Correlation between ADC values and Gleason score in evaluation of prostate cancer: multicentre experience and review of the literature

T1-mapping characterization of two tumor types

T1 mapping is a quantitative method to characterize tissues with magnetic resonance imaging in a quick and efficient manner. It utilizes the relaxation rate of protons to depict the underlying structures within the imaging frame. While T1-mapping techniques are used with some frequency in areas such as cardiac imaging, their application for understanding malignancies and identifying tumor structures has yet to be thoroughly investigated. Utilizing a saturation recovery method to acquire T1 maps for two different tumor models has revealed that longitudinal relaxation mapping is sensitive enough to distinguish between normal and malignant tissue. This is seen even with decreased signal/noise ratios using small voxel sizes to obtain high-resolution images. In both tumor models, it was revealed that relaxation mapping recorded significantly different relaxation values between regions encapsulating the tumor, muscle, kidney, or spleen, as well as between the cell lines themselves. This indicates a potential future application of relaxation mapping as a method to fingerprint various stages of tumor development and may prove a useful measure to identify micro-metastases.

Diffusion kurtosis imaging and standard diffusion imaging in the magnetic resonance imaging assessment of prostate cancer

Background and Objective

In recent years, magnetic resonance imaging (MRI) has shown excellent results in the study of the prostate gland. MRI has indeed shown to be advantageous in the prostate cancer (PCa) detection, as in guiding targeting biopsy, improving its diagnostic yield. Although current acquisition protocols provide for multiparametric acquisition, recent evidence has shown that biparametric protocols can be non-inferior in PCa detection. Diffusion-weighted imaging (DWI) sequence, in particular, plays a key role, particularly in the peripheral zone which accounts for the larger part of the prostate. High b-values are generally recommended, although with the possibility of obtaining non-Gaussian diffusion effects, which requires a more sophisticated model for the analysis, namely through the diffusion kurtosis imaging (DKI). Purpose of this narrative review was to analyze the current applications and clinical evidence regarding the use of DKI with a main focus on PCa detection, also in comparison with DWI.

Methods

This narrative review synthesized the findings of literature retrieved from main researches, narrative and systematic reviews, and meta-analyses obtained from PubMed.

Key Content and Findings

DKI analyses the non-Gaussian water diffusivity and describe the effect of signal intensity decay related to high b-value through two main metrics (Dapp and Kapp). Differently from DWI-apparent diffusion coefficient (DWI-ADC) which reflects only water restriction outside of cells, DKI metrics are supposed to represent also the direct interaction of water molecules with cell membranes and intracellular compounds. This review describes current evidence on ADC and DKI metrics in clinical imaging, and finally collect the results derived from the main articles focused on DWI and DKI models in detecting PCa.

Conclusions

DKI advantages, compared to conventional ADC analysis, still remain controversial. Wider application and greater technical knowledge of DKI, however, may help in proving its intrinsic validity in the field of oncology and therefore in the study of clinically significant PCa. Finally, a deep understanding of DKI is important for radiologists to better understand what Kapp and Dapp mean in the context of different cancer and how these metrics may vary specifically in PCa imaging.

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.

Dynamic Contrast-Enhanced Study in the mpMRI of the Prostate-Unnecessary or Underutilised? A Narrative Review

The aim of this review is to summarise recent scientific literature regarding the clinical use of DCE-MRI as a component of multiparametric resonance imaging of the prostate. This review presents the principles of DCE-MRI acquisition and analysis, the current role of DCE-MRI in clinical practice with special regard to its role in presently available categorisation systems, and an overview of the advantages and disadvantages of DCE-MRI described in the current literature. DCE-MRI is an important functional sequence that requires intravenous administration of a gadolinium-based contrast agent and gives information regarding the vascularity and capillary permeability of the lesion. Although numerous studies have confirmed that DCE-MRI has great potential in the diagnosis and monitoring of prostate cancer, its role is still inadequate in the PI-RADS categorisation. Moreover, there have been numerous scientific discussions about abandoning the intravenous application of gadolinium-based contrast as a routine part of MRI examination of the prostate. In this review, we summarised the recent literature on the advantages and disadvantages of DCE-MRI, focusing on an overview of currently available data on bpMRI and mpMRI, as well as on studies providing information on the potential better usability of DCE-MRI in improving the sensitivity and specificity of mpMRI examinations of the prostate.

AI-predicted mpMRI image features for the prediction of clinically significant prostate cancer

PURPOSE

To evaluate the feasibility of using mpMRI image features predicted by AI algorithms in the prediction of clinically significant prostate cancer (csPCa).

MATERIALS AND METHODS

This study analyzed patients who underwent prostate mpMRI and radical prostatectomy (RP) at the Affiliated Hospital of Jiaxing University between November 2017 and December 2022. The clinical data collected included age, serum prostate-specific antigen (PSA), and biopsy pathology. The reference standard was the prostatectomy pathology, and a Gleason Score (GS) of 3 + 3 = 6 was considered non-clinically significant prostate cancer (non-csPCa), while a GS ≥ 3 + 4 was considered csPCa. A pre-trained AI algorithm was used to extract the lesion on mpMRI, and the image features of the lesion and the prostate gland were analyzed. Two logistic regression models were developed to predict csPCa: an MR model and a combined model. The MR model used age, PSA, PSA density (PSAD), and the AI-predicted MR image features as predictor variables. The combined model used biopsy pathology and the aforementioned variables as predictor variables. The model's effectiveness was evaluated by comparing it to biopsy pathology using the area under the curve (AUC) of receiver operation characteristic (ROC) analysis.

