Automatic Detection and Quantitative DCE-MRI Scoring of Prostate Cancer Aggressiveness

The Role of Dynamic Contrast Enhanced Magnetic Resonance Imaging in Evaluating Prostate Adenocarcinoma: A Partially-Blinded Retrospective Study of a Prostatectomy Patient Cohort With Whole Gland Histopathology Correlation and Application of PI-RADS or TNM Staging

BACKGROUND

Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in the current Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS v2.1) is considered optional, with primary scoring based on T2-weighted imaging (T2WI) and diffusion weighted imaging (DWI). Our study is designed to assess the relative contribution of DCE MRI in a patient-cohort with whole mount prostate histopathology and spatially-mapped prostate adenocarcinoma (PCa) for reference.

METHODS

We performed a partially-blinded retrospective review of 47 prostatectomy patients with recent multi-parametric MRI (mpMRI). Scans included T2WI, DWI with apparent diffusion coefficient (ADC) mapping, and DCE imaging. Lesion conspicuity was scored on a 10-point scale with ≥ 6 considered "positive," and image quality was assessed on a 4-point scale for each sequence. The diagnostic contribution of DCE images was evaluated on a 4-point scale. The mpMRI studies were assigned PI-RADS scores and tumor, node, metastasis (TNM) T-stage with blinded comparison to spatially-mapped whole-mount pathology. Results were compared to the prospective clinical reports, which used standardized PI-RADS templates that emphasize T2WI, DWI and ADC.

RESULTS

Per lesion sensitivity for PCa was 93.5%, 82.6%, 63.0%, and 58.7% on T2WI, DCE, ADC and DWI, respectively. Mean lesion conspicuity was 8.5, 7.9, 6.2, and 6.1, on T2W, DCE, ADC and DWI, respectively. The higher values on T2WI and DCE imaging were not significantly different from each other but were both significantly different from DWI and ADC (p < 0.001). DCE scans were determined to have a marked diagnostic contribution in 83% of patients, with the most common diagnostic yield being detection of contralateral peripheral zone tumor or delineating presence/absence of extra-prostatic extension (EPE), contributing to more accurate PCa staging by PI-RADS or TNM, as compared to histopathology.

CONCLUSION

We demonstrate that DCE may contribute to lesion detection and local staging as compared to T2WI plus DWI-ADC alone and that lesion conspicuity using DCE is markedly improved as compared to DWI-ADC. These findings support modification of PI-RADS v2.1 to include use of DCE acquisitions and that a TNM staging is feasible on mpMRI as compared to surgical pathology.

HRS Improves Active Surveillance for Prostate Cancer by Timely Identification of Progression

RATIONALE AND OBJECTIVES

Active surveillance (AS) is the preferred management strategy for low-risk prostate cancer. This study aimed to evaluate the impact of Habitat Risk Score (HRS), an automated approach for mpMRI analysis, for early detection of progressors in a prospective AS clinical trial (MAST NCT02242773).

MATERIALS AND METHODS

The MAST protocol includes Confirmatory mpMRI ultrasound fusion (MRI-US) biopsy and yearly surveillance MRI-US biopsies for up to 3 years. Clinical and mpMRI data from patients that progressed based on protocol criteria at years 1-3 were reviewed. Patients were classified as "MRI/HRS Progressors" if the PI-RADS lesion(s) had been targeted throughout the surveillance and resulted in positive biopsies, or as "Missed Progressors" if the lesion(s) were not identified by PI-RADS ("PI-RADS Miss") or were missed by the biopsy ("Needle Miss"). HRS maps were generated for each patient and evaluated for association with histopathological progression.

RESULTS

Of the 34 patients, 15 were classified as "MRI/HRS Progressors" and 19 as "Missed Progressors" (12 "PI-RADS Miss", seven "Needle Miss"). In all cases, HRS confirmed the PI-RADS assessment. In the "PI-RADS Miss" group, HRS identified the lesions in all patients that were not targeted by biopsy and resulted in patient reclassification. HRS volumes showed clear association with tumor evolution both in terms of volume and aggressiveness over time.

CONCLUSION

HRS volumes can serve as a quantitative biomarker for early detection of progression and lead to timely conversion to treatment, thereby improving patient outcomes and reducing the burden of unnecessary surveillance.

