The evaluation of prostate lesions with IVIM DWI and MR perfusion parameters at 3T MRI

Improving Accuracy of Intravoxel Incoherent Motion Reconstruction using Kalman Filter in Combination with Neural Networks: A Simulation Study

Background

The intravoxel Incoherent Motion (IVIM) model extracts perfusion map and diffusion coefficient map using diffusion-weighted imaging. The main limitation of this model is inaccuracy in the presence of noise.

Objective

This study aims to improve the accuracy of IVIM output parameters.

Material and Methods

In this simulated and analytical study, the Kalman filter is applied to reject artifact and measurement noise. The proposed method purifies the diffusion coefficient from blood motion and noise, and then an artificial neural network is deployed in estimating perfusion parameters.

Results

Based on the T-test results, however, the estimated parameters of the conventional method were significantly different from actual values, those of the proposed method were not substantially different from actual. The accuracy of f and D* also was improved by using Artificial Neural Network (ANN) and their bias was minimized to 4% and 12%, respectively.

Conclusion

The proposed method outperforms the conventional method and is a promising technique, leading to reproducible and valid maps of D, f, and D*.

Magnetic Resonance Perfusion Imaging of Prostate

Magnetic resonance (MR) perfusion imaging, both with and without exogenous contrast agents, has the potential to assess tissue perfusion and vascularity in prostate cancer. Dynamic contrast-enhanced (DCE) MRI is an important element of the clinical non-invasive multiparametric MRI, which can be used to differentiate benign from malignant lesions, to stage tumors, and to monitor response to therapy. The arterial spin labeled (ASL) and intravoxel incoherent motion (IVIM) diffusion-weighted MRI have the advantage of quantitative perfusion measurements without the concerns of gadolinium-based contrast agent safety and retention issues. The adoption of these non-contrast techniques in clinical practice needs more research and clinical evaluation.

Intravoxel Incoherent Motion Quantification Dependent on Measurement SNR and Tissue Perfusion: A Simulation Study

Background

The intravoxel incoherent motion (IVIM) model extracts both functional and structural information of a tissue using motion-sensitizing gradients.

Objective

The Objective of the present work is to investigate the impact of signal to noise ratio (SNR) and physiologic conditions on the validity of IVIM parameters.

Material and Methods

This study is a simulation study, modeling IVIM at a voxel, and also done 10,000 times for every single simulation. Complex noises with various standard deviations were added to signal in-silico to investigate SNR effects on output validity. Besides, some blood perfusion situations for different tissues were considered based on their physiological range to explore the impacts of blood fraction at each voxel on the validity of the IVIM outputs. Coefficient variation (CV) and bias of the estimations were computed to assess the validity of the IVIM parameters.

Results

This study has shown that the validity of IVIM output parameters highly depends on measurement SNR and physiologic characteristics of the studied organ.

Conclusion

IVIM imaging could be useful if imaging parameters are correctly selected for each specific organ, considering hardware limitations.

A Review on the Use of Imaging Biomarkers in Oncology Clinical Trials: Quality Assurance Strategies for Technical Validation

Imaging biomarkers (IBs) have been proposed in medical literature that exploit images in a quantitative way, going beyond the visual assessment by an imaging physician. These IBs can be used in the diagnosis, prognosis, and response assessment of several pathologies and are very often used for patient management pathways. In this respect, IBs to be used in clinical practice and clinical trials have a requirement to be precise, accurate, and reproducible. Due to limitations in imaging technology, an error can be associated with their value when considering the entire imaging chain, from data acquisition to data reconstruction and subsequent analysis. From this point of view, the use of IBs in clinical trials requires a broadening of the concept of quality assurance and this can be a challenge for the responsible medical physics experts (MPEs). Within this manuscript, we describe the concept of an IB, examine some examples of IBs currently employed in clinical practice/clinical trials and analyze the procedure that should be carried out to achieve better accuracy and reproducibility in their use. We anticipate that this narrative review, written by the components of the EFOMP working group on "the role of the MPEs in clinical trials"-imaging sub-group, can represent a valid reference material for MPEs approaching the subject.

Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Parameters Could Predict International Society of Urological Pathology Risk Groups of Prostate Cancers on Radical Prostatectomy

BACKGROUND

The International Society of Urological Pathology (ISUP) grade and positive surgical margins (PSMs) after radical prostatectomy (RP) may reflect the prognosis of prostate cancer (PCa) patients. This study aimed to investigate whether DCE-MRI parameters (i.e., Ktrans, kep, and IAUC) could predict ISUP grade and PSMs after RP.

METHOD

Forty-five PCa patients underwent preoperative DCE-MRI. The clinical characteristics and DCE-MRI parameters of the 45 patients were compared between the low- and high-risk (i.e., ISUP grades III-V) groups and between patients with or without PSMs after RP. Multivariate logistic regression analysis was used to identify the significant predictors of placement in the high-risk group and PSMs.

RESULTS

The DCE parameter Ktrans-max was significantly higher in the high-risk group than in the low-risk group (p = 0.028) and was also a significant predictor of placement in the high-risk group (odds ratio [OR] = 1.032, 95% confidence interval [CI] = 1.005-1.060, p = 0.021). Patients with PSMs had significantly higher prostate-specific antigen (PSA) titers, positive biopsy core percentages, Ktrans-max, kep-median, and kep-max than others (all p < 0.05). Of these, positive biopsy core percentage (OR = 1.035, 95% CI = 1.003-1.068, p = 0.032) and kep-max (OR = 1.078, 95% CI = 1.012-1.148, p = 0.020) were significant predictors of PSMs.

CONCLUSION

Preoperative DCE-MRI parameters, specifically Ktrans-max and kep-max, could potentially serve as preoperative imaging biomarkers for postoperative PCa prognosis based on their predictability of PCa risk group and PSM on RP, respectively.

Sub-differentiation of PI-RADS 3 lesions in TZ by advanced diffusion-weighted imaging to aid the biopsy decision process

Background

Performing biopsy for intermediate lesions with PI-RADS 3 has always been controversial. Moreover, it is difficult to differentiate prostate cancer (PCa) and benign prostatic hyperplasia (BPH) nodules in PI-RADS 3 lesions by conventional scans, especially for transition zone (TZ) lesions. The purpose of this study is sub-differentiation of transition zone (TZ) PI-RADS 3 lesions using intravoxel incoherent motion (IVIM), stretched exponential model, and diffusion kurtosis imaging (DKI) to aid the biopsy decision process.

Methods

A total of 198 TZ PI-RADS 3 lesions were included. 149 lesions were BPH, while 49 lesions were PCa, including 37 non-clinical significant PCa (non-csPCa) lesions and 12 clinical significant PCa (csPCa) lesions. Binary logistic regression analysis was used to examine which parameters could predict PCa in TZ PI-RADS 3 lesions. The ROC curve was used to test diagnostic efficiency in distinguishing PCa from TZ PI-RADS 3 lesions, while one-way ANOVA analysis was used to examine which parameters were statistically significant among BPH, non-csPCa and csPCa.

Results

The logistic model was statistically significant (χ2 = 181.410, p<0.001) and could correctly classify 89.39% of the subjects. Parameters of fractional anisotropy (FA) (p=0.004), mean diffusion (MD) (p=0.005), mean kurtosis (MK) (p=0.015), diffusion coefficient (D) (p=0.001), and distribute diffusion coefficient (DDC) (p=0.038) were statistically significant in the model. ROC analysis showed that AUC was 0.9197 (CI 95%: 0.8736-0.9659). Sensitivity, specificity, positive predictive value and negative predictive value were 92.1%, 80.4%, 93.9% and 75.5%, respectively. FA and MK of csPCa were higher than those of non-csPCa (all p<0.05), while MD, ADC, D, and DDC of csPCa were lower than those of non-csPCa (all p<0.05).

