using only pretreatment imaging data. We will build upon these results by expanding our model to include separate proliferation and drug induced death terms to allow for therapy intervention and optimization. Additionally, we will incorporate other pretreatment data types including genetic data. [1] Liedtke et al., J of Clin Oncol, 2008. [2] Spring et al., Clin Cancer Res, 2020. [3] Schmid et al., NEJM, 2020. [4] Wu et al., Cancer Res, 2022. Citation Format: Casey Stowers, Chengyue Wu, Sidharth Kumar, Zhan Xu, Clinton Yam, Jong Bum Son, Jingfei Ma, Jonathan Tamir, Gaiane Rauch, Thomas Yankeelov. Integrating mechanism-based and data-driven modeling to predict the response of triple negative breast cancer to therapy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-07-03.

MRI based evaluation of carcinoma gallbladder using apparent diffusion coefficient (ADC) and diffusion weighted imaging (DWI)

Background Carcinoma of the gallbladder is a malignant neoplasm, which is associated with a poor prognosis due to its often-asymptomatic nature and late-stage diagnosis. Non-invasive imaging techniques play a key role in the early detection and characterization of gallbladder carcinoma. This thesis presents a comprehensive analysis of the utility of Diffusion-Weighted Imaging (DWI) and Apparent Diffusion Coefficient (ADC) mapping in the magnetic resonance imaging (MRI) evaluation of carcinoma of the gallbladder. The primary objective of this research was to investigate the exact diagnostic accuracy and potential clinical applications of DWI and ADC in assessing carcinoma of the gallbladder and related tumour aggressiveness. A systematic review of existing literature was conducted to establish a foundation for this study with efforts to acquire relevant imaging data. The results of this study demonstrate the efficacy of DWI and ADC in the detection of carcinoma of the gallbladder when compared to conventional MRI sequences. These advanced imaging techniques hold promise in increasing the accuracy of diagnosis, ultimately leading to improved patient outcomes and more informed clinical decision making. The potential of Diffusion-Weighted Imaging (DWI) and Apparent Diffusion Coefficient (ADC) mapping in the evaluation of gallbladder cancer using magnetic resonance imaging (MRI) is thoroughly analysed in this thesis. Methods A descriptive prospective study will be done at Acharya Vinoba Bhave Rural Hospital, Sawangi, involving 20 patients with clinically suspected carcinoma gallbladder, who are referred to the department of Radiodiagnosis. These patients will be subjected to study-purposive sampling. Conclusion After an appropriate statistical analysis, we expect to assess the role of Diffusion-Weighted Imaging (DWI) and Apparent Diffusion Coefficient (ADC) mapping in the magnetic resonance imaging (MRI) evaluation of carcinoma of the gallbladder. CTRI registration This trial has been submitted to Clinical Trials Registry-India (CTRI) for Review. Registration Number: REF/2023/09/072780 Registration date:01/05/2023 URL of trial: https://www.ctri.nic.in/

Reassessment of practice of Chinese surgeons since introduction of the watch and wait strategy after neoadjuvant therapy for rectal cancer