RESULTS

A total of 315 eligible patients were enrolled with an average age of 70.8 ± 5.9. Based on RP pathology, 18 had non-csPCa, and 297 had csPCa. PSA, PSAD, biopsy pathology, and ADC value of the prostate outside the lesion (ADCprostate) varied significantly across different ISUP grade groups of RP pathology (P < 0.001). Other clinical variables and image features did not vary significantly across different ISUP grade groups (P > 0.05). The MR model included PSAD, the ratio of ADC value between the lesion and the prostate outside the lesion (ADClesion/prostate), the signal intensity ratio of DWI between the lesion and the prostate outside the lesion (DWIlesion/prostate), and the ratio of DWIlesion/prostate to ADClesion/prostate. The combined model included biopsy pathology, ADClesion/prostate, mean signal intensity of the lesion on DWI (DWIlesion), DWI signal intensity of the prostate outside the lesion (DWIprostate), and signal intensity ratio of DWI between the lesion and the prostate outside the lesion (DWIlesion/prostate). The AUC of the MR model (0.830, 95% CI 0.743, 0.916) was not significantly different from that of biopsy pathology (0.820, 95% CI 0.728, 0.912, P = 0.884). The AUC of the combined model (0.915, 95% CI 0.849, 0.980) was higher than that of the biopsy pathology (P = 0.042) and MR model (P = 0.031).

CONCLUSION

The aggressiveness of prostate cancer can be effectively predicted using AI-extracted image features from mpMRI images, similar to biopsy pathology. The prediction accuracy was improved by combining the AI-extracted mpMRI image features with biopsy pathology, surpassing the performance of biopsy pathology alone.

Quantitative Evaluation of Apparent Diffusion Coefficient Values, ISUP Grades and Prostate-Specific Antigen Density Values of Potentially Malignant PI-RADS Lesions

The aim of this study was to demonstrate the correlation between ADC values and the ADC/PSAD ratio for potentially malignant prostate lesions classified into ISUP grades and to determine threshold values to differentiate benign lesions (noPCa), clinically insignificant (nsPCa) and clinically significant prostate cancer (csPCa). We enrolled a total of 403 patients with 468 prostate lesions, of which 46 patients with 50 lesions were excluded for different reasons. Therefore, 357 patients with a total of 418 prostate lesions remained for the final evaluation. For all lesions, ADC values were measured; they demonstrated a negative correlation with ISUP grades (p < 0.001), with a significant difference between csPCa and a combined group of nsPCa and noPCa (ns-noPCa, p < 0.001). The same was true for the ADC/PSAD ratio, but only the ADC/PSAD ratio proved to be a significant discriminator between nsPCa and noPCa (p = 0.0051). Using the calculated threshold values, up to 31.6% of biopsies could have been avoided. Furthermore, the ADC/PSAD ratio, with the ability to distinguish between nsPCa and noPCa, offers possible active surveillance without prior biopsy.

Quantitative ADC: An Additional Tool in the Evaluation of Prostate Cancer?

Prostate cancer is one of the most common tumors among the male population. Magnetic resonance imaging (MRI), standardized by the PI-RADS version 2.1 scoring system, has a fundamental role in detecting prostate cancer and evaluating its aggressiveness. Diffusion-weighted imaging sequences and apparent diffusion coefficient values, in particular, are considered fundamental for the detection and characterization of lesions. In 2016 the International Society of Urological Pathology introduced a new anatomopathological 5-grade scoring system for prostate cancer. The aim of this study is to evaluate the correlation between quantitative apparent diffusion coefficient values (ADC) derived from diffusion-weighted imaging (DWI) sequences and the International Society of Urological Pathology (ISUP) and PI-RADS groups. Our retrospective study included 143 patients with 154 suspicious lesions, observed on prostate magnetic resonance imaging and compared with the histological results of the biopsy. We observed that ADC values can aid in discriminating between not clinically significant (ISUP 1) and clinically significant (ISUP 2-5) prostate cancers. In fact, ADC values were lower in ISUP 5 lesions than in negative lesions. We also found a correlation between ADC values and PI-RADS groups; we noted lower ADC values in the PI-RADS 5 and PI-RADS 4 groups than in the PI-RADS 3 group. In conclusion, quantitative apparent diffusion coefficient values can be useful to assess the aggressiveness of prostate cancer.

Prostate Region-Wise Imaging Biomarker Profiles for Risk Stratification and Biochemical Recurrence Prediction

BACKGROUND

Identifying prostate cancer (PCa) patients with a worse prognosis and a higher risk of biochemical recurrence (BCR) is essential to guide treatment choices. Here, we aimed to identify possible imaging biomarker (perfusion/diffusion + radiomic features) profiles extracted from MRIs that were able to discriminate patients according to their risk or the occurrence of BCR 10 years after diagnosis, as well as to evaluate their predictive value with or without clinical data.

METHODS

Patients with localized PCa receiving neoadjuvant androgen deprivation therapy and radiotherapy were retrospectively evaluated. Imaging features were extracted from MRIs for each prostate region or for the whole gland. Univariate and multivariate analyses were conducted.

RESULTS

128 patients (mean [range] age, 71 [50-83] years) were included. Prostate region-wise imaging biomarker profiles mainly composed of radiomic features allowed discriminating risk groups and patients experiencing BCR. Heterogeneity-related radiomic features were increased in patients with worse prognosis and with BCR. Overall, imaging biomarkers profiles retained good predictive ability (AUC values superior to 0.725 in most cases), which generally improved when clinical data were included (particularly evident for the prediction of the BCR, with AUC values ranging from 0.841 to 0.877 for combined models and sensitivity values above 0.960) and when models were built per prostate region vs. the whole gland.

CONCLUSIONS

Prostate region-aware imaging profiles enable identification of patients with worse prognosis and with a higher risk of BCR, retaining higher predictive values when combined with clinical variables.

Correlation of mean apparent diffusion coefficient (ADC) and maximal standard uptake value (SUVmax) evaluated by diffusion-weighted MRI and 18F-FDG-PET/CT in children with Hodgkin lymphoma: a feasibility study

BACKGROUND

The objective was to analyse if magnetic resonance imaging (MRI) can act as a non-radiation exposure surrogate for (18)F-Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) in children with histologically confirmed Hodgkin lymphoma (HL) before treatment. This was done by analysing a potential correlation between apparent diffusion coefficient (ADC) in MRI and the maximum standardized uptake value (SUVmax) in FDG-PET/CT.