Conclusion

FA, MD, MK, D, and DDC can predict PCa in TZ PI-RADS 3 lesions and inform the decision-making process of whether or not to perform a biopsy. Moreover, FA, MD, MK, D, DDC, and ADC may have ability to identify csPCa and non-csPCa in TZ PI-RADS 3 lesions.

A preliminary study on the diagnostic value of PSADR, DPC and TSRP in the distinction of prostatitis and prostate cancer

BACKGROUND

The purpose of this study was to investigate the ability of differential diagnosis of prostate specific antigen decline rate (PSADR) per week, degree of prostatic collapse (DPC) and tissue signal rate of prostate (TSRP) between prostatitis and prostate cancer.

METHODS

The clinical data of 92 patients [prostate specific antigen (PSA) > 10 ng/mL] who underwent prostate biopsy in the Department of Urology, the Second Affiliated Hospital of Xi 'an Jiaotong University from May 2017 to April 2020 were reviewed retrospectively. They were divided into two groups, prostatitis group (n = 42) and prostate cancer (PCa) group (n = 50), according to pathological results. Parameters, like patient characteristics, PSADR, DPC, TSRP and infectious indicators, were compared and analyzed by t test or non-parametric test to identify if there were significant differences. The thresholds of parameters were determined by the receiver operating characteristic curve (ROC), and the data were analyzed to investigate the diagnostic value in distinguishing of prostatitis and prostate cancer.

RESULTS

There were statistical differences in age, PSADR, DPC, TSRP, neutrophil percentage in serum, white blood cell (WBC) in urine and prostate volume between prostatitis group and PCa group (P < 0.001, < 0.001, = 0.001, 0.001, 0.024, 0.014, < 0.001 respectively). There was no statistical difference in serum WBC count, serum neutrophil count, monocyte percentage and urine bacterial count between two groups (P = 0.089, 0.087, 0.248, 0.119, respectively). Determined by ROC curve, when the thresholds of PSADR per week as 3.175 ng/mL/week, DPC as 1.113, TSRP as 2.708 were cutoffs of distinguishing prostatitis and prostate cancer. When combining these three indexes to diagnose, the accuracy rate of diagnosis of prostatitis was 78.85%, the accuracy rate of diagnosis of prostate cancer was 97.50%. Univariate analysis suggested that PSADR, DPC and TSRP played an important role in differentiating prostate cancer from prostatitis (P < 0.05), multivariate analysis suggested PSADR > 3.175 might be good indicators when distinguishing prostate disease with prostatitis (OR = 14.305, 95%CI = 3.779 ~ 54.147), while DPC > 1.113 and TSRP > 2.708 might be associated with a higher risk of prostate cancer (OR = 0.151, 95%CI = 0.039 ~ 0.588; OR = 0.012, 95%CI = 0.005 ~ 0.524, respectively).

CONCLUSION

The combination of PSADR per week, DPC, and TSRP might be helpful to distinguish prostate cancer and prostatitis, and can reduce unnecessary invasive and histological procedure.

Dynamic contrast-enhanced (DCE) imaging: state of the art and applications in whole-body imaging

Dynamic contrast-enhanced (DCE) imaging is a non-invasive technique used for the evaluation of tissue vascularity features through imaging series acquisition after contrast medium administration. Over the years, the study technique and protocols have evolved, seeing a growing application of this method across different imaging modalities for the study of almost all body districts. The main and most consolidated current applications concern MRI imaging for the study of tumors, but an increasing number of studies are evaluating the use of this technique also for inflammatory pathologies and functional studies. Furthermore, the recent advent of artificial intelligence techniques is opening up a vast scenario for the analysis of quantitative information deriving from DCE. The purpose of this article is to provide a comprehensive update on the techniques, protocols, and clinical applications - both established and emerging - of DCE in whole-body imaging.

Delta radiomics: a systematic review

BACKGROUND

Radiomics can provide quantitative features from medical imaging that can be correlated with various biological features and clinical endpoints. Delta radiomics, on the other hand, consists in the analysis of feature variation at different acquisition time points, usually before and after therapy. The aim of this study was to provide a systematic review of the different delta radiomics approaches.

