New prostate MRI techniques and sequences

MRI-based virtual pathology of the prostate

Prostate cancer poses significant diagnostic challenges, with conventional methods like prostate-specific antigen (PSA) screening and transrectal ultrasound (TRUS)-guided biopsies often leading to overdiagnosis or miss clinically significant cancers. Multiparametric MRI (mpMRI) has emerged as a more reliable tool. However, it is limited by high inter-observer variability and radiologists missing up to 30% of clinically significant cancers. This article summarizes a few of these recent advancements in quantitative MRI techniques that look at the "Virtual Pathology" of the prostate with an aim to enhance prostate cancer detection and characterization. These techniques include T2 relaxation-based techniques such as luminal water imaging, diffusion based such as vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) and restriction spectrum imaging or combined relaxation-diffusion techniques such as hybrid multi-dimensional MRI (HM-MRI), time-dependent diffusion imaging, and diffusion-relaxation correlation spectrum imaging. These methods provide detailed insights into underlying prostate microstructure and tissue composition and have shown improved diagnostic accuracy over conventional MRI. These innovative MRI methods hold potential for augmenting mpMRI, reducing variability in diagnosis, and paving the way for MRI as a 'virtual histology' tool in prostate cancer diagnosis. However, they require further validation in larger multi-center clinical settings and rigorous in-depth radiological-pathology correlation are needed for broader implementation.

Four-quadrant vector mapping of hybrid multidimensional MRI data for the diagnosis of prostate cancer

PURPOSE

The interpretation of prostate multiparametric magnetic resonance imaging (MRI) is subjective in nature, and there is large inter-observer variability among radiologists and up to 30% of clinically significant cancers are missed. This has motivated the development of new MRI techniques and sequences, especially quantitative approaches to improve prostate cancer diagnosis. Using hybrid multidimensional MRI, apparent diffusion coefficient (ADC) and T2 have been shown to change as a function of echo time (TE) and b-values, and that this dependence is different for cancer and benign tissue, which can be exploited for prostate cancer diagnosis. The purpose of this study is to investigate whether four-quadrant vector mapping of hybrid multidimensional MRI (HM-MRI) data can be used to diagnose prostate cancer (PCa) and determine cancer aggressiveness.

METHODS

Twenty-one patients with confirmed PCa underwent preoperative MRI prior to radical prostatectomy. Axial HM-MRI were acquired with all combinations of TE = 47, 75, 100 ms and b-values of 0, 750, 1500 s/mm2 , resulting in a 3 × 3 data matrix associated with each voxel. Prostate Quadrant (PQ) mapping analysis represents HM-MRI data for each voxel as a color-coded vector in the four-quadrant space of HM-MRI parameters (a 2D matrix of signal values for each combination of b-value and TE) with associated amplitude and angle information representing the change in T2 and ADC as a function of b-value and TE, respectively.

RESULTS

Cancers have a higher PQ4 (22.50% ± 21.27%) and lower PQ2 (69.86% ± 28.24%) compared to benign tissue: peripheral, transition, and central zone (PQ4 = 0.13% ± 0.56%, 5.73% ± 15.07%, 2.66% ± 4.05%, and PQ2 = 98.51% ± 3.05%, 86.18% ± 21.75%, 93.38% ± 9.88%, respectively). Cancers have a higher vector angle (206.5 ± 41.8°) and amplitude (0.017 ± 0.013) compared to benign tissue. PQ metrics showed moderate correlation with Gleason score (|ρ| = 0.388-0.609), with more aggressive cancers being associated with increased PQ4 and angle and reduced PQ2 and amplitude. A combination of four-quadrant analysis metrics provided an area under the curve of 0.904 (p < 0.001) for the differentiation of prostate cancer from benign prostatic tissue.

CONCLUSIONS

Four-quadrant vector mapping of HM-MRI data provides effective cancer markers, with cancers associated with high PQ4 and high vector angle and lower PQ2 and vector amplitude.