Is There an Added Value of Quantitative DCE-MRI by Magnetic Resonance Dispersion Imaging for Prostate Cancer Diagnosis?

In this multicenter, retrospective study, we evaluated the added value of magnetic resonance dispersion imaging (MRDI) to standard multiparametric MRI (mpMRI) for PCa detection. The study included 76 patients, including 51 with clinically significant prostate cancer (csPCa), who underwent radical prostatectomy and had an mpMRI including dynamic contrast-enhanced MRI. Two radiologists performed three separate randomized scorings based on mpMRI, MRDI and mpMRI+MRDI. Radical prostatectomy histopathology was used as the reference standard. Imaging and histopathology were both scored according to the Prostate Imaging-Reporting and Data System V2.0 sector map. Sensitivity and specificity for PCa detection were evaluated for mpMRI, MRDI and mpMRI+MRDI. Inter- and intra-observer variability for both radiologists was evaluated using Cohen's Kappa. On a per-patient level, sensitivity for csPCa for radiologist 1 (R1) for mpMRI, MRDI and mpMRI+MRDI was 0.94, 0.82 and 0.94, respectively. For the second radiologist (R2), these were 0.78, 0.94 and 0.96. R1 detected 4% additional csPCa cases using MRDI compared to mpMRI, and R2 detected 20% extra csPCa cases using MRDI. Inter-observer agreement was significant only for MRDI (Cohen's Kappa = 0.4250, p = 0.004). The results of this study show the potential of MRDI to improve inter-observer variability and the detection of csPCa.

Uncovering prostate cancer aggressiveness signal in T2-weighted MRI through a three-reference tissues normalization technique

Quantitative T2-weighted MRI (T2W) interpretation is impeded by the variability of acquisition-related features, such as field strength, coil type, signal amplification, and pulse sequence parameters. The main purpose of this work is to develop an automated method for prostate T2W intensity normalization. The procedure includes the following: (i) a deep learning-based network utilizing MASK R-CNN for automatic segmentation of three reference tissues: gluteus maximus muscle, femur, and bladder; (ii) fitting a spline function between average intensities in these structures and reference values; and (iii) using the function to transform all T2W intensities. The T2W distributions in the prostate cancer regions of interest (ROIs) and normal appearing prostate tissue (NAT) were compared before and after normalization using Student's t-test. The ROIs' T2W associations with the Gleason Score (GS), Decipher genomic score, and a three-tier prostate cancer risk were evaluated with Spearman's correlation coefficient (rS ). T2W differences in indolent and aggressive prostate cancer lesions were also assessed. The MASK R-CNN was trained with manual contours from 32 patients. The normalization procedure was applied to an independent MRI dataset from 83 patients. T2W differences between ROIs and NAT significantly increased after normalization. T2W intensities in 231 biopsy ROIs were significantly negatively correlated with GS (rS  = -0.21, p = 0.001), Decipher (rS  = -0.193, p = 0.003), and three-tier risk (rS  = -0.235, p < 0.001). The average T2W intensities in the aggressive ROIs were significantly lower than in the indolent ROIs after normalization. In conclusion, the automated triple-reference tissue normalization method significantly improved the discrimination between prostate cancer and normal prostate tissue. In addition, the normalized T2W intensities of cancer exhibited a significant association with tumor aggressiveness. By improving the quantitative utilization of the T2W in the assessment of prostate cancer on MRI, the new normalization method represents an important advance over clinical protocols that do not include sequences for the measurement of T2 relaxation times.

Quantitative DCE Dynamics on Transformed MR Imaging Discriminates Clinically Significant Prostate Cancer

