Multiparametric MRI Apparent Diffusion Coefficient (ADC) Accuracy in Diagnosing Clinically Significant Prostate Cancer

Prostate diffusion-weighted imaging (DWI) in MR-guided radiotherapy: Reproducibility assessment on 1.5 T MR-Linac and 1.5 T MR-simulator

PURPOSE

Diffusion-weighted imaging (DWI) holds promise for image-guided radiotherapy (MRgRT) in prostate cancer. However, challenges persist due to image distortion, artifacts, and apparent diffusion coefficient (ADC) reproducibility issues. This study aimed to assess DWI image quality and ADC reproducibility on both a 1.5 T MR-simulator and a 1.5 T MR-Linac, employing measurements from both an ACR MRI phantom and prostate cancer patients undergoing MRgRT.

METHODS

DW-MRI scans were conducted on 19 patients (mean age = 69 ± 8 years, with 23 MR-visible intra-prostatic lesions) and an ACR MRI phantom using a 1.5 T MR-simulator (b-values = 0, 800, 1400s/mm2) and a 1.5 T MR-Linac (b-values = 50, 500, 800 s/mm2). ADC homogeneity in the phantom was evaluated via 1D profile flatness (FL) in three directions. Image quality was assessed through qualitative 5-point Likert scale ratings and quantitative ADC and signal-to-noise ratio (SNR) measurements. Intra-observer reproducibility of image quality scores was evaluated using ICC(1, 2). Geometric distortion was measured by comparing landmark sizes on the ACR phantom against the ground truth. Mean ADC and reproducibility were assessed using Bland-Altman plots.

RESULTS

Both MR-simulator and MR-Linac demonstrated high ADC homogeneity (FL > 87.5% - MR-simulator: 97.23 ± 0.62%, MR-Linac: 94.75 ± 0.62%, p < 0.05) in the phantom. Image quality scores revealed acceptable ratings (≥3) for capsule demarcation, image artifacts, and geometric distortion in patients. However, intra-prostatic lesions were barely discernible in b800 images for both MR-simulator (average score = 2.37 ± 1.33) and MR-Linac (average score = 2.16 ± 1.28). While MR-Linac DWI scans exhibited significantly more severe geometric distortion than MR-simulator scans (p < 0.01), most phantom measurements fell within the image in-plane resolution of 3 mm. Significant differences were noted in MR-simulator ADC (CTV: 1.20 ± 0.14 × 10-3 mm2/s (MR-simulator) vs 1.06 ± 0.10 × 10-3 mm2/s (MR-Linac); GTV: 1.05 ± 0.21 × 10-3 mm2/s vs 0.91 ± 0.16 × 10 mm2/s, all p < 0.05), with a small non-zero bias observed in the Bland-Altman analysis (CTV: 12.3%; GTV: 14.5%).

CONCLUSION

The significantly larger MR-simulator ADC and the small non-zero bias hint at potential systematic differences in ADC values acquired from an MR-simulator and an MR-Linac, both at 1.5 T. Although acceptable ADC homogeneity was noted, caution is warranted in interpreting MR-Linac DWI images due to occasional severe artifacts. Further studies are essential to validate DWI and ADC as reliable imaging markers in prostate cancer MRgRT.

Oncological Outcomes in Men With Favorable Intermediate Risk Prostate Cancer Enrolled in Active Surveillance

BACKGROUND/AIM

To evaluate the long-term oncological outcomes in men with intermediate risk prostate cancer (PCa) enrolled in active surveillance (AS).

PATIENTS AND METHODS

From April 2015 to December 2022, 30 men with Gleason score 3+4/ISUP Grade Group2 (GG2), greatest percentage of cancer (GPC) ≤50%, Gleason pattern 4 ≤10%, ≤3 positive biopsy cores were enrolled in AS. All patients underwent confirmatory transperineal saturation biopsy (SPBx: 20 cores) 12 months from diagnosis plus multiparametric magnetic resonance (mpMRI) evaluation. At the last follow-up, 68Ga prostate-specific membrane antigen (PSMA) positron-emission tomography (PET)/computed tomography (CT) was added: lesions with PIRADS score ≥3 and/or standardized uptake value (SUVmax) >5 were submitted to four targeted cores.

RESULTS

Three out of 30 (10%) men with GG2 PCa were reclassified at confirmatory biopsy. At the last follow-up (median 5.2 years), only 2 of 27 (7.4%) men were reclassified and 23/30 (76.6%) continued AS.