PATIENTS AND METHODS

Seventeen patients (six female, eleven male, median age: 16 years, range: 12-20 years) with histologically confirmed HL were retrospectively analysed. The patients underwent both MRI and (18)F-FDG PET/CT before the start of treatment. (18)F-FDG PET/CT data and correlating ADC maps in MRI were collected. For each HL-lesion two readers independently evaluated the SUVmax and correlating meanADC.

RESULTS

The seventeen patients had a total of 72 evaluable lesions of HL and there was no significant difference in the number of lesions between male and female patients (median male: 15, range: 12-19 years, median female: 17 range: 12-18 years, p = 0.021). The mean duration between MRI and PET/CT was 5.9 ± 5.3 days. The inter-reader agreement as assessed by the intraclass correlation coefficient (ICC) was excellent (ICC = 0.98, 95% CI: 0.97-0.99). The correlated SUVmax and meanADC of all 17 patients (ROIs n = 72) showed a strong negative correlation of -0.75 (95% CI: -0.84, - -0.63, p = 0.001). Analysis revealed a difference in the correlations of the examination fields. The correlated SUVmax and meanADC showed a strong correlation at neck and thoracal examinations (neck: -0.83, 95% CI: -0.93, - -0.63, p < 0.0001, thoracal: -0.82, 95% CI: -0.91, - -0.64, p < 0.0001) and a fair correlation at abdominal examinations of -0.62 (95% CI: -0.83, - -0.28, p = 0.001).

CONCLUSIONS

SUVmax and meanADC showed a strong negative correlation in paediatric HL lesions. The assessment seemed robust according to inter-reader agreements. Our results suggest that ADC maps and meanADC have the potential to replace PET/CT in the analysis of disease activity in paediatric Hodgkin lymphoma patients. This may help reduce the number of PET/CT examinations and decrease radiation exposure to children.

Prostate and metastasis diffusion volume based on apparent diffusion coefficient as a prognostic factor in Hormone-naïve prostate Cancer

In this study, to assess the utility of whole-body DWI (WB-DWI) as an imaging biomarker for metastatic hormone-naïve prostate cancer (mHNPC), we evaluated tumor diffusion volume based on apparent diffusion coefficient (ADC) values. WB-DWI results obtained from 62 mHNPC patients were evaluated in this retrospective analysis. The association with castration resistant-free survival (CFS) was evaluated for both prostate and metastatic tumor diffusion volume (pDV and mDV, respectively) based on WB-DWI. The usefulness of pDV and mDV based on ADC values to predict CFS was also examined. During the follow-up period, 22 patients progressed to castration-resistant prostate cancer, and the median CFS was 42.6 months. The median mDV and pDV were 6.7 and 12.6 mL, respectively. mDV was a significant predictor of CFS (hazard ratio [HR]: 2.75; p = 0.022), while pDV was not significant. When DV was divided into groups by ADC values (× 10^- 3 mm²/s) of 0.4-1.0 and 1.0-1.8 (× 10^- 3 mm²/s), mDV with ADC values (× 10^- 3 mm²/s) of 0.4-1.0 (mDV_0.4-1.0) showed a more favorable association with CFS compared to total mDV. On multivariate analysis, mDV_0.4-1.0 and Gleason grade group had a statistically significant association with CFS (HR: 4.0; p = 0.004, and HR: 3.4; p = 0.006, respectively), while pDV with ADC values (× 10^- 3 mm²/s) of 0.4-1.0 did not have a significant association. mDV is useful for predicting CFS in mHNPC patients. mDV may be a better imaging biomarker when based on ADC values.

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.

Automated Patient-level Prostate Cancer Detection with Quantitative Diffusion Magnetic Resonance Imaging

Background

Multiparametric magnetic resonance imaging (mpMRI) improves detection of clinically significant prostate cancer (csPCa), but the subjective Prostate Imaging Reporting and Data System (PI-RADS) system and quantitative apparent diffusion coefficient (ADC) are inconsistent. Restriction spectrum imaging (RSI) is an advanced diffusion-weighted MRI technique that yields a quantitative imaging biomarker for csPCa called the RSI restriction score (RSI_rs).

Objective

To evaluate RSI_rs for automated patient-level detection of csPCa.

Design setting and participants

We retrospectively studied all patients (n = 151) who underwent 3 T mpMRI and RSI (a 2-min sequence on a clinical scanner) for suspected prostate cancer at University of California San Diego during 2017-2019 and had prostate biopsy within 180 d of MRI.

Intervention

We calculated the maximum RSI_rs and minimum ADC within the prostate, and obtained PI-RADS v2.1 from medical records.

Outcome measurements and statistical analysis

We compared the performance of RSI_rs, ADC, and PI-RADS for the detection of csPCa (grade group ≥2) on the best available histopathology (biopsy or prostatectomy) using the area under the curve (AUC) with two-tailed α = 0.05. We also explored whether the combination of PI-RADS and RSI_rs might be superior to PI-RADS alone and performed subset analyses within the peripheral and transition zones.

Results and limitations

AUC values for ADC, RSI_rs, and PI-RADS were 0.48 (95% confidence interval: 0.39, 0.58), 0.78 (0.70, 0.85), and 0.77 (0.70, 0.84), respectively. RSI_rs and PI-RADS were each superior to ADC for patient-level detection of csPCa (p < 0.0001). RSI_rs alone was comparable with PI-RADS (p = 0.8). The combination of PI-RADS and RSI_rs had an AUC of 0.85 (0.78, 0.91) and was superior to either PI-RADS or RSI_rs alone (p < 0.05). Similar patterns were seen in the peripheral and transition zones.

Conclusions

RSI_rs is a promising quantitative marker for patient-level csPCa detection, warranting a prospective study.