Dynamic contrast enhancement (DCE) imaging is a valuable sequence of multiparametric magnetic resonance imaging (mpMRI). A DCE sequence enhances the vasculature and complements T2-weighted (T2W) and Diffusion-weighted imaging (DWI), allowing early detection of prostate cancer. However, DCE assessment has remained primarily qualitative. The study proposes quantifying DCE characteristics (T1W sequences) using six time-dependent metrics computed on feature transformations (306 radiomic features) of abnormal image regions observed over time. We applied our methodology to prostate cancer patients with the DCE MRI images (n = 25) who underwent prostatectomy with confirmed pathological assessment of the disease using Gleason Score. Regions of abnormality were assessed on the T2W MRI, guided using the whole mount pathology. Preliminary analysis finds over six temporal DCE imaging features obtained on different transformations on the imaging regions showed significant differences compared to the indolent counterpart (P ≤ 0.05, q ≤ 0.01). We find classifier models using logistic regression formed on DCE features after feature-based transformation (Centre of Mass) had an AUC of 0.89-0.94. While using mean feature-based transformation, the AUC was in the range of 0.71-0.76, estimated using the 0.632 bootstrap cross-validation method and after applying sample balancing using the synthetic minority oversampling technique (SMOTE). Our study finds, radiomic transformation of DCE images (T1 sequences) provides better signal standardization. Their temporal characteristics allow improved discrimination of aggressive disease.

The role of radiomics in prostate cancer radiotherapy

"Radiomics," as it refers to the extraction and analysis of a large number of advanced quantitative radiological features from medical images using high-throughput methods, is perfectly suited as an engine for effectively sifting through the multiple series of prostate images from before, during, and after radiotherapy (RT). Multiparametric (mp)MRI, planning CT, and cone beam CT (CBCT) routinely acquired throughout RT and the radiomics pipeline are developed for extraction of thousands of variables. Radiomics data are in a format that is appropriate for building descriptive and predictive models relating image features to diagnostic, prognostic, or predictive information. Prediction of Gleason score, the histopathologic cancer grade, has been the mainstay of the radiomic efforts in prostate cancer. While Gleason score (GS) is still the best predictor of treatment outcome, there are other novel applications of quantitative imaging that are tailored to RT. In this review, we summarize the radiomics efforts and discuss several promising concepts such as delta-radiomics and radiogenomics for utilizing image features for assessment of the aggressiveness of prostate cancer and its outcome. We also discuss opportunities for quantitative imaging with the advance of instrumentation in MRI-guided therapies.

Dynamic contrast-enhanced magnetic resonance imaging biomarkers predict chemotherapeutic responses and survival in primary central-nervous-system lymphoma

OBJECTIVES

To evaluate the utility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the response of chemotherapy and clinical outcomes in primary central-nervous-system lymphoma (PCNSL) patients.

METHODS

DCE-MRI in 56 patients enrolled in a prospective study was performed at baseline and 30 days after treatment from 2016 to 2019. Multivariate logistic regression analyses were performed to assess risk factors for tumor responses. The predictive values of related parameters derived from DCE were analyzed via receiver operating characteristic (ROC) curve analysis. To evaluate prognostic factors, the Kaplan-Meier survival analysis with log-rank tests and Cox regression tests were analyzed.

RESULTS

K^trans and V_e were higher in the non-response group than in the response group (p < 0.05). The K^trans and the percentage of K^trans decreased after 30 days of treatment were independent predictors of chemotherapy responses (p = 0.034 and p = 0.019). ROC analysis indicated that the cut-off point of K^trans for predicting chemotherapeutic responses was 0.353 min^-1 (AUC, 0.941; 95% CI, 0.87-1; p < 0.001) and percentage of K^trans decreased after 30 days of treatment was 15.2% (AUC, 0.858; 95% CI, 0.742-0.970; p < 0.001). The greater decrease in K^trans correlated with a longer progression-free survival (PFS) (χ² = 13.203, p < 0.001). The higher K^trans was an independent predictor for shorter PFS (hazard ratio, 10.182; 95% CI, 2.510-41.300; p = 0.001).

CONCLUSIONS

K^trans and K^trans change measured by DCE-MRI were reliable biomarkers for predicting chemotherapy responses in PCNSL patients.

KEY POINTS

• Baseline K^trans and greater decrease in K^trans can predict chemotherapeutic efficacy. • DCE-MRI provides quantitative parameters reflecting the tumor microenvironment. • Targeted treatment therapy can be given with more evidence in the future.

Phase I Trial of MRI-Guided Prostate Cancer Lattice Extreme Ablative Dose (LEAD) Boost Radiation Therapy

PURPOSE

A phase I clinical trial was designed to test the feasibility and toxicity of administering high-dose spatially fractionated radiation therapy to magnetic resonance imaging (MRI)-defined prostate tumor volumes, in addition to standard treatment.