METHODS

Eligible articles were searched in Embase, PubMed, and ScienceDirect using a search string that included free text and/or Medical Subject Headings (MeSH) with three key search terms: "radiomics", "texture", and "delta". Studies were analysed using QUADAS-2 and the RQS tool.

RESULTS

Forty-eight studies were finally included. The studies were divided into preclinical/methodological (five studies, 10.4%); rectal cancer (six studies, 12.5%); lung cancer (twelve studies, 25%); sarcoma (five studies, 10.4%); prostate cancer (three studies, 6.3%), head and neck cancer (six studies, 12.5%); gastrointestinal malignancies excluding rectum (seven studies, 14.6%), and other disease sites (four studies, 8.3%). The median RQS of all studies was 25% (mean 21% ± 12%), with 13 studies (30.2%) achieving a quality score < 10% and 22 studies (51.2%) < 25%.

CONCLUSIONS

Delta radiomics shows potential benefit for several clinical endpoints in oncology (differential diagnosis, prognosis and prediction of treatment response, and evaluation of side effects). Nevertheless, the studies included in this systematic review suffer from the bias of overall low quality, so that the conclusions are currently heterogeneous, not robust, and not replicable. Further research with prospective and multicentre studies is needed for the clinical validation of delta radiomics approaches.

Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma

This article provides an overview of diagnostic evaluation and ablation treatment assessment in Hepatocellular Carcinoma (HCC). Only studies, in the English language from January 2010 to January 202, evaluating the diagnostic tools and assessment of ablative therapies in HCC patients were included. We found 173 clinical studies that satisfied the inclusion criteria.HCC may be noninvasively diagnosed by imaging findings. Multiphase contrast-enhanced imaging is necessary to assess HCC. Intravenous extracellular contrast agents are used for CT, while the agents used for MRI may be extracellular or hepatobiliary. Both gadoxetate disodium and gadobenate dimeglumine may be used in hepatobiliary phase imaging. For treatment-naive patients undergoing CT, unenhanced imaging is optional; however, it is required in the post treatment setting for CT and all MRI studies. Late arterial phase is strongly preferred over early arterial phase. The choice of modality (CT, US/CEUS or MRI) and MRI contrast agent (extracelllar or hepatobiliary) depends on patient, institutional, and regional factors. MRI allows to link morfological and functional data in the HCC evaluation. Also, Radiomics is an emerging field in the assessment of HCC patients.Postablation imaging is necessary to assess the treatment results, to monitor evolution of the ablated tissue over time, and to evaluate for complications. Post- thermal treatments, imaging should be performed at regularly scheduled intervals to assess treatment response and to evaluate for new lesions and potential complications.

The Assessment of Prostate Cancer Aggressiveness Using a Combination of Quantitative Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Objective

To explore the value of combining dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative parameters with apparent diffusion coefficient (ADC) values in the diagnosis of prostate cancer.

Methods

The clinical data of 146 patients with prostate lesions, including 87 patients with prostate cancer (PCa) and 59 with benign prostatic hyperplasia (BPH), were collected. After DCE-MRI and diffusion-weighted imaging (DWI) prostate scans, the magnitude of the DCE-MRI transfer constant (K^trans), rate constant (k_ep), the volume of the extravascular extracellular space (v_e), and the ADC between the groups were compared, and the correlations between the DCE-MRI parameters and Gleason scores were analyzed. The diagnostic efficacy of these quantitative parameters was assessed by the area under the receiver operating characteristic (ROC) curve.

Results

The DCE-MRI parameters K^trans and k_ep were significantly greater in the PCa group than in the BPH group (p < 0.05). The ROC curve showed the area under the K^trans, k_ep, and ADC curves to be 0.665, 0.658, and 0.782, respectively. When all three quantitative indicators were combined, the area under the ROC curve was 0.904, with sensitivity and specificity rates of 83.6% and 93.7%, respectively. The Gleason scores were positively correlated with the K^trans, k_ep, and v_e (r = 0.39, 0.572, 0.30, respectively; p < 0.05) and negatively correlated with the ADC (r = –0.525; p < 0.05).