Objective: To investigate perspectives and changes in treatment selection by Chinese surgeons since introduction of the watch-and-wait approach after neoadjuvant therapy for rectal cancer. Methods: A cross-sectional survey was conducted using a questionnaire distributed through the "Wenjuanxing" online survey platform. The survey focused on the recognition and practices of Chinese surgeons regarding the strategy of watch-and-wait after neoadjuvant therapy for rectal cancer and was disseminated within the China Watch-and-Wait Database (CWWD) WeChat group. This group targets surgeons of deputy chief physician level and above in surgical, radiotherapy, or internal medicine departments of nationally accredited tumor-specialist or comprehensive hospitals (at provincial or municipal levels) who are involved in colorectal cancer diagnosis and treatment. From 13 to 16 December 2023, 321 questionnaires were sent with questionnaire links in the CWWD WeChat group. The questionnaires comprised 32 questions encompassing: (1) basic physician characteristics (including surgical volume); (2) assessment methods and criteria for clinical complete response (cCR); (3) patients eligible for watch-and-wait; (4) neoadjuvant therapies and other measures for achieving cCR; (5) willingness to implement watch-and-wait and factors influencing that willingness; (6) risks and monitoring of watch-and-wait; (7) subsequent treatment and follow-up post watch-and-wait; (8) suggestions for development of the CWWD. Descriptive statistics were employed for data analysis, with intergroup comparisons conducted using the χ2 or Fisher's exact probability tests. Results: The response rate was 31.5%, comprising 101 responses from the 321 individuals in the WeChat group. Respondents comprised 101 physicians from 70 centers across 23 provinces, municipalities, and autonomous regions nationwide, 85.1% (86/101) of whom represented provincial tertiary hospitals. Among the respondents, 87.1% (88/101) had implemented the watch-and-wait strategy. The approval rate (65.6%, 21/32) and proportion of patients often informed (68.8%, 22/32) were both significantly higher for doctors in oncology hospitals than for those in general hospitals (27.7%, 18/65; 32.4%, 22/68) (χ2=12.83, P<0.001; χ2=11.70, P=0.001, respectively). The most used methods for diagnosing cCR were digital rectal examination (90.1%, 91/101), colonoscopy (91.1%, 92/101), and rectal T2-weighted magnetic resonance imaging (86.1%, 87/101). Criteria used to identify cCR comprised absence of a palpable mass on digital rectal examination (87.1%, 88/101), flat white scars or new capillaries on colonoscopy (77.2%, 78/101), absence of evident tumor signals on rectal T2-weighted sequences or T2WI low signals or signals equivalent to the intestinal wall (83.2%, 84/101), and absence of tumor hyperintensity on diffusion-weighted imaging with no corresponding hypointensity on apparent diffusion coefficient maps (66.3%, 67/101). As for selection of neoadjuvant regimen and assessment of cCR, 57.4% (58/101) of physicians preferred a long course of radiotherapy with or without induction and/or consolidation capecitabine + oxaliplatin, whereas 25.7% (26/101) preferred immunotherapy in combination with chemotherapy and concurrent radiotherapy. Most (96.0%, 97/101) physicians believed that the primary lesion should be assessed ≤12 weeks after completion of radiotherapy. Patients were frequently informed about the possibility of achieving cCR after neoadjuvant therapy and the strategy of watch-and-wait by 43.6% (44/101) of the responding physicians and 38.6% (39/101) preferred watch-and-wait for patients who achieved cCR or near cCR after neoadjuvant therapy for rectal cancer. Capability for multiple follow-up evaluations (70.3%, 71/101) was a crucial factor influencing physicians' choice of watch-and-wait after cCR. The proportion who patients who did not achieve cCR and underwent surgical treatment was lower in provincial tertiary hospitals (74.2%, 23/31) than in provincial general hospitals (94.5%, 52/55) and municipal hospitals (12/15); these differences are statistically significant (χ2=7.43, P=0.020). The difference between local recurrence and local regrowth was understood by 88.1% (89/101) of respondents and 87.2% (88/101) agreed with monitoring every 3 months for 5 years. An increase in local excision or puncture rates to reduce organ resections in patients with pCR was proposed by 64.4% (65/101) of respondents. Conclusion: Compared with the results of a previous survey, Chinese surgeons' awareness of the watch-and-wait concept has improved significantly. Oncologists in oncology hospitals are more aware of the concept of watch-and-wait.

To characterize small renal cell carcinoma using diffusion relaxation correlation spectroscopic imaging and apparent diffusion coefficient based histogram analysis: a preliminary study.

To study the capability of diffusion-relaxation correlation spectroscopic imaging (DR-CSI) on subtype classification and grade differentiation for small renal cell carcinoma (RCC). Histogram analysis for apparent diffusion coefficient (ADC) was studied for comparison. A total of 61 patients with small RCC (< 4 cm) were included in the retrospective study. MRI data were reviewed, including a multi-b (0-1500 s/mm2) multi-TE (51-200 ms) diffusion weighted imaging (DWI) sequence. Region of interest (ROI) was delineated manually on DWI to include solid tumor. For each patient, a D-T2 spectrum was fitted and segmented into 5 compartments, and the volume fractions VA, VB, VC, VD, VE were obtained. ADC mapping was calculated, and histogram parameters ADC 90th, 10th, median, standard deviation, skewness and kurtosis were obtained. All MRI metrices were compared between clear cell RCC (ccRCC) and non-ccRCC group, and between high-grade and low-grade group. Receiver operator curve analysis was used to assess the corresponding diagnostic performance. Significantly higher ADC 90th, ADC 10th and ADC median, and significantly lower DR-CSI VB was found for ccRCC compared to non-ccRCC. Significantly lower ADC 90th, ADC median and significantly higher VB was found for high-grade RCC compared to low-grade. For identifying ccRCC from non-ccRCC, VB showed the highest area under curve (AUC, 0.861) and specificity (0.882). For differentiating high- from low-grade, ADC 90th showed the highest AUC (0.726) and specificity (0.786), while VB also displayed a moderate AUC (0.715). DR-CSI may offer improved accuracy in subtype identification for small RCC, while do not show better performance for small RCC grading compared to ADC histogram.