CONCLUSION

Men with favorable GG2 PCa enrolled in AS have good long-term oncological results. The use of selective criteria (i.e., SPBx, mpMRI, PSMA PET/CT) reduces the risk of reclassification.

Comparison of the Utility of PI-RADS 2.1, ADC Values, and Combined Use of Both, for the Diagnosis of Transition Zone Prostate Cancers

OBJECTIVE

To assess the performance of apparent diffusion coefficient (ADC; values or category) alone, Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) scoring alone, and the two in combination, to diagnose transition zone prostate cancers (PCas).

METHODS

This retrospective study included 222 patients who underwent multiparametric magnetic resonance imaging of the prostate between May 2020 and December 2022 and who had pathologically confirmed PCa or benign prostatic hyperplasia (BPH). Prostate Imaging Reporting and Data System version 2.1 and ADC (values or category) were used in the assessment of suspicious findings identified in the transition zone. The interobserver agreements for region-of-interest measurements were calculated by intraclass correlation coefficients. Logistic regression analyses were used to determine the performance of PI-RADS v2.1 alone and in combination with ADC (values or category) to diagnose PCa. Receiver operating characteristic curve and DeLong test were used to evaluate the diagnostic performance of the quantitative parameters.

RESULTS

A total of 152 patients had BPH, and 70 patients had PCa. For BPH versus PCa, the ADC values of PCa (0.64 × 10 -3 ± 0.16 × 10 -3 mm 2 /s) were significantly lower than BPH (1.06 ± 0.18 × 10 -3 mm 2 /s; P < 0.001). The PI-RADS scores for PCa (5 [interquartile range, 5-5]) were significantly higher than BPH (2 [interquartile range, 2-3]; P < 0.001). For all patients who had PI-RADS 1-5, the combined use of ADC (values or category) together with PI-RADS v2.1 did not perform significantly better than the use of PI-RADS v2.1 alone. The receiver operating characteristic of ADC category in combination with PI-RADS v2.1 score, 0.756 (95% confidence interval, 0.646-0.846), was significantly higher than that for PI-RADS 2.1 alone, 0.631 (95% confidence interval, 0.514-0.738), in PI-RADS 3-4 lesions ( P = 0.047).

CONCLUSION

The ADC category can help to improve the diagnostic performance of PI-RADS v2.1 category 3-4 lesions in diagnosing PCa.

Values of multiparametric and biparametric MRI in diagnosing clinically significant prostate cancer: a multivariate analysis

BACKGROUND

To investigate the value of semi-quantitative and quantitative parameters (PI-RADS score, T2WI score, ADC, Ktrans, and Kep) based on multiparametric MRI (mpMRI) or biparametric MRI (bpMRI) combined with prostate specific antigen density (PSAD) in detecting clinically significant prostate cancer (csPCa).

METHODS

A total of 561 patients (276 with csPCa; 285 with non-csPCa) with biopsy-confirmed prostate diseases who underwent preoperative mpMRI were included. Prostate volume was measured for calculation of PSAD. Prostate index lesions were scored on a five-point scale on T2WI images (T2WI score) and mpMRI images (PI-RADS score) according to the PI-RADS v2.1 scoring standard. DWI and DCE-MRI images were processed to measure the quantitative parameters of the index lesion, including ADC, Kep, and Ktrans values. The predictors of csPCa were screened by logistics regression analysis. Predictive models of bpMRI and mpMRI were established. ROC curves were used to evaluate the efficacy of parameters and the model in diagnosing csPCa.

RESULTS

The independent diagnostic accuracy of PSA density, PI-RADS score, T2WI score, ADCrec, Ktrans, and Kep for csPCa were 80.2%, 89.5%, 88.3%, 84.6%, 58.5% and 61.6%, respectively. The diagnostic accuracy of bpMRI T2WI score and ADC value combined with PSAD was higher than that of PI-RADS score. The combination of mpMRI PI‑RADS score, ADC value with PSAD had the highest diagnostic accuracy.

CONCLUSIONS

PI-RADS score according to the PI-RADS v2.1 scoring standard was the most accurate independent diagnostic index. The predictive value of bpMRI model for csPCa was slightly lower than that of mpMRI model, but higher than that of PI-RADS score.

Clinical and Radiological Factors for Predicting Clinically Significant Prostate Cancer in Biopsy-Naive Patients With PI-RADS 3 Lesions

OBJECTIVE

This study intends to examine the anticipatory power of clinical and radiological parameters in detecting clinically significant prostate cancer in patients demonstrating Prostate Imaging Reporting and Data System 3 lesions.