Advanced Diffusion-Weighted MRI for Cancer Microstructure Assessment in Body Imaging, and Its Relationship With Histology

Diffusion-weighted magnetic resonance imaging (DW-MRI) aims to disentangle multiple biological signal sources in each imaging voxel, enabling the computation of innovative maps of tissue microstructure. DW-MRI model development has been dominated by brain applications. More recently, advanced methods with high fidelity to histology are gaining momentum in other contexts, for example, in oncological applications of body imaging, where new biomarkers are urgently needed. The objective of this article is to review the state-of-the-art of DW-MRI in body imaging (ie, not including the nervous system) in oncology, and to analyze its value as compared to reference colocalized histology measurements, given that demonstrating the histological validity of any new DW-MRI method is essential. In this article, we review the current landscape of DW-MRI techniques that extend standard apparent diffusion coefficient (ADC), describing their acquisition protocols, signal models, fitting settings, microstructural parameters, and relationship with histology. Preclinical, clinical, and in/ex vivo studies were included. The most used techniques were intravoxel incoherent motion (IVIM; 36.3% of used techniques), diffusion kurtosis imaging (DKI; 16.7%), vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT; 13.3%), and imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED; 11.7%). Another notable category of techniques relates to innovative b-tensor diffusion encoding or joint diffusion-relaxometry. The reviewed approaches provide histologically meaningful indices of cancer microstructure (eg, vascularization/cellularity) which, while not necessarily accurate numerically, may still provide useful sensitivity to microscopic pathological processes. Future work of the community should focus on improving the inter-/intra-scanner robustness, and on assessing histological validity in broader contexts. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.

[Multiparametric MRI of the prostate: requirements and principles regarding diagnostic reporting]

Multiparametric MRI (mpMRI) is one of the primary diagnostic tools for detecting clinically relevant prostate cancer. It should be routinely used in addition to urological investigations owing to its higher diagnostic yield than systematic biopsies. However, combining targeted and systematic biopsies achieves the highest diagnostic rate. The Prostate Imaging Reporting and Data System (PI-RADS Version 2.1) standardizes the acquisition and interpretation of mpMRI of the prostate. It consists of high-resolution T2- and diffusion-weighted images, the corresponding apparent diffusion coefficient (ADC) maps, and a dynamic contrast-enhanced sequence. Reports describe the increasing likelihood of clinically significant prostate cancer with PI-RADS categories 1-5. The MRI sequence determining the PI-RADS category of a lesion depends on its location within the prostate: in the transitional zone, the T2-weighted sequence and, in the peripheral zone, the diffusion-weighted sequence are the primary determinants. The diffusion-weighted and contrast-enhanced sequences provide secondary classification for the transitional and peripheral zones, respectively. This review summarizes and illustrates the diagnostic criteria defined in PI-RADS 2.1. In addition, evidence for mpMRI of the prostate, its indication and implementation are described.

Joint estimation of relaxation and diffusion tissue parameters for prostate cancer with relaxation-VERDICT MRI

This work presents a biophysical model of diffusion and relaxation MRI for prostate called relaxation vascular, extracellular and restricted diffusion for cytometry in tumours (rVERDICT). The model includes compartment-specific relaxation effects providing T1/T2 estimates and microstructural parameters unbiased by relaxation properties of the tissue. 44 men with suspected prostate cancer (PCa) underwent multiparametric MRI (mp-MRI) and VERDICT-MRI followed by targeted biopsy. We estimate joint diffusion and relaxation prostate tissue parameters with rVERDICT using deep neural networks for fast fitting. We tested the feasibility of rVERDICT estimates for Gleason grade discrimination and compared with classic VERDICT and the apparent diffusion coefficient (ADC) from mp-MRI. The rVERDICT intracellular volume fraction f_ic discriminated between Gleason 3 + 3 and 3 + 4 (p = 0.003) and Gleason 3 + 4 and ≥ 4 + 3 (p = 0.040), outperforming classic VERDICT and the ADC from mp-MRI. To evaluate the relaxation estimates we compare against independent multi-TE acquisitions, showing that the rVERDICT T2 values are not significantly different from those estimated with the independent multi-TE acquisition (p > 0.05). Also, rVERDICT parameters exhibited high repeatability when rescanning five patients (R² = 0.79-0.98; CV = 1-7%; ICC = 92-98%). The rVERDICT model allows for accurate, fast and repeatable estimation of diffusion and relaxation properties of PCa sensitive enough to discriminate Gleason grades 3 + 3, 3 + 4 and ≥ 4 + 3.