Patient summary

We evaluated a rapid, advanced prostate magnetic resonance imaging technique called restriction spectrum imaging to see whether it could give an automated score that predicted the presence of clinically significant prostate cancer. The automated score worked about as well as expert radiologists' interpretation. The combination of the radiologists' scores and automated score might be better than either alone.

Dynamic Contrast Enhanced Study in Multiparametric Examination of the Prostate—Can We Make Better Use of It?

We sought to investigate whether quantitative parameters from a dynamic contrast-enhanced study can be used to differentiate cancer from normal tissue and to determine a cut-off value of specific parameters that can predict malignancy more accurately, compared to the obturator internus muscle as a reference tissue. This retrospective study included 56 patients with biopsy proven prostate cancer (PCa) after multiparametric magnetic resonance imaging (mpMRI), with a total of 70 lesions; 39 were located in the peripheral zone, and 31 in the transition zone. The quantitative parameters for all patients were calculated in the detected lesion, morphologically normal prostate tissue and the obturator internus muscle. Increase in the K_trans value was determined in lesion-to-muscle ratio by 3.974368, which is a cut-off value to differentiate between prostate cancer and normal prostate tissue, with specificity of 72.86% and sensitivity of 91.43%. We introduced a model to detect prostate cancer that combines K_trans lesion-to-muscle ratio value and iAUC lesion-to-muscle ratio value, which is of higher accuracy compared to individual variables. Based on this model, we identified the optimal cut-off value with 100% sensitivity and 64.28% specificity. The use of quantitative DCE pharmacokinetic parameters compared to the obturator internus muscle as reference tissue leads to higher diagnostic accuracy for prostate cancer detection.

Comparison of diagnostic performance and inter-reader agreement between PI-RADS v2.1 and PI-RADS v2: systematic review and meta-analysis

OBJECTIVES

To perform a systematic review and meta-analysis comparing diagnostic performance and inter reader agreement between PI-RADS v. 2.1 and PI-RADS v. 2 in the detection of clinically significant prostate cancer (csPCa).

METHODS

A systematic review was performed, searching the major biomedical databases (Medline, Embase, Scopus), using the keywords "PIRADS 2.1" or "PI RADS 2.1" or "PI-RADS 2.1". Studies reporting on head-to-head diagnostic comparison between PI-RADS v. 2.1 and v. 2 were included. Pooled sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were compared between PI-RADS v. 2.1 and v. 2. Summary receiver operator characteristic graphs were plotted. Analysis was performed for whole gland, and pre-planned subgroup analysis was performed by tumour location (whole gland vs transition zone (TZ)), high b-value DWI (b-value ≥1400 s/mm²), and reader experience (<5 years vs ≥5 years with prostate MRI interpretation). Inter-reader agreement and pooled rates of csPCa for PI-RADS 1-3 lesions were compared between PI-RADS v. 2.1 and v. 2. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool v. 2 (QUADAS-2).

RESULTS

Eight studies (1836 patients, 1921 lesions) were included. Pooled specificity for PI-RADS v. 2.1 was significantly lower than PI-RADS v. 2 for whole gland (0.62 vs 0.66, p = 0.02). Pooled sensitivities, PPVs and NPVs were not significantly different (p = 0.17, 0.31, 0.41). Pooled specificity for PI-RADS v. 2.1 was significantly lower than PI-RADS v. 2 for TZ only (0.67 vs 0.72, p = 0.01). Pooled sensitivities, PPVs and NPVs were not significantly different (p = 0.06, 0.36, 0.17). Amongst studies utilising diffusion-weighted imaging with highest b-value of ≥1400 s/mm², pooled sensitivities, specificities, PPVs and NPVs were not significantly different (p = 0.52, 0.4, 0.5, 0.47). There were no significant differences in pooled sensitivities, specificities, PPVs and NPVs between PI-RADS v. 2.1 and PI-RADS v. 2 for less-experienced readers (p = 0.65, 0.37, 0.65, 0.81) and for more experienced readers (p = 0.57, 0.90, 0.91, 0.65). For PI-RADS v. 2.1 alone, there were no significant differences in pooled sensitivity, specificity, PPV and NPV between less and more experienced readers (p = 0.38, 0.70, 1, 0.48). Inter-reader agreement was moderate to substantial for both PI-RADS v. 2.1 and v. 2. There were no significant differences between pooled csPCa rates between PI-RADS v. 2.1 and v. 2 for PI-RADS 1-2 lesions (6.6% vs  7.3%, p = 0.53), or PI-RADS 3 lesions (24.1% vs  26.8%, p = 0.28).

CONCLUSIONS

Diagnostic performance and inter-reader agreement for PI-RADS v. 2.1 is comparable to PI-RADS v. 2, however the significantly lower specificity of PI-RADS v. 2.1 may result in increased number of unnecessary biopsies.

ADVANCES IN KNOWLEDGE

1. Compared to PI-RADS v. 2, PI-RADS v. 2.1 has a non-significantly higher sensitivity but a significantly lower specificity for detection of clinically significant prostate cancer.2. PI-RADS v. 2.1 could potentially result in considerable increase in number of negative targeted biopsy rates for PI-RADS 3 lesions, which could have been potentially avoided.

Longitudinal evaluation of apparent diffusion coefficient values as a predictor of Prostate Cancer Research International Active Surveillance reclassification

PURPOSE

This study aimed to evaluate the effectiveness of apparent diffusion coefficient (ADC) parameters in distinguishing between Prostate Cancer Research International Active Surveillance (PRIAS) non-reclassification and reclassification groups during active surveillance (AS) of prostate cancer.