Validating the diagnostic accuracy of an MRI-based scoring system for differentiating benign uterine leiomyomas from leiomyosarcomas.

Uterine leiomyomas are the most common benign uterine tumors. They are difficult to distinguish from their malignant counterparts-smooth muscle tumors of unknown malignant potential (STUMP) and leiomyosarcoma. The purpose of this study is to propose and validate the diagnostic accuracy of the MRI-based Oman-Canada Scoring System of Myometrial Masses (OCSSMM) to differentiate uterine leiomyomas from STUMP/leiomyosarcomas. This is a retrospective study performed at two tertiary care centers. All patients with a pathology-proven uterine mass who underwent pre-operative pelvic MRI between January 2010 and January 2020 were included. Using a 1.5T MRI machine, sequences included were axial/coronal/sagittal T2 and T1 weighted imaging, axial diffusion weighted and apparent diffusion coefficient map, and axial or sagittal dynamic contrast-enhanced sequences. A scoring system was designed based on previously published worrisome MRI features for uterine leiomyosarcoma. Each feature was allocated a score from 0 to 2 according to the strength of association with malignancy. Subsequently, the MR images were blindly and independently reviewed by a fellowship-trained radiologist and a clinical fellow/senior resident. Each uterine mass was scored according to their imaging features. The scores were divided into five categories according to the sum of scores. Category III and above was considered positive for leiomyosarcoma/STUMP. Sensitivity, specificity, and positive and negative predictive values were calculated. A total of 244 women were included (age range 20-74 years, mean 40). Of these, 218 patients had benign leiomyoma, 13 had STUMP, and 13 had leiomyosarcoma. The sensitivity and specificity of the scoring system were 92.3% and 64.7%, respectively. The negative predictive value was 98.6%. No leiomyosarcoma was missed using this scoring system. The presence of non-cystic T2 hyperintensity or diffusion restriction in a uterine mass were the most sensitive signs of a leiomyosarcoma/STUMP. The proposed multi-parametric MRI scoring system may be useful in differentiating benign uterine leiomyomas from leiomyosarcomas/STUMP.

High-resolution 3T to 7T ADC map synthesis with a hybrid CNN-transformer model.

7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion-weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC maps. However, 7T magnetic resonance imaging (MRI) currently suffers from limited clinical unavailability, higher cost, and increased susceptibility to artifacts. To address these issues, we propose a hybrid CNN-transformer model to synthesize high-resolution 7T ADC maps from multimodal 3T MRI. The Vision CNN-Transformer (VCT), composed of both Vision Transformer (ViT) blocks and convolutional layers, is proposed to produce high-resolution synthetic 7T ADC maps from 3T ADC maps and 3T T1-weighted (T1w) MRI. ViT blocks enabled global image context while convolutional layers efficiently captured fine detail. The VCT model was validated on the publicly available Human Connectome Project Young Adult dataset, comprising 3T T1w, 3T DWI, and 7T DWI brain scans. The Diffusion Imaging in Python library was used to compute ADC maps from the DWI scans. A total of 171 patient cases were randomly divided into 130 training cases, 20 validation cases, and 21 test cases. The synthetic ADC maps were evaluated by comparing their similarity to the ground truth volumes with the following metrics: peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean squared error (MSE). In addition, RESULTS: The results are as follows: PSNR: 27.0±0.9dB, SSIM: 0.945±0.010, and MSE: 2.0E-3±0.4E-3. Both qualitative and quantitative results demonstrate that VCT performs favorably against other state-of-the-art methods. We have introduced various efficiency improvements, including the implementation of flash attention and training on 176×208 resolution images. These enhancements have resulted in the reduction of parameters and training time per epoch by 50% in comparison to ResViT. Specifically, the training time per epoch has been shortened from 7.67 min to 3.86 min. We propose a novel method to predict high-resolution 7T ADC maps from low-resolution 3T ADC maps and T1w MRI. Our predicted images demonstrate better spatial resolution and contrast compared to 3T MRI and prediction results made by ResViT and pix2pix. These high-quality synthetic 7T MR images could be beneficial for disease diagnosis and intervention, producing higher resolution and conformal contours, and as an intermediate step in generating synthetic CT for radiation therapy, especially when 7T MRI scanners are unavailable.

Usefulness of Diffusion-Weighted Imaging in Evaluating Acute Cellular Rejection and Monitoring Treatment Response in Liver Transplant Recipients.