Effect of Esomeprazole Treatment on Extracellular Tumor pH in a Preclinical Model of Prostate Cancer by MRI-CEST Tumor pH Imaging

Novel anticancer treatments target the pH regulating system that plays a major role in tumor progression by creating an acidic microenvironment, although few studies have addressed their effect on tumor acidosis. In this study, we investigated in vivo several proton pump inhibitors (PPIs) targeting NHE-1 (Amiloride and Cariporide) and V-ATPase (Esomeprazole and Lansoprazole) proton transporters in the DU145 androgen-insensitive human prostate cancer model. In cellulo results showed that DU145 are sensitive, with decreasing efficacy, to Amiloride, Esomeprazole and Lansoprazole, with marked cell toxicity both in normoxia and in hypoxia, with almost any change in pH. In vivo studies were performed upon administration of Esomeprazole to assess both the acute and chronic effects, and Iopamidol-based tumor pH imaging was performed to evaluate tumor acidosis. Although statistically significant tumor pH changes were observed a few hours after Esomeprazole administration in both the acute study and up to one week of treatment in the chronic study, longer treatment resulted in a lack of changes in tumor acidosis, which was associated to similar tumor growth curves between treated and control groups in both the subcutaneous and orthotopic models. Overall, this study highlights MRI-CEST tumor pH imaging as a valid approach to monitoring treatment response to PPIs.

Detection of prostate cancer using diffusion-relaxation correlation spectrum imaging with support vector machine model - a feasibility study

BACKGROUND

To evaluate the performance of diffusion-relaxation correlation spectrum imaging (DR-CSI) with support vector machine (SVM) in detecting prostate cancer (PCa).

METHODS

In total, 114 patients (mean age, 66 years, range, 48-87 years) who received a prostate MRI and underwent biopsy were enrolled in three stages. Thirty-nine were assigned for the exploration stage to establish the model, 18 for the validation stage to choose the appropriate scale for mapping and 57 for the test stage to compare the diagnostic performance of the DR-CSI and PI-RADS.

RESULTS

In the exploration stage, the DR-CSI model was established and performed better than the ADC and T₂ values (both P < 0.001). The validation result shows that at least 2 pixels were required for both the long-axis and short-axis in the mapping procedure. In the test stage, DR-CSI had higher accuracy than PI-RADS ≥ 3 as a positive finding based on patient (84.2% vs. 63.2%, P = 0.004) and lesion (78.8% vs. 57.6%, P = 0.001) as well as PI-RADS ≥ 4 on lesion (76.5% vs. 64.7%, P = 0.029), while there was no significant difference between DR-CSI and PI-RADS ≥ 4 based on patient (P = 0.508). For clinically significant PCa, DR-CSI had higher accuracy than PI-RADS ≥ 3 based on patients (84.2% vs. 63.2%, P = 0.004) and lesions (62.4% vs. 48.2%, P = 0.036). There was no significant difference between DR-CSI and PI-RADS ≥ 4 (P = 1.000 and 0.845 for the patient and lesion levels, respectively).

CONCLUSIONS

DR-CSI combined with the SVM model may improve the diagnostic accuracy of PCa.

TRIAL REGISTRATION

This study was approved by the Ethics Committee of our institute (Approval No. KY2018-213). Written informed consent was obtained from all participants.