METHODS

We included 55 patients who fulfilled the PRIAS criteria and underwent ≥ 2 magnetic resonance imaging (MRI) including diffusion-weighted imaging with an interval of ≤ 3 years between baseline and second MRI. A mono-exponential fitting model was used to automatically create ADC maps with minimum b-values of 0 and maximum of 2000 s/mm². For detectable lesions on ADC maps, the lesions were manually segmented on each slice of the ADC maps. For undetectable lesions, the corresponding normal-appearing zone of the lobe on each slice of ADC maps was segmented. The ADC data for each slice were summed to obtain the 25th, 50th, and 75th percentile ADC values of the histogram at baseline and second MRI. These ADC parameters at baseline and second MRI, and the changes of ADC parameters from baseline to second MRI were compared between PRIAS non-reclassification and reclassification groups.

RESULTS

The PRIAS reclassification group had significantly lower 25th, 50th, and 75th percentile ADC values at second MRI compared to the non-reclassification group. The non-reclassification group had significantly lower changes in ADC values in these percentiles compared to the reclassification group.

CONCLUSION

The ADC parameters at second MRI and the changes from baseline to second MRI may be effective distinguishing factors between PRIAS non-reclassification and reclassification groups.

The impact of multiparametric MRI features to identify the presence of prevalent cribriform pattern in the peripheral zone tumors

PURPOSE

To assess the role of the multiparametric Magnetic Resonance Imaging (mpMRI) in predicting the cribriform pattern in both the peripheral and transition zones (PZ and TZ) clinically significant prostate cancers (csPCas).

MATERIAL AND METHODS

We retrospectively evaluated 150 patients who underwent radical prostatectomy for csPCa and preoperative mpMRI. Patients with negative (n = 25) and positive (n = 125) mpMRI, stratified according to the presence of prevalent cribriform pattern (PCP, ≥ 50%) and non-PCP (< 50%) at specimen, were included. Difference between the two groups were evaluated. Multivariate logistic regression was used to identify predictors of PCP among mpMRI parameters. The receiver operating characteristic (ROC) analysis was performed to evaluate the area under the curve (AUC) of apparent diffusion coefficient (ADC) and ADC ratio in detecting lesions harboring PCP.

RESULTS

Considering 135 positive lesions at the mpMRI, 30 (22.2%) and 105 (77.8%) harbored PCP and non-PCP PCa. The PCP lesions had more frequently nodular morphology (83.3% vs 62.9%; p = 0.04) and significantly lower mean ADC value (0.87 ± 0.16 vs 0.95 ± 0.18; p = 0.03) and ADC ratio (0.52 ± 0.09 vs 0.60 ± 0.14; p = 0.003) when compared with non-PCP lesions. At univariate and multivariate analyses, mean ADC and ADC ratio resulted as independent predictors of the presence of the PCP of the PZ tumors(OR: 0.025; p = 0.03 and OR: 0.001; p = 0.004, respectively). At the ROC analysis, the AUC of mean ADC and ADC ratio to predict the presence of PCP in patients with PZ suspicious lesion at the mpMRI were 0.69 (95% CI 0.56-0.81P, p = 0.003) and 0.72 (95% CI 0.62-0.82P, p = 0.001), respectively.

CONCLUSIONS

The mpMRI may correctly identify PCP tumors of the PZ and the mean ADC value and ADC ratio can predict the presence of the cribriform pattern in the PCa.

Current Applications and Future Development of Magnetic Resonance Fingerprinting in Diagnosis, Characterization, and Response Monitoring in Cancer

Magnetic resonance imaging (MRI) has enabled non-invasive cancer diagnosis, monitoring, and management in common clinical settings. However, inadequate quantitative analyses in MRI continue to limit its full potential and these often have an impact on clinicians' judgments. Magnetic resonance fingerprinting (MRF) has recently been introduced to acquire multiple quantitative parameters simultaneously in a reasonable timeframe. Initial retrospective studies have demonstrated the feasibility of using MRF for various cancer characterizations. Further trials with larger cohorts are still needed to explore the repeatability and reproducibility of the data acquired by MRF. At the moment, technical difficulties such as undesirable processing time or lack of motion robustness are limiting further implementations of MRF in clinical oncology. This review summarises the latest findings and technology developments for the use of MRF in cancer management and suggests possible future implications of MRF in characterizing tumour heterogeneity and response assessment.

Prostate Cancer Diffusion-Weighted Magnetic Resonance Imaging: Does the Choice of Diffusion-Weighting Level Matter?

BACKGROUND

Diffusion-weighted magnetic resonance imaging plays an important role in multiparametric assessment of prostate lesions. The derived apparent diffusion coefficient (ADC) could be a useful quantitative biomarker for malignant growth, but lacks acceptance because of low reproducibility.

PURPOSE

To investigate the impact of the choice of diffusion-weighting levels (b-values) on contrast-to-noise ratio and quantitative measures in prostate diffusion-weighted MRI.

STUDY TYPE

Retrospective and simulation based on published data.

SUBJECTS

Patient cohort (21 men with Prostate Imaging-Reporting and Data System (PI-RADS) version 2 score ≥3) from a single-center study.

FIELD STRENGTH/SEQUENCE

3 T/diffusion-weighted imaging with single-shot echo-planar imaging.

ASSESSMENT

Both clinical data and simulations based on previously acquired data were used to quantify the influence of b-value choice in normal peripheral zone (PZ) and PZ tumor lesions. For clinical data, ADC was determined for different combinations of b-values. Contrast-to-noise ratio and quantitative diffusion measures were simulated for a wide range of b-values.

STATISTICAL TESTS

Tissue ADC and the lesion-to-normal tissue ADC ratios of different b-value combinations were compared with paired two-tailed Student's t-tests. A P-value <0.05 was considered statistically significant.

RESULTS

Findings about b-value dependence derived from clinical data and from simulations agreed with each other. Provided measurement was limited to two b-values, simulation-derived optimal b-value choices coincided with PI-RADSv2 recommendations. For two-point measurements, ADC decreased by 15% when the maximum b-value increased from 1000 to 1500 seconds/mm² , but corresponding lesion-to-normal tissue ADC ratio showed no significant change (P = 0.86 for acquired data). Simulations with three or more measurement points produced ADCs that declined by only 8% over this range of maximum b-value. Corresponding ADC ratios declined between 2.6% (three points) and 3.8% (21 points). Simulations also revealed an ADC reduction of about 19% with the shorter echo and diffusion time evaluated.