Acute cellular rejection (ACR) is a significant immune issue among recipients following liver transplantation. Although diffusion-weighted magnetic resonance imaging (DWI) is widely used for diagnosing liver disease, it has not yet been utilized for monitoring ACR in patients after liver transplantation. Therefore, the aim of this study was to evaluate the efficacy of DWI in monitoring treatment response among recipients with ACR. This study enrolled 25 recipients with highly suspected ACR rejection, and all subjects underwent both biochemistry and DWI scans before and after treatment. A pathological biopsy was performed 4 to 24 h after the first MRI examination to confirm ACR and degree of rejection. All patients were followed up and underwent a repeated MRI scan when their liver function returned to the normal range. After data acquisition, the DWI data were post-processed to obtain the apparent diffusion coefficient (ADC) map on a voxel-by-voxel basis. Five regions of interest were identified on the liver parenchyma to measure the mean ADC values from each patient. Finally, the mean ADC values and biochemical markers were statistically compared between ACR and non-ACR groups. A receiver operating characteristic (ROC) curve was constructed to evaluate the performance of the ADC and biochemical data in detecting ACR, and correlation analysis was used to understand the relationship between the ADC values, biochemical markers, and the degree of rejection. The histopathologic results revealed that 20 recipients had ACR, including 10 mild, 9 moderate, and 1 severe rejection. The results demonstrated that the ACR patients had significantly lower hepatic ADC values than those in patients without ACR. After treatment, the hepatic ADC values in ACR patients significantly increased to levels similar to those in non-ACR patients with treatment. The ROC analysis showed that the sensitivity and specificity for detecting ACR were 80% and 95%, respectively. Furthermore, the correlation analysis revealed that the mean ADC value and alanine aminotransferase level had strong and moderate negative correlation with the degree of rejection, respectively (r = -0.72 and -0.47). The ADC values were useful for detecting hepatic ACR and monitoring treatment response after immunosuppressive therapy.

Prediction of histopathologic grades of bladder cancer with radiomics based on MRI: Comparison with traditional MRI

PurposeTo compare biparametric magnetic resonance imaging (bp-MRI) radiomics signatures and traditional MRI model for the preoperative prediction of bladder cancer (BCa) grade. Materials and methodsThis retrospective study included 255 consecutive patients with pathologically confirmed 113 low-grade and 142 high-grade BCa. The traditional MRI nomogram model was developed using univariate and multivariate logistic regression by the mean apparent diffusion coefficient (ADC), vesical imaging reporting and data system, tumor size, and the number of tumors. Volumes of interest were manually drawn on T2-weighted imaging (T2WI) and ADC maps by 2 radiologists. Using one-way analysis of variance, correlation, and least absolute shrinkage and selection operator methods to select features. Then, a logistic regression classifier was used to develop the radiomics signatures. Receiver operating characteristic (ROC) analysis was used to compare the diagnostic abilities of the radiomics and traditional MRI models by the DeLong test. Finally, decision curve analysis was performed by estimating the clinical usefulness of the 2 models. ResultsThe area under the ROC curves (AUCs) of the traditional MRI model were 0.841 in the training cohort and 0.806 in the validation cohort. The AUCs of the 3 groups of radiomics model [ADC, T2WI, bp-MRI (ADC and T2WI)] were 0.888, 0.875, and 0.899 in the training cohort and 0.863, 0.805, and 0.867 in the validation cohort, respectively. The combined radiomics model achieved higher AUCs than the traditional MRI model. decision curve analysis indicated that the radiomics model had higher net benefits than the traditional MRI model. ConclusionThe bp-MRI radiomics model may help distinguish high-grade and low-grade BCa and outperforming the traditional MRI model. Multicenter validation is needed to acquire high-level evidence for its clinical application.

Histogram analysis of MR quantitative parameters: are they correlated with prognostic factors in prostate cancer?