Evaluating metallic artefact of biodegradable magnesium-based implants in magnetic resonance imaging

Magnesium (Mg) implants have shown to cause image artefacts or distortions in magnetic resonance imaging (MRI). Yet, there is a lack of information on how the degradation of Mg-based implants influences the image quality of MRI examinations. In this study, Mg-based implants are analysed in vitro, ex vivo, and in the clinical setting for various magnetic field strengths with the aim to quantify metallic artefact behaviour. In vitro corroded Mg-based screws and a titanium (Ti) equivalent were imaged according to the ASTM F2119. Mg-based and Ti pins were also implanted into rat femurs for different time points and scanned to provide insights on the influence of soft and hard tissue on metallic artefact. Additionally, MRI data of patients with scaphoid fractures treated with CE-approved Mg-based compression screws (MAGNEZIX®) were analysed at various time points post-surgery. The artefact production of the Mg-based material decreased as implant material degraded in all settings. The worst-case imaging scenario was determined to be when the imaging plane was selected to be perpendicular to the implant axis. Moreover, the Mg-based implant outperformed the Ti equivalent in all experiments by producing lower metallic artefact (p < 0.05). This investigation demonstrates that Mg-based implants generate significantly lower metallic distortion in MRI when compared to Ti. Our positive findings suggest and support further research into the application of Mg-based implants including post-operative care facilitated by MRI monitoring of degradation kinetics and bone/tissue healing processes.

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Mg-based implants produce lower metallic artefact than Ti in MRI.

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Metallic artefact production of Mg reduces as degradation increases.

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Mg implants provide sufficient visualisation in MRI for better postoperative care.

Novel method for evaluating spatial resolution of magnetic resonance images

Recently, several methods for evaluating the spatial resolution of magnetic resonance imaging have been reported. However, these methods are not simple and can only be used for specific devices. In this study, we develop a new method (the ladder method) and evaluate its measurement accuracy by adapting the International Electrotechnical Commission (IEC) method to evaluate the spatial resolution. First, the suitable condition for the ladder method was determined by numerical experiments. The ladder method uses a phantom with a periodic pattern which is based on IEC method. Subsequently, the ladder method is evaluated in terms of spatial resolution by dividing the standard deviation (SD) by the average signal in the region of interest (ROI) on the ladder phantom image. To evaluate the precision of the ladder method, it is compared with the modulation transfer function (MTF) calculated from an edge image. The numerical experiment result shows that the evaluation of the spatial resolution using the ladder method is viable, in which a single regression analysis's coefficient of correlation between ladder and MTF of 0.90 or higher is obtained for all evaluations. The ladder method can be assessed using only the signal mean value and SD in the ROI on the target image and exhibit a strong correlation with the MTF. Therefore, the ladder method is a promising method as a substitute for the MTF.

Current Opinion on the Use of Magnetic Resonance Imaging in Staging Prostate Cancer: A Narrative Review

Accurate staging is critical for treatment planning and prognosis in men with prostate Cancer. Prostate magnetic imaging resonance (MRI) may aid in the staging evaluation by verifying organ-confined status, assessing the status of the pelvic lymph nodes, and establishing the local extent of the tumor in patients being considered for therapy. MRI has a high specificity for diagnosing extracapsular extension, and therefore may impact the decision to perform nerve sparing prostatectomy, along with seminal vesicle invasion and lymph node metastases; however, its sensitivity remains limited. Current guidelines vary significantly regarding endorsing the use of MRI for staging locoregional disease. For high-risk prostate cancer, most guidelines recommend cross sectional imaging, including MRI, to evaluate for more extensive disease that may merit change in radiation field, extended androgen deprivation therapy, or guiding surgical planning. Although MRI offers reasonable performance characteristics to evaluate bone metastases, guidelines continue to support the use of bone scintigraphy. Emerging imaging technologies, including coupling positron emission tomography (PET) with MRI, have the potential to improve the accuracy of prostate cancer staging with the use of novel radiotracers.