DATA CONCLUSION

The comprehensive assessment of b-value dependence permits better formulation of protocol and analysis recommendations for obtaining reproducible results in prostate cancer diffusion-weighted MRI.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 2.

When to perform vertebroplasty? A retrospective analysis from a single center and a review of the literature

BACKGROUND AND AIM

To establish an optimal timing for vertebroplasty in order to obtain a clinically important pain reduction and improving quality of live in patients with osteoporotic or traumatic vertebral fractures.

METHODS

This study includes 22 vertebroplasty procedures performed from October 2018 to July 2020 in 21 patients with traumatic or osteoporotic vertebral fractures (19 female, two men; age between 53 and 89 years). All treatments were executed under fluoroscopic guidance using 11 or 13 G needle through transpedicular or costovertebral unilateral approach. Each patient underwent conscious sedation, continuously monitored by an anesthesiologist. Preoperative MRI images, obtained by 3T or 1.5T MRI scanner, always showed bone marrow edema. The VAS scale and Roland Morris disability questionnaire (RMdq) were administered to patients before and after the treatment to evaluate pain and life quality.

RESULTS

7 patients were treated in the first month after the injury, one was treated twice; 8 patients in the second month, 6 in the third. We observed a reduction of: 5.5 points in the vas scale, 10.3 in the RMdq in the first month; 5.6 points vas, 11.6 points RMdq in the second month; 4 points vas and 9.75 points RMdq in the third month.

CONCLUSIONS

This study demostrated that, in our preliminary experience, vertebroplasty has the best outcome if performed at 2 months from injury.

Basics in Magnetic Resonance guided Focused Ultrasound: technical basis and clinical application. A brief overview

First applications of high focused ultrasound as intracranial ablative therapy were firstly described in early 50’. Since then, the technological innovations have shown an increasingly safe and effective face of this technique. And in the last few years, Magnetic Resonance (MR) guided Focused Ultrasound (gFUS) has become a valid minimally invasive technique in the treatment of several diseases, from bone tumors to symptomatic uterine fibroids or essential tremors. MR guidance, through the tomographic view, offers the advantage of an accurate target detection and treatment planning. Moreover, real-time monitoring sequences allow to avoid non-target ablation. An adequate knowledge of FUS is essential to understand its clinical effectiveness. Therefore, this brief review aims to debate the physical characteristics of US and the main fields of clinical application.

The role of interventional radiology in hepatic and renal hemorrhage embolization: single center experience and literature review

Background and aim:

Intraabdominal hemorrhage secondary to liver and kidney injury is a major cause of morbidity and mortality. Endovascular arterial embolization is an established interventional radiology technique used to treat active bleeding, and its role in managing abdominal hemorrhages is growing, given the increasing trend for conservative treatment. Our study aims to retrospectively evaluate the technical and clinical results and the possible complications of arterial embolization procedures performed in emergency, in post-traumatic, iatrogenic, and pathological hepatic and renal bleedings.

Methods:

We performed a ten-year, single-center retrospective survey (from January 2010 to December 2019) of all patients treated in emergency by intra-arterial embolization of liver and kidney bleeding. Preliminary CT angiography studies were evaluated, as well as the angiographic findings. Materials used, procedural data, and clinical outcomes, including complications, were recorded.

Results:

The diagnostic angiography showed a single source of bleeding in 20 cases (66.7%), two bleeding vessels in 4 cases (13.3%), and multiple hemorrhagic sources in 6 cases (20%). All bleeding sources were successfully embolized; in 12 patients (40%), complete embolization was achieved with coils and 18 patients (60%) with hemostatic sponges. In one case, a second embolization procedure was performed for the persistence of hemodynamic instability. No major post-procedural complications were recorded. The mean procedure duration was 65.1 minutes.

Conclusions:

Based on our experience and literature data, the treatment of endovascular embolization in acute abdominal bleeding of hepatic and renal origin represents the treatment of choice, as it can provide complete therapeutic success in hemodynamically stable patients. (www.actabiomedica.it)

Predicting the aggressiveness of peripheral zone prostate cancer using a fractional order calculus diffusion model

PURPOSE

To evaluate the performance of parameters D, β, μ from the Fractional Order Calculus (FROC) model at differentiating peripheral zone (PZ) prostate cancer (PCa) MATERIAL AND METHODS: 75 patients who underwent targeted MRI-guided TRUS prostate biopsy within 6 months of MRI were reviewed retrospectively. Regions of interest (ROI) were placed on suspicious lesions on MRI scans. ROIs were then correlated to pathological results based on core biopsy location. The final tumor count is a total: 23 of GS 6 (3 + 3), 36 of GS 7 (3 + 4), 18 of GS 7 (4 + 3), and 19 of GS ≥ 8. Diffusion-weighted imaging (DWI) scans were fitted into the FROC and monoexponential model to calculate ADC and FROC parameters: anomalous diffusion coefficient D, intravoxel diffusion heterogeneity β, and spatial parameter μ. The performance of FROC parameters and ADC at differentiating PCa grade was evaluated with receiver operating characteristic (ROC) analysis.

RESULTS

In differentiating low (GS 6) vs. intermediate (GS 7) risk PZ PCa, combination of (D, β) provides the best performance with AUC of 0.829 with significance of p = 0.018 when compared to ADC (AUC of 0.655). In differentiating clinically significant (GS 6) vs. clinically significant (GS ≥ 7) PCa, combination of (D, β, μ) provides highest AUC of 0.802 when compared to ADC (AUC of 0.671) with significance of p = 0.038. Stratification of intermediate (GS 7) and high (GS ≥ 8) risk PCa with FROC did not reach a significant difference when compared to ADC.