To investigate the correlation between quantitative MR parameters and prognostic factors in prostate cancer (PCa). A total of 186 patients with pathologically confirmed PCa who underwent preoperative multiparametric MRI (mpMRI), including synthetic MRI (SyMRI), were enrolled from two medical centers. The histogram metrics of SyMRI [T1, T2, proton density (PD)] and apparent diffusion coefficient (ADC) values were extracted. The Mann‒Whitney U test or Student's t test was employed to determine the association between these histogram features and the prognostically relevant factors. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the differentiation performance. Spearman's rank correlation coefficients were calculated to determine the correlations between histogram parameters and the International Society of Urological Pathology (ISUP) grade group as well as pathological T stage. Significant correlations were found between the histogram parameters and the ISUP grade as well as pathological T stage of PCa. Among these histogram parameters, ADC_minimum had the strongest correlation with the ISUP grade (r = -0.481, p<0.001), and ADC_Median showed the strongest association with pathological T stage (r = -0.285, p=0.008). The ADC_10th percentile exhibited the highest performance in identifying clinically significant prostate cancer (csPCa) (AUC 0.833; 95% CI 0.771-0.883). When discriminating between the status of different prognostically relevant factors, a significant difference was observed between extraprostatic extension-positive and -negative cancers with regard to histogram parameters of the ADC map (10th percentile, 90th percentile, mean, median, minimum) and T1 map (minimum) (p=0.002-0.032). Moreover, histogram parameters of the ADC map (90th percentile, maximum, mean, median), T2 map (10th percentile, median), and PD map (10th percentile, median) were significantly lower in PCa with perineural invasion (p=0.009-0.049). The T2 values were significantly lower in patients with seminal vesicle invasion (minimum, p=0.036) and positive surgical margin (10th percentile, 90th percentile, mean, median, and minimum, p=0.015-0.025). Quantitative histogram parameters derived from synthetic MRI and ADC maps may have great potential for predicting the prognostic features of PCa.

Determination of p53abn endometrial cancer: a multitask analysis using radiological-clinical nomogram on MRI.

We aimed to differentiate endometrial cancer (EC) between TP53mutation (P53abn) and Non-P53abn subtypes using radiological-clinical nomogram on EC body volume MRI. We retrospectively recruited 227 patients with pathologically proven EC from our institution. All these patients have undergone molecular pathology diagnosis based on the Cancer Genome Atlas. Clinical characteristics and histological diagnosis were recorded from the hospital information system. Radiomics features were extracted from online Pyradiomics processors. The diagnostic performance across different acquisition protocols was calculated and compared. The radiological-clinical nomogram was established to determine the nonendometrioid, high-risk, and P53abn EC group. The best MRI sequence for differentiation P53abn from the non-P53abn group was contrast-enhanced T1WI (test AUC: 0.8). The best MRI sequence both for differentiation endometrioid cancer from nonendometrioid cancer and high-risk from low- and intermediate-risk groups was apparent diffusion coefficient map (test AUC: 0.665 and 0.690). For all 3 tasks, the combined model incorporating all the best discriminative features from each sequence yielded the best performance. The combined model achieved an AUC of 0.845 in the testing cohorts for P53abn cancer identification. The MR-based radiomics diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC: 0.834 vs 0.682). In the present study, the diagnostic model based on the combination of both radiomics and clinical features yielded a higher performance in differentiating nonendometrioid and P53abn cancer from other EC molecular subgroups, which might help design a tailed treatment, especially for patients with high-risk EC. (1) The contrast-enhanced T1WI was the best MRI sequence for differentiation P53abn from the non-P53abn group (test AUC: 0.8). (2) The radiomics-based diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC: 0.834 vs 0.682). (3) The proposed model derived from multi-parametric MRI images achieved a higher accuracy in P53abn EC identification (AUC: 0.845).

Presacral Schwannoma Resembling Lymph Node Metastasis in a Patient with Locally Advanced Prostate Cancer

Introduction: SSchwannomas originating in the pelvic region are rare and present diagnostic challenges when they coexist with malignancies. Case Presentation: A 58-year-old man with urinary frequency and an elevated prostate-specific antigen level underwent preoperative imaging. T2-weighted magnetic resonance imaging (MRI) of the prostate identified an irregular hypointense lesion with extracapsular invasion, as well as a presacral nodule resembling lymph node metastasis. The presacral nodule exhibited an intermediate signal intensity on the apparent diffusion coefficient map that was derived from diffusion-weighted images. Dynamic contrast-enhanced MRI revealed persistent enhancement in the presacral node, while the prostate tumor exhibited strong early enhancement followed by washout. Pathological and laparoscopic findings confirmed the coexistence of locally advanced prostate cancer and pelvic schwannoma. Conclusions: MRI is useful not only in detecting prostate cancer but also in differentiating lymph node metastases from schwannoma.