METHODS

This was a retrospective study. The study included participation from 453 patients at the First Affiliated Hospital of Soochow University, sampled between September 2017 through August 2022. Each patient underwent a routine 12-core prostate biopsy followed by a 2 to 5 core fusion-targeted biopsy. We utilized both univariate and multivariate logistic regression analyses to identify the parameters that have a correlation with clinically significant prostate cancer. The predictive ability of these parameters was assessed using the receiver operating characteristic curve, leading to the creation of a nomogram.

RESULTS

Clinically significant prostate cancer was detected in 68 out of 453 patients with Prostate Imaging Reporting and Data System 3 lesions (15.01%). Among Prostate Imaging Reporting and Data System 3a and 3b patients, 4.78% (3.09% of the total) and 33.75% (11.92% of the total), respectively, had clinically significant prostate cancer. Systematic biopsy improved prostate cancer and clinically significant prostate cancer detection rates by 7.72% and 3.09%, respectively, compared to targeted biopsy. Without systematic biopsy, there would be an undetected rate of 15% for prostate cancer and 8.13% for clinically significant prostate cancer in Prostate Imaging Reporting and Data System 3b patients. Several clinical parameters, including age, prostate-specific antigen density, lesion volume, apparent diffusion coefficient, and digital rectal examination, were statistically significant in the logistic regression analysis for clinically significant prostate cancer. The individual diagnostic accuracies of these parameters for clinically significant prostate cancer were 0.648, 0.645, 0.75, 0.763, and 0.7, respectively, but their combined accuracy improved to 0.866. A well-fit nomogram based on the identified risk factors was constructed (χ2 = 10.254, P = .248).

CONCLUSION

The combination of age, prostate-specific antigen density, lesion volume, apparent diffusion coefficient, and digital rectal examination presented a higher diagnostic value for clinically significant prostate cancer than any single parameter in patients with Prostate Imaging Reporting and Data System 3 lesions. Systematic biopsy proved crucial for biopsy-naive patients with Prostate Imaging Reporting and Data System 3 lesions and should not be omitted.

Microvascular proliferation is associated with high tumour blood flow by mpMRI and disease progression in primary prostate cancer

Active angiogenesis may be assessed by immunohistochemistry using Nestin, a marker of newly formed vessels, combined with Ki67 for proliferating cells. Here, we studied microvascular proliferation by Nestin-Ki67 co-expression in prostate cancer, focusing on relations to quantitative imaging parameters from anatomically matched areas obtained by preoperative mpMRI, clinico-pathological features and prognosis. Tumour slides from 67 patients (radical prostatectomies) were stained for Nestin-Ki67. Proliferative microvessel density (pMVD) and presence of glomeruloid microvascular proliferation (GMP) were recorded. From mpMRI, forward volume transfer constant (Ktrans), reverse volume transfer constant (kep), volume of EES (ve), blood flow, and apparent diffusion coefficient (ADC) were obtained. High pMVD was associated with high blood flow (p = 0.008) and low ADC (p = 0.032). High Ktrans, kep, and blood flow were associated with high Gleason score. High pMVD, GMP, and low ADC were associated with most adverse clinico-pathological factors. Regarding prognosis, high pMVD, Ktrans, kep, and low ADC were associated with reduced biochemical recurrence-free- and metastasis-free survival (p ≤ 0.044) and high blood flow with reduced time to biochemical- and clinical recurrence (p < 0.026). In multivariate analyses however, microvascular proliferation was a stronger predictor compared with blood flow. Indirect, dynamic markers of angiogenesis from mpMRI and direct, static markers of angiogenesis from immunohistochemistry may aid in the stratification and therapy planning of prostate cancer patients.

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.

Comparison of Magnetic Resonance Imaging-Based Radiomics Features with Nomogram for Prediction of Prostate Cancer Invasion

Objective

To explore the value of the magnetic resonance imaging (MRI) radiomics model in predicting prostate cancer (PCa) invasion.

Methods

Clinical data of 86 pathologically confirmed PCa patients in our hospital were collected, including 44 cases in the invasive group and 42 cases in the non-invasive group. All patients underwent MRI examinations, and the same parameters were used. The lesion area was manually delineated and the radiomics features were extracted from T2WI. The radiomics signature based on LASSO regression was established. Besides, logistic regression was used to identify independent clinical predictors, and a combined model incorporating the radiomics signature and independent clinical risk factor was constructed. Finally, the receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA) was performed to compare the prediction efficiency and clinical benefit of each model.