CONCLUSION

Combination of FROC parameters shows greater performance than ADC at differentiating low vs. intermediate risk and clinically insignificant vs. significant prostate cancers in peripheral zone lesions. The FROC diffusion model holds promise as a quantitative imaging technique for non-invasive evaluation of PZ PCa.

Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI

PURPOSE

Risk classification of primary prostate cancer in clinical routine is mainly based on prostate-specific antigen (PSA) levels, Gleason scores from biopsy samples, and tumor-nodes-metastasis (TNM) staging. This study aimed to investigate the diagnostic performance of positron emission tomography/magnetic resonance imaging (PET/MRI) in vivo models for predicting low-vs-high lesion risk (LH) as well as biochemical recurrence (BCR) and overall patient risk (OPR) with machine learning.

METHODS

Fifty-two patients who underwent multi-parametric dual-tracer [18F]FMC and [68Ga]Ga-PSMA-11 PET/MRI as well as radical prostatectomy between 2014 and 2015 were included as part of a single-center pilot to a randomized prospective trial (NCT02659527). Radiomics in combination with ensemble machine learning was applied including the [68Ga]Ga-PSMA-11 PET, the apparent diffusion coefficient, and the transverse relaxation time-weighted MRI scans of each patient to establish a low-vs-high risk lesion prediction model (MLH). Furthermore, MBCR and MOPR predictive model schemes were built by combining MLH, PSA, and clinical stage values of patients. Performance evaluation of the established models was performed with 1000-fold Monte Carlo (MC) cross-validation. Results were additionally compared to conventional [68Ga]Ga-PSMA-11 standardized uptake value (SUV) analyses.

RESULTS

The area under the receiver operator characteristic curve (AUC) of the MLH model (0.86) was higher than the AUC of the [68Ga]Ga-PSMA-11 SUVmax analysis (0.80). MC cross-validation revealed 89% and 91% accuracies with 0.90 and 0.94 AUCs for the MBCR and MOPR models respectively, while standard routine analysis based on PSA, biopsy Gleason score, and TNM staging resulted in 69% and 70% accuracies to predict BCR and OPR respectively.

CONCLUSION

Our results demonstrate the potential to enhance risk classification in primary prostate cancer patients built on PET/MRI radiomics and machine learning without biopsy sampling.

Ex Vivo MR Histology and Cytometric Feature Mapping Connect Three-dimensional in Vivo MR Images to Two-dimensional Histopathologic Images of Murine Sarcomas

Purpose To establish a platform for quantitative tissue-based interpretation of cytoarchitecture features from tumor MRI measurements. Materials and Methods In a pilot preclinical study, multicontrast in vivo MRI of murine soft-tissue sarcomas in 10 mice, followed by ex vivo MRI of fixed tissues (termed MR histology), was performed. Paraffin-embedded limb cross-sections were stained with hematoxylin-eosin, digitized, and registered with MRI. Registration was assessed by using binarized tumor maps and Dice similarity coefficients (DSCs). Quantitative cytometric feature maps from histologic slides were derived by using nuclear segmentation and compared with registered MRI, including apparent diffusion coefficients and transverse relaxation times as affected by magnetic field heterogeneity (T2* maps). Cytometric features were compared with each MR image individually by using simple linear regression analysis to identify the features of interest, and the goodness of fit was assessed on the basis of R² values. Results Registration of MR images to histopathologic slide images resulted in mean DSCs of 0.912 for ex vivo MR histology and 0.881 for in vivo MRI. Triplicate repeats showed high registration repeatability (mean DSC, >0.9). Whole-slide nuclear segmentations were automated to detect nuclei on histopathologic slides (DSC = 0.8), and feature maps were generated for correlative analysis with MR images. Notable trends were observed between cell density and in vivo apparent diffusion coefficients (best line fit: R² = 0.96, P < .001). Multiple cytoarchitectural features exhibited linear relationships with in vivo T2* maps, including nuclear circularity (best line fit: R² = 0.99, P < .001) and variance in nuclear circularity (best line fit: R² = 0.98, P < .001). Conclusion An infrastructure for registering and quantitatively comparing in vivo tumor MRI with traditional histologic analysis was successfully implemented in a preclinical pilot study of soft-tissue sarcomas. Keywords: MRI, Pathology, Animal Studies, Tissue Characterization Supplemental material is available for this article. © RSNA, 2021.

Clinical application and progress of quantitative functional magnetic resonance imaging in prostate cancer

Magnetic resonance imaging (MRI) is a very important imaging method for diagnosis and treatment of prostate cancer (PCa) in clinical practice. As functional MRI is growing and maturing, its quantitative parameters are expected to enhance the clinical value of MRI furtherly. Intravoxel incoherent motion diffusion imaging, diffusion tensor imaging, and diffusion kurtosis imaging, which were derived from diffusion weighted imaging, have provided richer and more accurate parameters. The newly-developed magnetic resonance elastography can complement the mechanical characteristics of PCa.

Metastatic Diffusion Volume Based on Apparent Diffusion Coefficient as a Prognostic Factor in Castration-Resistant Prostate Cancer

BACKGROUND

Whole-body diffusion-weighted MRI (WB-DWI) is useful for assessing disease activity in castration-resistant prostate cancer (CRPC). MET-RADS-P is a subjective assessment-based reporting system proposed to standardize the interpretation of WB-DWI. However, a quantitative evaluation of WB-DWI has not been fully investigated.

PURPOSE

To investigate the validity, and analyze the prognostic value, of quantitative evaluation of WB-DWI based on apparent diffusion coefficient (ADC) values for CRPC.

STUDY TYPE

Retrospective.

POPULATION

Sixty-six patients with CRPC. The median age was 75 years. During the median follow-up period of 25.2 months, 23 of 66 patients (34.8%) died of prostate cancer.