Abstract 6223: Personalized MRI-informed forecasting of prostate cancer progression during active surveillance

Abstract Low and intermediate-risk prostate cancer (PCa) patients are eligible for active surveillance (AS), whereby treatment is delayed until progression to higher-risk disease is detected via longitudinal multiparametric magnetic resonance imaging (mpMRI) scans, biopsies, and prostate specific antigen (PSA) tests. However, AS relies on a population-based observational paradigm in which standard testing frequencies may not match the patient’s tumor dynamics, resulting in a delayed diagnosis of progressive disease. To address these issues, we propose to advance AS towards a patient-specific predictive paradigm by leveraging computational tumor forecasts obtained with a biomechanistic model informed by mpMRI and clinical data collected during standard AS. Here, we present a retrospective study with n = 16 PCa cases. All patients had three mpMRI scans obtained over 2.6 to 5.6 years during AS. Our biomechanistic model describes PCa growth in terms of the dynamics of tumor cell density as a combination of tumor cell mobility and net proliferation. The model is defined on the patient’s prostate geometry, which is segmented on T2-weighted MRI data. Tumor cell density estimates are derived from apparent diffusion coefficient (ADC) maps obtained from diffusion-weighted MRI data. To facilitate modeling, the longitudinal imaging datasets are non-rigidly co-registered for each patient. We initialize the model with the tumor cell density map obtained from the ADC map of the first scan. The model is parameterized by minimizing the model-data mismatch in tumor cell density at the second scan date, and then we perform a tumor forecast up to the third scan date. Finally, we build a logistic classifier from a panel of model-based biomarkers calculated from the personalized model forecasts (e.g., tumor volume, total proliferation activity) at the times of histopathological assessment (i.e., biopsy, surgery) to classify tumors as low risk (Gleason score 3+3, n = 16) or intermediate-high risk (Gleason score ≥ 3+4, n = 15). We obtained a concordance correlation coefficient (CCC) for tumor volume of 0.89 at both model calibration and forecasting horizon. The spatial fit of tumor cell density yielded a median Dice score and CCC of 0.80 and 0.59 at the second mpMRI date, and of 0.76 and 0.60 at the third mpMRI date, respectively. The logistic classifier of PCa risk yielded an area under the ROC curve of 0.90 and operates at optimal sensitivity, specificity, and accuracy of 86.7%, 93.8%, and 90.3%, respectively. The time trajectories of PCa risk obtained from the temporal predictions of the model-based biomarkers enabled identification of PCa progression by more than 1 year. Thus, while further development and validation over larger cohorts are required, these results suggest that our forecasting technology is a promising tool to predict PCa progression in AS and, hence, optimally guide monitoring and treatment decisions during AS. Citation Format: Guillermo Lorenzo Gomez, Chengyue Wu, Joshua P. Yung, John F. Ward, Hector Gomez, Alessandro Reali, Thomas E. Yankeelov, Aradhana M. Venkatesan, Thomas J. Hughes. Personalized MRI-informed forecasting of prostate cancer progression during active surveillance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6223.

Enhancing Diagnostic Precision: Evaluation of Preprocessing Filters in Simple Diffusion Kurtosis Imaging for Head and Neck Tumors.

Background: Our initial clinical study using simple diffusion kurtosis imaging (SDI), which simultaneously produces a diffusion kurtosis image (DKI) and an apparent diffusion coefficient map, confirmed the usefulness of SDI for tumor diagnosis. However, the obtained DKI had noticeable variability in the mean kurtosis (MK) values, which is inherent to SDI. We aimed to improve this variability in SDI by preprocessing with three different filters (Gaussian [G], median [M], and nonlocal mean) of the diffusion-weighted images used for SDI. Methods: The usefulness of filter parameters for diagnosis was examined in basic and clinical studies involving 13 patients with head and neck tumors. Results: The filter parameters, which did not change the median MK value, but reduced the variability and significantly homogenized the MK values in tumor and normal tissues in both basic and clinical studies, were identified. In the receiver operating characteristic curve analysis for distinguishing tumors from normal tissues using MK values, the area under curve values significantly improved from 0.627 without filters to 0.641 with G (σ = 0.5) and 0.638 with M (radius = 0.5). Conclusions: Thus, image pretreatment with G and M for SDI was shown to be useful for improving tumor diagnosis in clinical practice.

Prediction of pathological complete response in locally advanced head and neck squamous cell carcinoma treated with neoadjuvant chemo-immunotherapy using volumetric multisequence MRI histogram analysis.