Results

A total of 867 radiomics features were obtained, and six of them were incorporated into the radiomics model. Multivariate logistic regression analysis exhibited the Gleason score as an independent clinical risk factor for PCa invasion. ROC results showed that the performance of the radiomics model was comparable to that of the clinical-radiomics model in predicting PCa invasion, and it was better than that of the single Gleason score. DCA also confirmed the considerable clinical application value of the radiomics and the clinical-radiomics models.

Conclusion

As a simple, non-invasive, and efficient method, the radiomics model has important predictive value for PCa invasion.

Clinical value of minimum apparent diffusion coefficient for prediction of clinically significant prostate cancer in the transition zone

BACKGROUND

This study investigated the association between apparent diffusion coefficients in Prostate Imaging Reporting and Data System 4/5 lesions and clinically significant prostate cancer in the transition zone.

METHODS

We included 102 patients who underwent transperineal cognitive fusion targeted biopsy for Prostate Imaging Reporting and Data System 4/5 lesions in the transition zone between 2016 and 2020. The association between apparent diffusion coefficients and prostate cancers in the transition zone was analyzed.

RESULTS

The detection rate of prostate cancer was 49% (50/102), including clinically significant prostate cancer in 37.3% (38/102) of patients. The minimum apparent diffusion coefficients in patients with clinically significant prostate cancer were 494.5 ± 133.6 µm2/s, which was significantly lower than 653.8 ± 172.5 µm2/s in patients with benign histology or clinically insignificant prostate cancer. Age, prostate volume, transition zone volume, and mean and minimum apparent diffusion coefficients were associated with clinically significant prostate cancer. Multivariate analysis demonstrated that only the minimum apparent diffusion coefficient value (odds ratio: 0.994; p < 0.001) was an independent predictor of clinically significant prostate cancer. When the cutoff value of the minimum apparent diffusion coefficient was less than 595 µm2/s, indicating the presence of prostate cancer in the transition zone, the detection rate increased to 59.2% (29/49) in this cohort.

CONCLUSION

The minimum apparent diffusion coefficient provided additional value to indicate the presence of clinically significant prostate cancer in the transition zone. It may help consider the need for subsequent biopsies in patients with Prostate Imaging Reporting and Data System 4/5 lesions and an initial negative targeted biopsy.

Correlation between ADC, ADC ratio, and Gleason Grade group in prostate cancer patients undergoing radical prostatectomy: Retrospective multicenter study with different MRI scanners

Background

MRI is an important tool in the prostate cancer work-up, with special emphasis on the ADC sequence. This study aimed to investigate the correlation between ADC and ADC ratio compared to tumor aggressiveness determined by a histopathological examination after radical prostatectomy.

Methods

Ninety-eight patients with prostate cancer underwent MRI at five different hospitals prior to radical prostatectomy. Images were retrospectively analyzed individually by two radiologists. The ADC of the index lesion and reference tissues (contralateral normal prostatic, normal peripheral zone, and urine) was recorded. Absolute ADC and different ADC ratios were compared to tumor aggressivity according to the ISUP Gleason Grade Groups extracted from the pathology report using Spearman's rank correlation coefficient (ρ). ROC curves were used to evaluate the ability to discriminate between ISUP 1-2 and ISUP 3-5 and intra class correlation and Bland-Altman plots for interrater reliability.

Results

All patients had prostate cancer classified as ISUP grade ≥ 2. No correlation was found between ADC and ISUP grade. We found no benefit of using the ADC ratio over absolute ADC. The AUC for all metrics was close to 0.5, and no threshold could be extracted for prediction of tumor aggressivity. The interrater reliability was substantial to almost perfect for all variables analyzed.

Conclusions

ADC and ADC ratio did not correlate with tumor aggressiveness defined by ISUP grade in this multicenter MRI study. The result of this study is opposite to previous research in the field.

Correlation of multiparametric MRI with histopathological grade of peripheral zone prostate carcinoma

BACKGROUND

Prostatic cancer is the second most common malignant tumor in men. Preoperative grading of prostate cancer is important for its management. Our objective is to compare individual and combined detection rates of T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI), and magnetic resonance spectroscopy (MRS) for prostate cancer with histopathological diagnosis as its golden standard.

METHODS

Forty-four patients with positive digital rectal examination (DRE) findings and elevated prostate specific antigen (PSA), underwent multiparametric MRI (Mp-MRI). T2WI, DWI, DCE-MRI and MRS were done in all the patients. Cognitive magnetic resonance-transrectal ultrasound (MR-TRUS) fusion biopsy was done in all the patients. Sensitivity and specificity of T2WI, DWI, DCE-MRI, and Prostate Imaging - Reporting and Data System PIRADS version 2 was obtained. Apparent diffusion coefficient (ADC) value and choline/citrate ratio were obtained for each lesion and correlated with histopathological grade.