FIELD STRENGTH/SEQUENCE

A 1.5 T WB-DWI was used with two b-values (0 s/mm² -1000 s/mm² ). A single-shot echo-planar imaging sequence was used.

ASSESSMENT

WB-DWI were evaluated by three readers according to MET-RADS-P scoring system. Using imaging software, Attractive BDScore, tumor diffusion volume (mDV) and ADC value of metastatic lesion (mADC) was calculated by two readers. The mDV was calculated with ADC values (×10^-3  mm² /sec) of 0.4-0.9 (mDV_0.4-0.9 ), 0.9-1.4 (mDV_0.9-1.4 ), and 1.4-1.8 (mDV_1.4-1.8 ), respectively.

STATISTICAL TESTS

Spearman's rank correlation coefficient was used to assess the correlation. The relationships between the variables with cancer-specific survival (CSS) were evaluated. Multivariate analysis was performed using the Cox proportional hazards model.

RESULTS

mDVs showed a strong positive correlation with MET-RADS-P scores (r = 0.90/0.87, P < 0.05 for both). mDV showed a statistically significant association with CSS (hazard ratio [HR]: 1.01, P < 0.05). When the mDVs calculated based on the ADC values were included, mDV_0.4-0.9 (HR: 1.02, P < 0.05) and the number of therapeutic lines (HR: 1.35, P < 0.05) were significant independent indicators of CSS shortening.

CONCLUSION

Assessment of metastatic tumor volume based on ADC values can be used in the prognostic evaluation of patients with CRPC. WB-DWI might be a potential prognostic imaging biomarker for CRPC.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

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

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

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

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

Diagnostic and interventional radiology: an update

NOT PRESENT.

Diffusion-Weighted Imaging in Oncology: An Update

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

Evaluation of relationships between the final Gleason score, PI-RADS v2 score, ADC value, PSA level, and tumor diameter in patients that underwent radical prostatectomy due to prostate cancer

INTRODUCTION

This study aimed to investigate the relationship between the serum PSA level, Gleason score (GS), PI-RADS v2 score, tumor ADC_min value, and the largest tumor diameter in patients that underwent radical prostatectomy (RP) due to prostate cancer (PCa) and to comparatively evaluate the variables of these parameters in clinically significant and insignificant PCa groups.

MATERIALS AND METHODS

The mpMRI examinations of the patients who underwent RP due to PCa were retrospectively evaluated. According to the final GS, the lesions were divided into two groups as clinically significant (GS ≥ 7) and insignificant (GS ≤ 6). The PSA value, tumor ADC_min value, tumor diameter, and PI-RADS score were compared between the clinically significant and nonsignificant PCa groups using Student's t-test. The correlations between the serum PSA level, GS, PI-RADS v2 score, tumor ADC_min value, and tumor diameter were evaluated separately (Pearson's correlation analysis was used for peripheral gland tumors, and Spearman's correlation analysis for central gland tumors). A ROC analysis was undertaken to evaluate the efficacy of the tumor ADC_min, diameter and PSA values in differentiating clinically significant and nonsignificant tumors.

RESULTS

In both central and peripheral gland tumors, there was a correlation between the PSA level, tumor diameter, PI-RADS score, ADC_min value, and GS at various levels (poor, moderate, and high). In central gland tumors, there was no significant difference between the two groups in terms of the PSA value and PI-RADS scores (p > 0.05), but the ADC_min value and diameter of the tumor significantly differed (p < 0.05). For peripheral gland tumors, significant differences were observed in all parameters (p < 0.05). The cut-off values for the peripheral and central gland tumors are as follows: lesion diameter, 13.5 mm and 19 mm; tumor ADC_min, 0.709 × 10^-3 mm²/s and 0.874 × 10^-3 mm²/s; and PSA level, 8.47 ng/ml and 11.10 ng/ml, respectively.

CONCLUSION

The current PI-RADS v2 scoring system can be inadequate in distinguishing clinically significant and insignificant groups in central gland tumors. A separate cut-off value of the tumor diameter should be determined for central and peripheral gland tumors. Tumor ADC_min values can be used as a predictive parameter. The PSA cut-off value should be kept lower in peripheral gland tumors.

Quantitative MRI Changes During Weekly Ultra-Hypofractionated Prostate Cancer Radiotherapy With Integrated Boost

Purpose: Quantitative MRI reflects tissue characteristics. As possible changes during radiotherapy may lead to treatment adaptation based on response, we here assessed if such changes during treatment can be detected.

Methods and Materials: In the hypoFLAME trial patients received ultra-hypofractionated prostate radiotherapy with an integrated boost to the tumor in 5 weekly fractions. We analyzed T2 and ADC maps of 47 patients that were acquired in MRI exams prior to and during radiotherapy, and performed rigid registrations based on the prostate contour on anatomical T2-weighted images. We analyzed median T2 and ADC values in three regions of interest (ROIs): the central gland (CG), peripheral zone (PZ), and tumor. We analyzed T2 and ADC changes during treatment and compared patients with and without hormonal therapy. We tested changes during treatment for statistical significance with Wilcoxon signed rank tests. Using confidence intervals as recommended from test-retest measurements, we identified persistent T2 and ADC changes during treatment.

Results: In the CG, median T2 and ADC values significantly decreased 12 and 8%, respectively, in patients that received hormonal therapy, while in the PZ these values decreased 17 and 18%. In the tumor no statistically significant change was observed. In patients that did not receive hormonal therapy, median ADC values in the tumor increased with 20%, while in the CG and PZ no changes were observed. Persistent T2 changes in the tumor were found in 2 out of 24 patients, while none of the 47 patients had persistent ADC changes.

Conclusions: Weekly quantitative MRI could identify statistically significant ADC changes in the tumor in patients without hormonal therapy. On a patient level few persistent T2 changes in the tumor were observed. Long-term follow-up is required to relate the persistent T2 and ADC changes to outcome and evaluate the applicability of quantitative MRI for response based treatment adaptation.