Conclusion

The DCE-MRI quantitative parameters K^trans and k_ep, as well as the ADC value, provided effective references for the differential diagnosis of PCa and BPH, as well as more precise and reliable quantitative parameters for grading the aggressiveness of PCa.

Characterisation of prostate cancer using texture analysis for diagnostic and prognostic monitoring

Automated classification of significant prostate cancer (PCa) using MRI plays a potential role in assisting in clinical decision-making. Multiparametric MRI using a machine-aided approach is a better step to improve the overall accuracy of diagnosis of PCa. The objective of this study was to develop and validate a framework for differentiating Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) grades (grade 2 to grade 5) of PCa using texture features and machine learning (ML) methods with diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC). The study cohort included an MRI dataset of 59 patients with clinically proven PCa. Regions of interest (ROIs) for a total of 435 lesions were delineated from the segmented peripheral zones of DWI and ADC. Six texture methods comprising 98 texture features in total (49 each of DWI and ADC) were extracted from lesion ROIs. Random forest (RF) and correlation-based feature selection methods were applied on feature vectors to select the best features for classification. Two ML classifiers, support vector machine (SVM) and K-nearest neighbour, were used and validated by 10-fold cross-validation. The proposed framework achieved high diagnostic performance with a sensitivity of 85.25% ± 3.84%, specificity of 95.71% ± 1.96%, accuracy of 84.90% ± 3.37% and area under the receiver-operating characteristic curve of 0.98 for PI-RADS v2 grades (2 to 5) classification using the RF feature selection method and Gaussian SVM classifier with combined features of DWI + ADC. The proposed computer-assisted framework can distinguish between PCa lesions with different aggressiveness based on PI-RADS v2 standards using texture analysis to improve the efficiency of PCa diagnostic performance.

Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment

Magnetic resonance imaging (MRI) is increasingly used in the management of prostate cancer (PCa). Quantitative MRI (qMRI) parameters, derived from multi-parametric MRI, provide indirect measures of tumour characteristics such as cellularity, angiogenesis and hypoxia. Using Artificial Intelligence (AI), relevant information and patterns can be efficiently identified in these complex data to develop quantitative imaging biomarkers (QIBs) of tumour function and biology. Such QIBs have already demonstrated potential in the diagnosis and staging of PCa. In this review, we explore the role of these QIBs in monitoring treatment response during and after PCa radiotherapy (RT). Recurrence of PCa after RT is not uncommon, and early detection prior to development of metastases provides an opportunity for salvage treatments with curative intent. However, the current method of monitoring treatment response using prostate-specific antigen levels lacks specificity. QIBs, derived from qMRI and developed using AI techniques, can be used to monitor biological changes post-RT providing the potential for accurate and early diagnosis of recurrent disease.

Apparent diffusion coefficient magnetic resonance imaging (ADC-MRI) in the axillary breast cancer lymph node metastasis detection: a narrative review

The presence of axillary lymph nodes metastases in breast cancer is the most significant prognostic factor, with a great impact on morbidity, disease-related survival and management of oncological therapies; for this reason, adequate imaging evaluation is strictly necessary. Physical examination is not enough sensitive to assess breast cancer nodal status; axillary ultrasonography (US) is commonly used to detect suspected or occult nodal metastasis, providing exclusively morphological evaluation, with low sensitivity and positive predictive value. Currently, sentinel lymph node biopsy (SLNB) and/or axillary dissection are the milestone for the diagnostic assessment of axillary lymph node metastases, although its related morbidity. The impact of magnetic resonance imaging (MRI) in the detection of nodal metastases has been widely investigated, as it continues to represent the most promising imaging modality in the breast cancer management. In particular, diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values represent additional reliable non-contrast sequences, able to improve the diagnostic accuracy of breast cancer MRI evaluation. Several studies largely demonstrated the usefulness of implementing DWI/ADC MRI in the characterization of breast lesions. Herein, in the light of our clinical experience, we perform a review of the literature regarding the diagnostic performance and accuracy of ADC value as potential pre-operative tool to define metastatic involvement of nodal structures in breast cancer patients. For the purpose of this review, PubMed, Web of Science, and SCOPUS electronic databases were searched with different combinations of "axillary lymph node", "breast cancer", "MRI/ADC", "breast MRI" keywords. All original articles, reviews and metanalyses were included.