RESULTS

The mean age of the patients was 68.7 ± 10.1 years, and the mean serum PSA level was 58.1 ± 22.4 ng/dL. Of the 38 lesions in peripheral zone, 33 (87%) had histopathologically proven prostate cancer. T2WI had a sensitivity and specificity of 75.8% and 80% and DWI had a sensitivity and specificity of 90.9% and 80%, respectively, for detection of malignant prostatic lesion. The mean ADC values for prostate cancer, prostatitis, and normal prostatic parenchyma were 0.702 ± 0.094 × 10-3 mm2/sec, 0.959 ± 0.171 × 10-3 mm2/sec, and 1.31 ± 0.223 × 10-3 mm2/sec, respectively. Type 3 curve has lower sensitivity (45.5%) but high specificity (80%) for diagnosing prostate cancer.

CONCLUSION

DWI can be useful to differentiate benign from malignant prostatic lesions, and low-grade from high-grade prostate carcinoma. ADC value has a positive correlation with histopathological grade of prostate cancer.

Introducing new plan evaluation indices for prostate dose painting IMRT plans based on apparent diffusion coefficient images

BACKGROUND

Dose painting planning would be more complicated due to different levels of prescribed doses and more complex evaluation with conventional plan quality indices considering uniform dose prescription. Therefore, we tried to introduce new indices for evaluating the dose distribution conformity and homogeneity of treatment volumes based on the tumoral cell density and relative volumes of each lesion in prostate IMRT.

METHODS

CT and MRI scans of 20 male patients having local prostate cancer were used for IMRT DP planning. Apparent diffusion coefficient (ADC) images were imported to a MATLAB program to identify lesion regions based on ADC values automatically. Regions with ADC values lower than 750 mm²/s and regions with ADC values higher than 750 and less than 1500 mm²/s were considered CTV_70Gy (clinical tumor volume with 70 Gy prescribed dose), and CTV_60Gy, respectively. Other regions of the prostate were considered as CTV_53Gy. New plan evaluation indices based on evaluating the homogeneity (IOE(H)), and conformity (IOE(C)) were introduced, considering the relative volume of each lesion and cellular density obtained from ADC images. These indices were compared with conventional homogeneity and conformity indices and IOEs without considering cellular density. Furthermore, tumor control probability (TCP) was calculated for each patient, and the relationship of the assessed indices were evaluated with TCP values.

RESULTS

IOE (H) and IOE (C) with considering cellular density had significantly lower values compared to conventional indices and IOEs without considering cellular density. (P < 0.05). TCP values had a stronger relationship with IOE(H) considering cell density (R² = -0.415), and IOE(C) without considering cell density (R² = 0.624).

CONCLUSION

IOE plan evaluation indices proposed in this study can be used for evaluating prostate IMRT dose painting plans. We suggested to consider cell densities in the IOE(H) calculation formula and it's appropriate to calculate IOE(C) without considering cell density values.

MRI Radiomics in Prostate Cancer: A Reliability Study

BACKGROUND

Radiomics can provide quantitative features from medical imaging that can be correlated to clinical endpoints. The challenges relevant to robustness of radiomics features have been analyzed by many researchers, as it seems to be influenced by acquisition and reconstruction protocols, as well as by the segmentation of the region of interest (ROI). Prostate cancer (PCa) represents a difficult playground for this technique, due to discrepancies in the identification of the cancer lesion and the heterogeneity of the acquisition protocols. The aim of this study was to investigate the reliability of radiomics in PCa magnetic resonance imaging (MRI).

METHODS

A homogeneous cohort of patients with a PSA rise that underwent multiparametric MRI imaging of the prostate before biopsy was tested in this study. All the patients were acquired with the same MRI scanner, with a standardized protocol. The identification and the contouring of the region of interest (ROI) of an MRI suspicious cancer lesion were done by two radiologists with great experience in prostate cancer (>10 years). After the segmentation, the texture features were extracted with LIFEx. Texture features were then tested with intraclass coefficient correlation (ICC) analysis to analyze the reliability of the segmentation.