This study aimed to develop a multisequence MRI-based volumetric histogram metrics model for predicting pathological complete response (pCR) in advanced head and neck squamous cell carcinoma (HNSCC) patients undergoing neoadjuvant chemo-immunotherapy (NCIT) and compare its predictive performance with AJCC staging and RECIST 1.1 criteria. Twenty-four patients with locally advanced HNSCC from a prospective phase II trial were enrolled for analysis. All patients underwent pre- and post-NCIT MRI examinations from which whole-tumor histogram features were extracted, including T1WI, T2WI, enhanced T1WI (T1Gd), diffusion-weighted imaging (DWI) sequences, and their corresponding apparent diffusion coefficient (ADC) maps. The pathological results divided the patients into pathological complete response (pCR) and non-pCR (N-pCR) groups. Delta features were calculated as the percentage change in histogram features from pre- to post-treatment. After data reduction and feature selection, logistic regression was used to build prediction models. ROC analysis was performed to assess the diagnostic performance. Eleven of 24 patients achieved pCR. Pre_T2_original_firstorder_Minimum, Post_ADC_original_firstorder_MeanAbsoluteDeviation, and Delta_T1Gd_original_firstorder_Skewness were associated with achieving pCR after NCIT. The Combined_Model demonstrated the best predictive performance (AUC 0.95), outperforming AJCC staging (AUC 0.52) and RECIST 1.1 (AUC 0.72). The Pre_Model (AUC 0.83) or Post-Model (AUC 0.83) had a better predictive ability than AJCC staging. Multisequence MRI-based volumetric histogram analysis can non-invasively predict the pCR status of HNSCC patients undergoing NCIT. The use of histogram features extracted from pre- and post-treatment MRI exhibits promising predictive performance and offers a novel quantitative assessment method for evaluating pCR in HNSCC patients receiving NCIT.

Repeatability and reproducibility of prostate apparent diffusion coefficient values on a 1.5 T magnetic resonance linear accelerator

Background and PurposeIntegrated magnetic resonance linear accelerator (MR-Linac) systems offer potential for biologically based adaptive radiation therapy using apparent diffusion coefficient (ADC). Accurate tracking of longitudinal ADC changes is key to establishing ADC-driven dose adaptation. Here, we report repeatability and reproducibility of intraprostatic ADC using deformable image registration (DIR) to correct for inter-fraction prostate changes. Materials and MethodsThe study included within-fraction repeat ADC measurements for three consecutive fractions for 20 patients with prostate cancer treated on a 1.5 T MR-Linac. We deformably registered successive fraction T2-weighted images and applied the deformation vector field to corresponding ADC maps to align to fraction 2. We delineated gross tumour volume (GTV), peripheral zone (PZ) and prostate clinical target volume (CTV) regions-of-interest (ROIs) on T2-weighted MRI and copied to ADC maps. We computed intraclass correlation coefficients (ICC) and percent repeatability coefficient (%RC) to determine within-fraction repeatability and between-fraction reproducibility for individual voxels, mean and 10th percentile ADC values per ROI. ResultsThe ICC between repeats and fractions was excellent for mean and 10th percentile ADC in all ROIs (ICC > 0.86), and moderate repeatability and reproducibility existed for individual voxels (ICC > 0.542). Similarly, low %RC within-fraction (4.2–17.9 %) mean and 10th percentile ADC existed, with greater %RC between fractions (10.2–36.8 %). Higher %RC existed for individual voxel within-fraction (21.7–30.6 %) and between-fraction (32.1–34.5 %) ADC. ConclusionsResults suggest excellent ADC repeatability and reproducibility in clinically relevant ROIs using DIR to correct between-fraction anatomical changes. We established the precision of voxel-level ADC tracking for future biologically based adaptation implementation.

Multispecies comparative prostate anatomy by imaging: Implications for experimental models of prostatic disease.

There is an increasing interest in using preclinical models for development and assessment of medical devices and imaging techniques for prostatic disease care. Still, a comprehensive assessment of the prostate's radiological anatomy in primary preclinical models such as dogs, rabbits, and mice utilizing human anatomy as a reference point remains necessary with no optimal model for each purpose being clearly defined in the literature. Therefore, this study compares the anatomical characteristics of different animal models to the human prostatic gland from the imaging perspective. We imaged five Beagle laboratory dogs, five New Zealand White rabbits, and five mice, all sexually mature males, under Institutional Animal Care and Use Committee (IACUC) approval. Ultrasonography (US) was performed using the Vevo® F2 for mice (57 MHz probe). Rabbits and dogs were imaged using the Siemens® Acuson S3000 (17 MHz probe) and endocavitary (8 MHz) probes, respectively. Magnetic resonance imaging (MRI) was also conducted with a 7T scanner in mice and 3T scanner in rabbits and dogs. Canine transrectal US emerged as the optimal method for US imaging, depicting a morphologically similar gland to humans but lacking echoic zonal differentiation. MRI findings in canines indicated a homogeneously structured gland similar to the human peripheral zone on T2-weighted images (T2W) and apparent diffusion coefficient (ADC). In rabbits, US imaging faced challenges due to the pubic symphysis, whereas MRI effectively visualized all structures with the prostate presenting a similar aspect to the human peripheral gland on T2W and ADC maps. Murine prostate assessment revealed poor visualization of the prostate glands in ultrasound due to its small size, while 7T MRI delineated the distinct prostates and its lobes, with the lateral and dorsal prostate resembling the peripheral zone and the anterior prostate the central zone of the human gland. Dogs stand out as superior models for advanced preclinical studies in prostatic disease research. However, mice present as a good model for early stage studies and rabbits are a cost-effective alternative and serve as valuable tools in specific research domains when canine research is not feasible.