Histological validation of prostate tissue composition measurement using hybrid multi-dimensional MRI: agreement with pathologists' measures

PURPOSE

To validate prostate tissue composition measured using hybrid multi-dimensional MRI (HM-MRI) by comparing with reference standard (ground truth) results from pathologists' interpretation of clinical histopathology slides following whole mount prostatectomy.

MATERIALS AND METHODS

36 prospective participants with biopsy-confirmed prostate cancer underwent 3 T MRI prior to radical prostatectomy. Axial HM-MRI was acquired with all combinations of echo times of 57, 70, 150, 200 ms and b-values of 0, 150, 750, 1500 s/mm² and data were fitted using a 3-compartment signal model using custom software to generate volumes for each tissue component (stroma, epithelium, lumen). Three experienced genitourinary pathologists independently as well as in consensus reviewed each histology image and provide an estimate of percentage of epithelium and lumen for regions-of-interest corresponding to MRI (n = 165; 64 prostate cancers and 101 benign tissue). Agreement statistics using total deviation index (TDI_0.9) was performed for tissue composition measured using HM-MRI and reference standard results from pathologists' consensus.

RESULTS

Based on the initial results showing typical variation among pathologists TDI_0.9 = 25%, we determined we will declare acceptable agreement if the 95% one-sided upper confident limit of TDI_0.9 is less than 30%. The results of tissue composition measurement from HM-MRI compared to ground truth results from the consensus of 3 pathologists, reveal that ninety percent of absolute paired differences (TDI_0.9) were within 18.8% and 22.4% in measuring epithelium and lumen, respectively. We are 95% confident that 90% of absolute paired differences were within 20.6% and 24.2% in measuring epithelium and lumen, respectively. These were less than our criterion of 30% and inter-pathologists' agreement (22.3% for epithelium and 24.2% for lumen) and therefore we accept the agreement performance of HM-MRI. The results revealed excellent area under the ROC curve for differentiating cancer from benign tissue based on epithelium (HM-MRI: 0.87, pathologists: 0.97) and lumen volume (HM-MRI: 0.85, pathologists: 0.77).

CONCLUSION

The agreement in tissue composition measurement using hybrid multidimensional MRI and consensus of pathologists is on par with the inter-raters (pathologists) agreement.

Validation of Prostate Tissue Composition by Using Hybrid Multidimensional MRI: Correlation with Histologic Findings

Background Tissue estimates obtained by using microstructure imaging techniques, such as hybrid multidimensional (HM) MRI, may improve prostate cancer diagnosis but require histologic validation. Purpose To validate prostate tissue composition measured by using HM MRI, with quantitative histologic evaluation from whole-mount prostatectomy as the reference standard. Materials and Methods In this HIPAA-compliant study, from December 2016 to July 2018, prospective participants with biopsy-confirmed prostate cancer underwent 3-T MRI before radical prostatectomy. Axial HM MRI was performed with all combinations of echo times (57, 70, 150, and 200 msec) and b values (0, 150, 750, and 1500 sec/mm²). Data were fitted by using a three-compartment signal model to generate volumes for each tissue component (stroma, epithelium, lumen). Quantitative histologic evaluation was performed to calculate volume fractions for each tissue component for regions of interest corresponding to MRI. Tissue composition measured by using HM MRI and quantitative histologic evaluation were compared (paired t test) and correlated (Pearson correlation coefficient), and agreement (concordance correlation) was assessed. Receiver operating characteristic curve analysis for cancer diagnosis was performed. Results Twenty-five participants (mean age, 60 years ± 7 [standard deviation]; 30 cancers and 45 benign regions of interest) were included. Prostate tissue composition measured with HM MRI and quantitative histologic evaluation did not differ (stroma, 45% ± 11 vs 44% ± 11 [P = .23]; epithelium, 31% ± 15 vs 34% ± 15 [P = .08]; and lumen, 24% ± 13 vs 22% ± 11 [P = .80]). Between HM MRI and histologic evaluation, there was excellent correlation (Pearson r: overall, 0.91; stroma, 0.82; epithelium, 0.93; lumen, 0.90 [all P < .05]) and agreement (concordance correlation coefficient: overall, 0.91; stroma, 0.81; epithelium, 0.90; and lumen, 0.87). High areas under the receiver operating characteristic curve obtained with HM MRI (0.96 for epithelium and 0.94 for lumen, P < .001) and histologic evaluation (0.94 for epithelium and 0.88 for lumen, P < .001) were found for differentiation between benign tissue and prostate cancer. Conclusion Tissue composition measured by using hybrid multidimensional MRI had excellent correlation with quantitative histologic evaluation as the reference standard. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Muglia in this issue.