Biparametric (bp) and multiparametric (mp) magnetic resonance imaging (MRI) approach to prostate cancer disease: a narrative review of current debate on dynamic contrast enhancement

Prostate cancer is the most common malignancy in male population. Over the last few years, magnetic resonance imaging (MRI) has proved to be a robust clinical tool for identification and staging of clinically significant prostate cancer. Though suggestions by the European Society of Urogenital Radiology to use complete multiparametric (mp) T2-weighted/diffusion weighted imaging (DWI)/dynamic contrast enhancement (DCE) acquisition for all prostate MRI examinations, the real advantage of functional DCE remains a matter of debate. Recent studies demonstrate that biparametric (bp) and mp approaches have similar accuracy, but controversial evidences remain, and the specific potential benefits of contrast medium administration are still poorly discussed in literature. The bp approach is in fact sufficient in most cases to adequately identify a negative test, or to accurately define the degree of aggressiveness of a lesion, especially if larger or with major characteristics of malignancy. This feature would give the DCE a secondary role, probably limited to a second evaluation of the lesion location, for detecting small cancer or in case of controversy. However, DCE has proved to increase the sensitivity of prostate MRI, though a less specificity. Therefore, an appropriate decision algorithm is needed to standardize the MRI approach. Aim of this review study was to provide a schematic description of bpMRI and mpMRI approaches in the study of prostatic anatomy, focusing on comparative validity and current DCE application. Additional theoretical considerations on prostate MRI are provided.

Multiparametric MRI in the management of prostate cancer: an update-a narrative review

The growing interest in multiparametric MRI is leading to important changes in the diagnostic process of prostate cancer. MRI-targeted biopsy is likely to become a standard for the diagnosis of prostate cancer in the next years. Despite it is well known that MRI has no role as a staging technique, it is clear that multiparametric MRI may be of help in active surveillance protocols. Noteworthy, MRI in active surveillance is not recommended, but a proper understanding of its potential may be of help in achieving the goals of a delayed treatment strategy. Moreover, the development of minimally invasive techniques, like laparoscopic and robotic surgery, has led to greater expectations as regard to the functional outcomes of radical prostatectomy. Multiparametric MRI may play a role in planning surgical strategies, with the aim to provide the highest oncologic outcome with a minimal impact on the quality of life. We maintain that a proper anatomic knowledge of prostate lesions may allow the surgeon to achieve a better result in planning as well as in performing surgery and help the surgeon and the patient engage in a shared decision in planning a more effective strategy for prostate cancer control and treatment. This review highlights the advantages and the limitations of multiparametric MRI in prostate cancer diagnosis, in active surveillance and in planning surgery.

Pancreatic neuroendocrine tumours: spectrum of imaging findings

Pancreatic neuroendocrine tumours (pNETs) are rare and heterogeneous group of neoplasms presenting with a wide variety of symptoms and biological behaviour, from indolent to aggressive ones. pNETs are stratified into functional or non-functional, because of their ability to produce metabolically active hormones. pNETs can be an isolate phenomenon or a part of a hereditary syndrome like von Hippel-Lindau syndrome or neurofibromatosis-1. The incidence has increased in the last years, also because of the improvement of cross-sectional imaging. Computed tomography (CT), magnetic resonance imaging (MRI) and functional imaging are the mainstay imaging modalities used for tumour detection and disease extension assessment, due to easy availability and better contrast/spatial resolution. Radiological imaging plays a fundamental role in detection, characterization and surveillance of pNETs and is involved in almost every stage of patients' management. Moreover, with specific indications and techniques, interventional radiology can also play a role in therapeutic management. Surgery is the treatment of choice, consisting of either partial pancreatectomy or enucleation of the primary tumour. This article reviews the radiologic features of different pNETs as well as imaging mimics, in order to help radiologists to avoid potential pitfalls, to reach the correct diagnosis and to support the multidisciplinary team in establishing the right treatment.