METHODS AND MATERIALS

We enrolled 25 men with favorable to high-risk prostate cancer and 1 to 3 suspicious multiparametric MRI (mpMRI) gross tumor volumes (GTVs). The mpMRI-GTVs were treated on day 1 with 12 to 14 Gy via dose cylinders using a lattice extreme ablative dose technique. The entire prostate, along with the proximal seminal vesicles, was then treated to 76 Gy at 2 Gy/fraction. For some high-risk patients, the distal seminal vesicles and pelvic lymph nodes received 56 Gy at 1.47 Gy/fraction concurrently in 38 fractions. The total dose to the lattice extreme ablative dose cylinder volume(s) was 88 to 90 Gy (112-123 Gy in 2.0 Gy equivalents, assuming an α-to-β ratio of 3).

RESULTS

Dosimetric parameters were satisfactorily met. Median follow-up was 66 months. There were no grade 3 acute/subacute genitourinary or gastrointestinal adverse events. Maximum late genitourinary toxicity was grade 1 in 15 (60%), grade 2 in 4 (16%), and grade 4 in 1 (4%; sepsis after a posttreatment transurethral resection). Maximum late gastrointestinal toxicity was grade 1 in 11 (44%) and grade 2 in 4 (16%). Two patients experienced biochemical failure.

CONCLUSIONS

External beam radiation therapy delivered with an upfront spatially fractionated, stereotactic high-dose mpMRI-GTV boost is feasible and was not associated with any unexpected events. The technique is now part of a follow-up phase II randomized trial.

Habitats in DCE-MRI to Predict Clinically Significant Prostate Cancers

Prostate cancer identification and assessment of clinical significance continues to be a challenge. Routine multiparametric magnetic resonance imaging has shown to be useful in assessing disease progression. Although dynamic contrast-enhanced imaging (DCE) has the ability to characterize perfusion across time and has shown enormous utility, radiological assessment (Prostate Imaging-Reporting and Data System or PIRADS version 2) has limited its use owing to lack of consistency and nonquantitative nature. In our work, we propose a systematic methodology to quantify perfusion dynamics for the DCE imaging. Using these metrics, 7 different subregions or perfusion habitats of the targeted lesions are localized and related to clinical significance. We found that quantitative features describing the habitat based on the late area under the DCE time-activity curve was a good predictor of clinical significance disease. The best predictive feature in the habitat had an AUC of 0.82, CI [0.81–0.83].

Predicting clinically significant prostate cancer using DCE-MRI habitat descriptors

Prostate cancer diagnosis and treatment continues to be a major public health challenge. The heterogeneity of the disease is one of the major factors leading to imprecise diagnosis and suboptimal disease management. The improved resolution of functional multi-parametric magnetic resonance imaging (mpMRI) has shown promise to improve detection and characterization of the disease. Regions that subdivide the tumor based on Dynamic Contrast Enhancement (DCE) of mpMRI are referred to as DCE-Habitats in this study. The DCE defined perfusion curve patterns on the identified tumor habitat region are used to assess clinical significance. These perfusion curves were systematically quantified using seven features in association with the patient biopsy outcome and classifier models were built to find the best discriminating characteristics between clinically significant and insignificant prostate lesions defined by Gleason score (GS). Multivariable analysis was performed independently on one institution and validated on the other, using a multi-parametric feature model, based on DCE characteristics and ADC features. The models had an intra institution Area under the Receiver Operating Characteristic (AUC) of 0.82. Trained on Institution I and validated on the cohort from Institution II, the AUC was also 0.82 (sensitivity 0.68, specificity 0.95).

PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images

Grand challenges stimulate advances within the medical imaging research community; within a competitive yet friendly environment, they allow for a direct comparison of algorithms through a well-defined, centralized infrastructure. The tasks of the two-part PROSTATEx Challenges (the PROSTATEx Challenge and the PROSTATEx-2 Challenge) are (1) the computerized classification of clinically significant prostate lesions and (2) the computerized determination of Gleason Grade Group in prostate cancer, both based on multiparametric magnetic resonance images. The challenges incorporate well-vetted cases for training and testing, a centralized performance assessment process to evaluate results, and an established infrastructure for case dissemination, communication, and result submission. In the PROSTATEx Challenge, 32 groups apply their computerized methods (71 methods total) to 208 prostate lesions in the test set. The area under the receiver operating characteristic curve for these methods in the task of differentiating between lesions that are and are not clinically significant ranged from 0.45 to 0.87; statistically significant differences in performance among the top-performing methods, however, are not observed. In the PROSTATEx-2 Challenge, 21 groups apply their computerized methods (43 methods total) to 70 prostate lesions in the test set. When compared with the reference standard, the quadratic-weighted kappa values for these methods in the task of assigning a five-point Gleason Grade Group to each lesion range from - 0.24 to 0.27; superiority to random guessing can be established for only two methods. When approached with a sense of commitment and scientific rigor, challenges foster interest in the designated task and encourage innovation in the field.