Can machine learning-based analysis of multiparameter MRI and clinical parameters improve the performance of clinically significant prostate cancer diagnosis?

Purpose

To establish machine learning(ML) models for the diagnosis of clinically significant prostate cancer (csPC) using multiparameter magnetic resonance imaging (mpMRI), texture analysis (TA), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative analysis and clinical parameters and to evaluate the stability of these models in internal and temporal validation.

Methods

The dataset of 194 men was split into training (n = 135) and internal validation (n = 59) cohorts, and a temporal dataset (n = 58) was used for evaluation. The lesions with Gleason score ≥ 7 were defined as csPC. Logistic regression (LR), stepwise regression (SR), classical decision tree (cDT), conditional inference tree (CIT), random forest (RF) and support vector machine (SVM) models were established by combining mpMRI-TA, DCE-MRI and clinical parameters and validated by internal and temporal validation using the receiver operating characteristic (ROC) curve and Delong’s method.

Results

Eight variables were determined as important predictors for csPC, with the first three related to texture features derived from the apparent diffusion coefficient (ADC) mapping. RF, LR and SR models yielded larger and more stable area under the ROC curve values (AUCs) than other models. In the temporal validation, the sensitivity was lower than that of the internal validation (p < 0.05). There were no significant differences in specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV) and AUC (p > 0.05).

Conclusions

Each machine learning model in this study has good classification ability for csPC. Compared with internal validation, the sensitivity of each machine learning model in temporal validation was reduced, but the specificity, accuracy, PPV, NPV and AUCs remained stable at a good level. The RF, LR and SR models have better classification performance in the imaging-based diagnosis of csPC, and ADC texture-related parameters are of the highest importance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11548-021-02507-w.

Diffusion-weighted imaging in prostate cancer

Diffusion-weighted imaging (DWI), a key component in multiparametric MRI (mpMRI), is useful for tumor detection and localization in clinically significant prostate cancer (csPCa). The Prostate Imaging Reporting and Data System versions 2 and 2.1 (PI-RADS v2 and PI-RADS v2.1) emphasize the role of DWI in determining PIRADS Assessment Category in each of the transition and peripheral zones. In addition, several recent studies have demonstrated comparable performance of abbreviated biparametric MRI (bpMRI), which incorporates only T2-weighted imaging and DWI, compared with mpMRI with dynamic contrast-enhanced MRI. Therefore, further optimization of DWI is essential to achieve clinical application of bpMRI for efficient detection of csPC in patients with elevated PSA levels. Although DWI acquisition is routinely performed using single-shot echo-planar imaging, this method suffers from such as susceptibility artifact and anatomic distortion, which remain to be solved. In this review article, we will outline existing problems in standard DWI using the single-shot echo-planar imaging sequence; discuss solutions that employ newly developed imaging techniques, state-of-the-art technologies, and sequences in DWI; and evaluate the current status of quantitative DWI for assessment of tumor aggressiveness in PC.