RESULTS

Forty-four consecutive patients were included in the present analysis. In 26 patients (59.1%), the prostate biopsy confirmed the presence of prostate cancer, which was scored as Gleason 6 in 6 patients (13.6%), Gleason 3 + 4 in 8 patients (18.2%), and Gleason 4 + 3 in 12 patients (27.3%). The reliability analysis conversely showed poor reliability in the majority of the MRI acquisition (61% in T2, 89% in DWI50, 44% in DWI400, and 83% in DWI1,500), with ADC acquisition only showing better reliability (poor reliability in only 33% of the texture features).

CONCLUSIONS

The low ratio of reliability in a monoinstitutional homogeneous cohort represents a significant alarm bell for the application of MRI radiomics in the field of prostate cancer. More work is needed in a clinical setting to further study the potential of MRI radiomics in prostate cancer.

mpMRI PI-RADS score 3 lesions diagnosed by reference vs affiliated radiological centers: Our experience in 950 cases

INTRODUCTION

The detection rate for clinically significant prostate cancer (csPCa) in men with mpMRI PI-RADS score 3 diagnosed by affiliated radiology centers vs radiological reference center was evaluated.

MATERIALS AND METHODS

From January 2017 to December 2020, 950 men (median age 64 years) underwent mpMRI for abnormal PSA values (median 6.3 ng/ml). Among the 950 patients who underwent mpMRI 500 were evaluated by a reference center and 450 by outpatient radiological affiliated centers. All the mpMRI index lesions characterized by a PI-RADS 3 underwent targeted cores combined with extended prostate biopsy. Two radiologists of the radiological reference center revised all the mpMRI lesions 3.

RESULTS

Overall, 361/950 (38%) patients had a mpMRI lesion PI-RADS score 3: 120/500 cases (24%) vs 241/450 cases (53.5%) were diagnosed by reference vs affiliated radiological centers. The detection rate for cT1c csPCa was equal to 26.7% (35/120 cases) vs 16.6% (40/241 cases) in men with PI-RADS 3 lesions diagnosed in the reference vs the affiliated radiological centers (p < 0.05). Among the 241 PI-RADS score 3 lesions diagnosed by affiliated radiological centers 86/241 (35.7%) and 36/241 (15%) were downgraded (PI-RADS scores < 3) and upgraded (PI-RADS score 4) by the dedicated radiologists of the reference center.

CONCLUSIONS

In our series, about 35% and 15% of PI-RADS score 3 lesions diagnosed by affiliated radiological centers were downgraded and upgraded when revised by experencied radiologists, therefore a second opinion is mandatory especially in men enrolled in active surveillance protocols in whom mpMRI is recommended to reduce the number of scheduled repeated prostate biopsies.

Prostate cancer GTV delineation with biparametric MRI and 68Ga-PSMA-PET: comparison of expert contours and semi-automated methods

OBJECTIVE

The optimal method for delineation of dominant intraprostatic lesions (DIL) for targeted radiotherapy dose escalation is unclear. This study evaluated interobserver and intermodality variability of delineations on biparametric MRI (bpMRI), consisting of T₂ weighted (T₂W) and diffusion-weighted (DWI) sequences, and ⁶⁸Ga-PSMA-PET/CT; and compared manually delineated GTV contours with semi-automated segmentations based on quantitative thresholding of intraprostatic apparent diffusion coefficient (ADC) and standardised uptake values (SUV).

METHODS

16 patients who had bpMRI and PSMA-PET scanning performed prior to any treatment were eligible for inclusion. Four observers (two radiation oncologists, two radiologists) manually delineated the DIL on: (1) bpMRI (GTV_MRI), (2) PSMA-PET (GTV_PSMA) and (3) co-registered bpMRI/PSMA-PET (GTV_Fused) in separate sittings. Interobserver, intermodality and semi-automated comparisons were evaluated against consensus Simultaneous Truth and Performance Level Estimation (STAPLE) volumes, created from the relevant manual delineations of all observers with equal weighting. Comparisons included the Dice Similarity Coefficient (DSC), mean distance to agreement (MDA) and other metrics.

RESULTS

Interobserver agreement was significantly higher (p < 0.05) for GTV_PSMA (DSC: 0.822, MDA: 1.12 mm) and GTV_Fused (DSC: 0.787, MDA: 1.34 mm) than for GTV_MRI (DSC: 0.705, MDA 2.44 mm). Intermodality agreement between GTV_MRI and GTV_PSMA was low (DSC: 0.440, MDA: 4.64 mm). Agreement between semi-automated volumes and consensus GTV was low for MRI (DSC: 0.370, MDA: 8.16 mm) and significantly higher for PSMA-PET (0.571, MDA: 4.45 mm, p < 0.05).