Application of hierarchical clustering to multi-parametric MR in prostate: Differentiation of tumor and normal tissue with high accuracy

PURPOSE

Hierarchical clustering (HC), an unsupervised machine learning (ML) technique, was applied to multi-parametric MR (mp-MR) for prostate cancer (PCa). The aim of this study is to demonstrate HC can diagnose PCa in a straightforward interpretable way, in contrast to deep learning (DL) techniques.

METHODS

HC was constructed using mp-MR including intravoxel incoherent motion, diffusion kurtosis imaging, and dynamic contrast-enhanced MRI from 40 tumor and normal tissues in peripheral zone (PZ) and 23 tumor and normal tissues in transition zone (TZ). HC model was optimized by assessing the combinations of several dissimilarity and linkage methods. Goodness of HC model was validated by internal methods.

RESULTS

Accuracy for differentiating tumor and normal tissue by optimal HC model was 96.3% in PZ and 97.8% in TZ, comparable to current clinical standards. Relationship between input (DWI and permeability parameters) and output (tumor and normal tissue cluster) was shown by heat maps, consistent with literature.

CONCLUSION

HC can accurately differentiate PCa and normal tissue, comparable to state-of-the-art diffusion based parameters. Contrary to DL techniques, HC is an operator-independent ML technique producing results that can be interpreted such that the results can be knowledgeably judged.

Intravoxel Incoherent Motion Diffusion-Weighted Imaging Used to Detect Prostate Cancer and Stratify Tumor Grade: A Meta-Analysis

Objectives: Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) is a promising non-invasive imaging technique to detect and grade prostate cancer (PCa). However, the results regarding the diagnostic performance of IVIM-DWI in the characterization and classification of PCa have been inconsistent among published studies. This meta-analysis was performed to summarize the diagnostic performance of IVIM-DWI in the differential diagnosis of PCa from non-cancerous tissues and to stratify the tumor Gleason grades in PCa.

Materials and Methods: Studies concerning the differential diagnosis of prostate lesions using IVIM-DWI were systemically searched in PubMed, Embase, and Web of Science without time limitation. Review Manager 5.3 was used to calculate the standardized mean difference (SMD) and 95% confidence intervals of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (D^*), and perfusion fraction (f). Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as publication bias and heterogeneity. Fagan's nomogram was used to predict the post-test probabilities.

Results: Twenty studies with 854 patients confirmed with PCa were included. Most of the included studies showed a low to unclear risk of bias and low concerns regarding applicability. PCa showed a significantly lower ADC (SMD = −2.34; P < 0.001) and D values (SMD = −1.86; P < 0.001) and a higher D^* value (SMD = 0.29; P = 0.01) than non-cancerous tissues, but no difference was noted with the f value (SMD = −0.16; P = 0.50). Low-grade PCa showed higher ADC (SMD = 0.63; P < 0.001) and D values (SMD = 0.80; P < 0.001) than the high-grade lesions. ADC showed comparable diagnostic performance (sensitivity = 86%; specificity = 86%; AUC = 0.87) but higher post-test probabilities (60, 53, 36, and 36% for ADC, D, D^*, and f values, respectively) compared with the D (sensitivity = 82%; specificity = 82%; AUC = 0.85), D^* (sensitivity = 70%; specificity = 70%; AUC = 0.75), and f values (sensitivity = 73%; specificity = 68%; AUC = 0.76).