MRI-guided localization of the dominant intraprostatic lesion and dose analysis of volumetric modulated arc therapy planning for prostate cancer

PURPOSE

Primary radiation therapy is a curative treatment option for prostate cancer. The aim of this study was to evaluate the detection of the dominant intraprostatic lesion (DIL) with magnetic resonance imaging (MRI) for radiotherapy treatment planning, the comparison with transrectal ultrasound (TRUS)-guided biopsies and the examination of the dose distribution in relation to the DIL location.

MATERIALS AND METHODS

In all, 54 patients with treatment planning MRI for primary radiotherapy of prostate cancer from 03/2015 to 03/2017 at the Universitätsklinikum Würzburg were identified. The localization of the DIL was based on MRI with T2- and diffusion-weighted imaging. After registration of the MR image sets within Pinnacle³ (Philips Radiation Oncology Systems, Fitchburg, WI, USA), the dose distribution was analyzed. The location of the DIL was compared to the pathology reports in a side-based manner.

RESULTS

The DIL mean dose (Dmean) was 77.51 ± 0.77 Gy and in 50/51 cases within the tolerance range or exceeded the prescribed dose. There was a significant difference in Dmean between ventral (n = 21) and dorsal (n = 30) DIL (77.87 ± 0.67 vs. 77.26 ± 0.77 Gy; p = 0.005). MRI-guided localization showed an accuracy and sensitivity of up to 78.8% and 82.1% for inclusion of secondary lesions, respectively.

CONCLUSION

Up to 82.1% of histologically verified intraprostatic lesions were identified in the context of MRI-guided radiotherapy treatment planning. As expected, dorsal DIL tend to be minimally underdosed in comparison to ventral DIL. Adequate dose coverage was achieved in over 98% of patients.

An Automated Multiparametric MRI Quantitative Imaging Prostate Habitat Risk Scoring System for Defining External Beam Radiation Therapy Boost Volumes

PURPOSE

To develop a prostate tumor habitat risk scoring (HRS) system based on multiparametric magnetic resonance imaging (mpMRI) referenced to prostatectomy Gleason score (GS) for automatic delineation of gross tumor volumes. A workflow for integration of HRS into radiation therapy boost volume dose escalation was developed in the framework of a phase 2 randomized clinical trial (BLaStM).

METHODS AND MATERIALS

An automated quantitative mpMRI-based 10-point pixel-by-pixel method was optimized to prostatectomy GSs and volumes using referenced dynamic contrast-enhanced and apparent diffusion coefficient sequences. The HRS contours were migrated to the planning computed tomography scan for boost volume generation.

RESULTS

There were 51 regions of interest in 12 patients who underwent radical prostatectomy (26 with GS ≥7 and 25 with GS 6). The resultant heat maps showed inter- and intratumoral heterogeneity. The HRS6 level was significantly associated with radical prostatectomy regions of interest (slope 1.09, r = 0.767; P < .0001). For predicting the likelihood of cancer, GS ≥7 and GS ≥8 HRS6 area under the curve was 0.718, 0.802, and 0.897, respectively. HRS was superior to the Prostate Imaging, Reporting and Diagnosis System 4/5 classification, wherein the area under the curve was 0.62, 0.64, and 0.617, respectively (difference with HR6, P < .0001). HRS maps were created for the first 37 assessable patients on the BLaStM trial. There were an average of 1.38 habitat boost volumes per patient at a total boost volume average of 3.6 cm³.

CONCLUSIONS

An automated quantitative mpMRI-based method was developed to objectively guide dose escalation to high-risk habitat volumes based on prostatectomy GS.