CONCLUSION

⁶⁸Ga-PSMA-PET appears to improve interobserver consistency of DIL localisation vs bpMRI and may be more viable for simple quantitative delineation approaches; however, more sophisticated approaches to semi-automatic delineation factoring for patient- and disease-related heterogeneity are likely required.

ADVANCES IN KNOWLEDGE

This is the first study to evaluate the interobserver variability of prostate GTV delineations with co-registered bpMRI and ⁶⁸Ga-PSMA-PET.

Radio-pathomic mapping model generated using annotations from five pathologists reliably distinguishes high-grade prostate cancer

Purpose: Our study predictively maps epithelium density in magnetic resonance imaging (MRI) space while varying the ground truth labels provided by five pathologists to quantify the downstream effects of interobserver variability. Approach: Clinical imaging and postsurgical tissue from 48 recruited prospective patients were used in our study. Tissue was sliced to match the MRI orientation and whole-mount slides were stained and digitized. Data from 28 patients ( n = 33 slides) were sent to five pathologists to be annotated. Slides from the remaining 20 patients ( n = 123 slides) were annotated by one of the five pathologists. Interpathologist variability was measured using Krippendorff's alpha. Pathologist-specific radiopathomic mapping models were trained using a partial least-squares regression using MRI values to predict epithelium density, a known marker for disease severity. An analysis of variance characterized intermodel means difference in epithelium density. A consensus model was created and evaluated using a receiver operator characteristic classifying high grade versus low grade and benign, and was statistically compared to apparent diffusion coefficient (ADC). Results: Interobserver variability ranged from low to acceptable agreement (0.31 to 0.69). There was a statistically significant difference in mean predicted epithelium density values ( p < 0.001 ) between the five models. The consensus model outperformed ADC (areas under the curve = 0.80 and 0.71, respectively, p < 0.05 ). Conclusion: We demonstrate that radiopathomic maps of epithelium density are sensitive to the pathologist annotating the dataset; however, it is unclear if these differences are clinically significant. The consensus model produced the best maps, matched the performance of the best individual model, and outperformed ADC.

Prostate Cancer Detection in Screening Using Magnetic Resonance Imaging and Artificial Intelligence

Background:

Prostate cancer is a leading cause of death among men who do not participate in a screening programme. MRI forms a possible alternative for prostate analysis of a higher level of sensitivity than the PSA test or biopsy. Magnetic resonance is a non-invasive method and magnetic resonance tomography produces a large amount of data. If a screening programme were implemented, a dramatic increase in radiologist workload and patient waiting time will follow. Computer Aided-Diagnose (CAD) could assist radiologists to decrease reading times and cost, and increase diagnostic effectiveness. CAD mimics radiologist and imaging guidelines to detect prostate cancer.

Aim:

The purpose of this study was to analyse and describe current research in MRI prostate examination with the aid of CAD. The aim was to determine if CAD systems form a reliable method for use in prostate screening.

Methods:

This study was conducted as a systematic literature review of current scientific articles. Selection of articles was carried out using the “Preferred Reporting Items for Systematic Reviews and for Meta-Analysis” (PRISMA). Summaries were created from reviewed articles and were then categorised into relevant data for results.

Results:

CAD has shown that its capability concerning sensitivity or specificity is higher than a radiologist. A CAD system can reach a peak sensitivity of 100% and two CAD systems showed a specificity of 100%. CAD systems are highly specialised and chiefly focus on the peripheral zone, which could mean missing cancer in the transition zone. CAD systems can segment the prostate with the same effectiveness as a radiologist.

Conclusion:

When CAD analysed clinically-significant tumours with a Gleason score greater than 6, CAD outperformed radiologists. However, their focus on the peripheral zone would require the use of more than one CAD system to analyse the entire prostate.

Early Second Round Targeted Biopsy of PI-RADS Score 3 or 4 in 256 Men With Persistent Suspicion of Prostate Cancer

BACKGROUND/AIM

The aim of the study was to determine the rate of clinically significant prostate cancer (csPCa) cases in men submitted to early second round mpMRI/TRUS (multiparametric magnetic resonance imaging/transrectal ultrasound) fusion biopsy (TPBx).

MATERIALS AND METHODS

From January 2016 to December 2018, 256 men with a PI-RADS (Prostate Imaging-Reporting and Data System) score 3 (80 cases) or 4 (176 cases) and negative repeat transperineal saturation biopsy plus TPBx, underwent a new TPBx (four cores) for the persistent clinical suspicion of cancer. The accuracy of mpMRI ADC (apparent diffusion coefficient) values in the diagnosis of csPCa were evaluated.