High spectral and spatial resolution MRI of prostate cancer: a pilot study

PURPOSE

High spectral and spatial resolution (HiSS) MRI is a spectroscopic imaging method focusing on water and fat resonances that has good diagnostic utility in breast imaging. The purpose of this work was to assess the feasibility and potential utility of HiSS MRI for the diagnosis of prostate cancer.

METHODS

HiSS MRI was acquired at 3 T from six patients who underwent prostatectomy, yielding a train of 127 phase-coherent gradient echo (GRE) images. In the temporal domain, changes in voxel intensity were analyzed and linear (R) and quadratic (R1, R2) quantifiers of signal logarithm decay were calculated. In the spectral domain, three signal scaling-independent parameters were calculated: water resonance peak width (PW), relative peak asymmetry (PRA), and relative peak distortion from ideal Lorentzian shape (PRD). Seven cancer and five normal tissue regions of interest were identified in correlation with pathology and compared.

RESULTS

HiSS-derived quantifiers, except R2, showed high reproducibility (coefficients of variation, 5%-14%). Spectral domain quantifiers performed better than temporal domain quantifiers, with receiver operator characteristic areas under the curve ranging from of 0.83 to 0.91. For temporal domain parameters, the range was 0.74 to 0.91. Low absolute values of the coefficients of correlation between monoexponential decay markers (R, PW) and resonance shape markers (PRA, PRD) were observed (range, 0.23-0.38).

CONCLUSION

The feasibility and potential diagnostic utility of HiSS MRI in the prostate at 3 T without an endorectal coil was confirmed. Weak correlation between well-performing markers indicates that complementary information could be leveraged to further improve diagnostic accuracy.

Voxel-level Classification of Prostate Cancer on Magnetic Resonance Imaging: Improving Accuracy Using Four-Compartment Restriction Spectrum Imaging

BACKGROUND

Diffusion magnetic resonance imaging (MRI) is integral to detection of prostate cancer (PCa), but conventional apparent diffusion coefficient (ADC) cannot capture the complexity of prostate tissues and tends to yield noisy images that do not distinctly highlight cancer. A four-compartment restriction spectrum imaging (RSI₄ ) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI₄ -C₁ , yielded greatest tumor conspicuity.

PURPOSE

To evaluate the slowest diffusion compartment of a four-compartment spectrum imaging model (RSI₄ -C₁ ) as a quantitative voxel-level classifier of PCa.

STUDY TYPE

Retrospective.

SUBJECTS

Forty-six men who underwent an extended MRI acquisition protocol for suspected PCa. Twenty-three men had benign prostates, and the other 23 men had PCa.

FIELD STRENGTH/SEQUENCE

A 3 T, multishell diffusion-weighted and axial T2-weighted sequences.

ASSESSMENT

High-confidence cancer voxels were delineated by expert consensus, using imaging data and biopsy results. The entire prostate was considered benign in patients with no detectable cancer. Diffusion images were used to calculate RSI₄ -C₁ and conventional ADC. Classifier images were also generated.

STATISTICAL TESTS

Voxel-level discrimination of PCa from benign prostate tissue was assessed via receiver operating characteristic (ROC) curves generated by bootstrapping with patient-level case resampling. RSI₄ -C₁ was compared to conventional ADC for two metrics: area under the ROC curve (AUC) and false-positive rate for a sensitivity of 90% (FPR₉₀ ). Statistical significance was assessed using bootstrap difference with two-sided α = 0.05.

RESULTS

RSI₄ -C₁ outperformed conventional ADC, with greater AUC (mean 0.977 [95% CI: 0.951-0.991] vs. 0.922 [0.878-0.948]) and lower FPR₉₀ (0.032 [0.009-0.082] vs. 0.201 [0.132-0.290]). These improvements were statistically significant (P < 0.05).

DATA CONCLUSION

RSI₄ -C₁ yielded a quantitative, voxel-level classifier of PCa that was superior to conventional ADC. RSI classifier images with a low false-positive rate might improve PCa detection and facilitate clinical applications like targeted biopsy and treatment planning.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives

Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.