Conclusion: IVIM parameters are adequate to differentiate PCa from non-cancerous tissues with good diagnostic performance but are not superior to the ADC value. Diffusion coefficients can further stratify the tumor Gleason grades in PCa.

Comparative Study of Monoexponential, Intravoxel Incoherent Motion, Kurtosis, and IVIM-Kurtosis Models for the Diagnosis and Aggressiveness Assessment of Prostate Cancer

Objective: This study aimed to compare the potential of monoexponential model (MEM), intravoxel incoherent motion (IVIM) model, kurtosis model, and IVIM–kurtosis model in the diagnosis and aggressiveness assessment of prostate cancer (PCa).

Materials and Methods: Thirty-six patients were recruited. Diffusion-weighted images were acquired on a 3.0-T magnetic resonance imaging (MRI) system using 0 b values up to 2,000 s/mm² and analyzed using four models: MEM (ADC_MEM), IVIM (D_IVIM, D*_IVIM, f_IVIM), kurtosis (D_kurtosis, K_kurtosis), and IVIM–kurtosis (D_{IVIM−kurtosis}, D^*_{IVIM−kurtosis}, f_{IVIM−kurtosis}, D_{IVIM−kurtosis}) models. The values of these parameters were calculated and compared between PCa, benign prostatic hyperplasia (BPH), and prostatitis. Correlations between these parameters and the Gleason score (GS) of PCa were evaluated using the Pearson test.

Results: Forty-five lesions were studied, including 18 PCa, 12 prostatitis, and 15 BPH lesions. The ADC_MEM, D_IVIM, f_IVIM, D_kurtosis, and D_{IVIM−kurtosis} values were significantly lower and K_kurtosis and K_{IVIM−kurtosis} values were significantly higher in PCa compared with prostatitis and BPH. The area under the curve (AUC) of ADC_MEM showed significantly higher values than that of f_IVIM and K_{IVIM−kurtosis}, but no statistical differences were found between the other parameters. The D^*_{IVIM−kurtosis} value correlated negatively and f_{IVIM−kurtosis} and K_{IVIM−kurtosis} values correlated positively with the GS.

Conclusion: The MEM, IVIM, kurtosis, and IVIM–kurtosis models were all useful for the diagnosis of PCa, and the diagnostic efficacy seemed to be similar. The IVIM–kurtosis model may be superior to the MEM, IVIM, and kurtosis models in the grading of PCa.

Perfusion-driven Intravoxel Incoherent Motion (IVIM) MRI in Oncology: Applications, Challenges, and Future Trends

Recent developments in MR hardware and software have allowed a surge of interest in intravoxel incoherent motion (IVIM) MRI in oncology. Beyond diffusion-weighted imaging (and the standard apparent diffusion coefficient mapping most commonly used clinically), IVIM provides information on tissue microcirculation without the need for contrast agents. In oncology, perfusion-driven IVIM MRI has already shown its potential for the differential diagnosis of malignant and benign tumors, as well as for detecting prognostic biomarkers and treatment monitoring. Current developments in IVIM data processing, and its use as a method of scanning patients who cannot receive contrast agents, are expected to increase further utilization. This paper reviews the current applications, challenges, and future trends of perfusion-driven IVIM in oncology.

Diffusion-Weighted Imaging in Oncology: An Update

To date, diffusion weighted imaging (DWI) is included in routine magnetic resonance imaging (MRI) protocols for several cancers. The real additive role of DWI lies in the "functional" information obtained by probing the free diffusivity of water molecules into intra and inter-cellular spaces that in tumors mainly depend on cellularity. Although DWI has not gained much space in some oncologic scenarios, this non-invasive tool is routinely used in clinical practice and still remains a hot research topic: it has been tested in almost all cancers to differentiate malignant from benign lesions, to distinguish different malignant histotypes or tumor grades, to predict and/or assess treatment responses, and to identify residual or recurrent tumors in follow-up examinations. In this review, we provide an up-to-date overview on the application of DWI in oncology.