RESULTS

Overall detection rate of csPCa was equal to 10.1% (26/256 cases): 2.5% (2/80) versus 13.6% (24/176) had a PI-RADS score equal to 3 versus 4, respectively. The presence of csPCa was significantly correlated with an ADC value of 0.747×10^-3 mm²/sec.

CONCLUSION

A negative TBPx missed a csPCa in 13.6% of PI-RADS score 4 that was diagnosed by an early second round TBPx; the evaluation of ADC maps could select mpMRI lesions deserving a repeat TPBx.

DW-MRI for esophageal squamous cell carcinoma, correlations between ADC values with histologic differentiation and VEGF expression: A retrospective study

The aim of the present study was to assess the correlations between diffusion-weighted magnetic resonance imaging (DW-MRI) features with the histologic differentiation and the expression of vascular endothelial growth factor (VEGF) in esophageal squamous cell carcinoma (ESCC). A total of 52 patients with ESCC included in the present study received radiotherapy, and all patients underwent contrast enhanced MRI and DW-MRI prior to and following radiotherapy. The diffusion sensitivity coefficient (b value) was set as 800 s/mm². Apparent diffusion coefficient (ADC) values were automatically computed. VEGF expression was evaluated by immunohistochemical staining. The results demonstrated that the pathological grading of ESCC was positively correlated with ADC values (r=0.635, P=0.0007), and the VEGF expression was inversely correlated with ADC values (r=−0.321, P=0.008). However, no correlation was identified between the pathological grading and the VEGF expression (r=0.178, P=0.284). All patients were categorized as complete response (CR) or partial response (PR) and the ADC values were increased significantly following radiotherapy. The mean ADC values in the CR group were higher than the PR group prior to radiotherapy (t=5.156, P=0.0004). Therefore, we concluded that the DWI with ADC value measurement may represent the grade of tumor histologic differentiation and the degree of VEGF expression, and may also serve as a useful marker to predict radiotherapy and anti-VEGF response in ESCC. ADC value may be a substitution for assessing tumor angiogenesis and novel prognostic factor and contribute to the treatment of ESCC.

Quantitative characterisation of clinically significant intra-prostatic cancer by prostate-specific membrane antigen (PSMA) expression and cell density on PSMA-11

OBJECTIVES

To quantitatively characterize clinically significant intra-prostatic cancer (IPC) by prostate-specific membrane antigen (PSMA) expression and cell density on PSMA-11 positron emission tomography/magnetic resonance (PET/MR).

METHODS

Retrospective study approved by the institutional review board with informed written consent obtained. Patients with a solitary, biopsy-proven prostate cancer, Gleason score (GS) ≥7, presenting for initial evaluation by PET/computerised tomography (PET/CT), underwent early prostate PET/MR immediately after PSMA-11 tracer injection. PET/MR [MRI-based attenuation correction (MRAC)] and PET/CT [CT-based AC (CTAC)] maximal standardised uptake value (SUVmax) and minimal and mean apparent diffusion coefficient (ADCmin, ADCmean; respectively) in normal prostatic tissue (NPT) were compared to IPC area. The relationship between SUVmax, ADCmin and ADCmean measurements was obtained.

RESULTS

Twenty-two patients (mean age 69.5±5.0 years) were included in the analysis. Forty-four prostate areas were evaluated (22 IPC and 22 NPT). Median MRAC SUVmax of NPT was significantly lower than median MRAC SUVmax of IPC (p < 0.0001). Median ADCmin and ADCmean of NPT was significantly higher than median ADCmin and ADCmean of IPC (p < 0.0001). A very good correlation was found between MRAC SUVmax with CTAC SUVmax (rho = -0.843, p < 0.0001). A good inverse relationship was found between MRAC SUVmax and CTAC SUVmax with ADCmin (rho = -0.717, p < 0.0001 and -0.740, p < 0.0001; respectively; Z = 0.22, p = 0.82, NS) and with MRAC SUVmax and ADCmean (rho = -0.737, p < 0.0001).

CONCLUSIONS

PET/MR SUVmax, ADCmin and ADCmean are distinct biomarkers able to differentiate between IPC and NPT in naïve prostate cancer patients with GS ≥ 7.

KEY POINTS

• PSMA PET/MR metrics differentiate between normal and tumoural prostatic tissue. • A multi-parametric approach combining molecular and anatomical information might direct prostate biopsy. • PSMA PET/MR metrics are warranted for radiomics analysis.