Magnetic resonance imaging-based radiomics analysis of the differential diagnosis of ovarian clear cell carcinoma and endometrioid carcinoma: a retrospective study.

To retrospectively evaluate the diagnostic potential of magnetic resonance imaging (MRI)-based features and radiomics analysis (RA)-based features for discriminating ovarian clear cell carcinoma (CCC) from endometrioid carcinoma (EC). Thirty-five patients with 40 ECs and 42 patients with 43 CCCs who underwent pretherapeutic MRI examinations between 2011 and 2022 were enrolled. MRI-based features of the two groups were compared. RA-based features were extracted from the whole tumor volume on T2-weighted images (T2WI), contrast-enhanced T1-weighted images (cT1WI), and apparent diffusion coefficient (ADC) maps. The least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation method was performed to select features. Logistic regression analysis was conducted to construct the discriminating models. Receiver operating characteristic curve (ROC) analyses were performed to predict CCC. Four features with the highest absolute value of the LASSO algorithm were selected for theMRI-based, RA-based, and combined models: the ADC value, absence of thickening of the uterine endometrium, absence of peritoneal dissemination, and growth pattern of the solid component for the MRI-based model; Gray-Level Run Length Matrix (GLRLM) Long Run Low Gray-Level Emphasis (LRLGLE) on T2WI, spherical disproportion and Gray-Level Size Zone Matrix (GLSZM), Large Zone High Gray-Level Emphasis (LZHGE) on cT1WI, and GLSZM Normalized Gray-Level Nonuniformity (NGLN) on ADC map for the RA-based model; and the ADC value, spherical disproportion and GLSZM_LZHGE on cT1WI, and GLSZM_NGLN on ADC map for the combined model. Area under the ROC curves of those models were 0.895, 0.910, and 0.956. The diagnostic performance of the combined model was significantly superior (p = 0.02) to that of the MRI-based model. No significant differences were observed between the combined and RA-based models. Conventional MRI-based analysis can effectively distinguish CCC from EC. The combination of RA-based features with MRI-based features may assist in differentiating between the two diseases.

Evaluation of Uterine Carcinosarcoma and Uterine Endometrial Carcinoma Using Magnetic Resonance Imaging Findings and Texture Features.

Aim This study aimed to evaluate the diagnostic feasibility of magnetic resonance imaging (MRI) findings and texture features (TFs) for differentiating uterine endometrial carcinoma from uterine carcinosarcoma. Methods This retrospective study included 102 patients who were histopathologically diagnosed after surgery with uterine endometrial carcinoma (n=68) or uterine carcinosarcoma (n=34) between January 2008 and December 2021. We assessed conventional MRI findings and measurements (cMRFMs) and TFs on T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) map, as well as their combinations, in differentiating between uterine endometrial carcinoma and uterine carcinosarcoma. The least absolute shrinkage and selection operator (LASSO) was used to select three features with the highest absolute value of the LASSO regression coefficient for each model and construct a discriminative model. Binary logistic regression analysis was used to analyze the disease models and conduct receiver operating characteristic analyses on the cMRFMs, T2WI-TFs, ADC-TFs, and their combined model to compare the two diseases. Results A total of four models were constructed from each of the three selected features. The area under the curve (AUC) of the discriminative model using these features was 0.772, 0.878, 0.748, and 0.915 for the cMRFMs, T2WI-TFs, ADC-TFs, and a combined model of cMRFMs and TFs, respectively. The combined model showed a higher AUC than the other models, with a high diagnostic performance (AUC=0.915). Conclusion A combined model using cMRFMs and TFs might be helpful for the differential diagnosis of uterine endometrial carcinoma and uterine carcinosarcoma.