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

Bengtsson, Johan; Thimansson, Erik; Fridh, Erik Baubeta; Zackrisson, Sophia; Sundgren, Pia C.; Bjartell, Anders; Flondell-Sité, Despina

doi:10.3389/fonc.2023.1079040

Cited by 6 publications

(4 citation statements)

References 31 publications

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“…Interestingly, while ADC values have often been shown to inversely correlate with ISUP grade [ 64 , 65 ], in our study, there were no ADC features present in the top 10 for any of the 3 imaging classifiers. In line with our findings, when using a multicenter MRI dataset, Bengtsson et al [ 66 ] were not able to identify a correlation between ADC and ISUP grades, suggesting that the lack of harmonization in MRI parameters used to acquire and derive ADC maps might dilute the value of this feature in a clinical situation involving different field strengths, scanners, and acquisition parameters. This could potentially be an explanation for our results, which made use of a multicenter training dataset to identify the most important features.…”

Section: Discussionsupporting

confidence: 87%

Radiogenomics Analysis Linking Multiparametric MRI and Transcriptomics in Prostate Cancer

Fernandes

Schaap

Kant

et al. 2023

Cancers

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Prostate cancer (PCa) is a highly prevalent cancer type with a heterogeneous prognosis. An accurate assessment of tumor aggressiveness can pave the way for tailored treatment strategies, potentially leading to better outcomes. While tumor aggressiveness is typically assessed based on invasive methods (e.g., biopsy), radiogenomics, combining diagnostic imaging with genomic information can help uncover aggressive (imaging) phenotypes, which in turn can provide non-invasive advice on individualized treatment regimens. In this study, we carried out a parallel analysis on both imaging and transcriptomics data in order to identify features associated with clinically significant PCa (defined as an ISUP grade ≥ 3), subsequently evaluating the correlation between them. Textural imaging features were extracted from multi-parametric MRI sequences (T2W, DWI, and DCE) and combined with DCE-derived parametric pharmacokinetic maps obtained using magnetic resonance dispersion imaging (MRDI). A transcriptomic analysis was performed to derive functional features on transcription factors (TFs), and pathway activity from RNA sequencing data, here referred to as transcriptomic features. For both the imaging and transcriptomic features, different machine learning models were separately trained and optimized to classify tumors in either clinically insignificant or significant PCa. These models were validated in an independent cohort and model performance was used to isolate a subset of relevant imaging and transcriptomic features to be further investigated. A final set of 31 imaging features was correlated to 33 transcriptomic features obtained on the same tumors. Five significant correlations (p < 0.05) were found, of which, three had moderate strength (|r| ≥ 0.5). The strongest significant correlations were seen between a perfusion-based imaging feature—MRDI A median—and the activities of the TFs STAT6 (−0.64) and TFAP2A (−0.50). A higher-order T2W textural feature was also significantly correlated to the activity of the TF STAT6 (−0.58). STAT6 plays an important role in controlling cell proliferation and migration. Loss of the AP2alpha protein expression, quantified by TFAP2A, has been strongly associated with aggressiveness and progression in PCa. According to our findings, a combination of texture features extracted from T2W and DCE, as well as perfusion-based pharmacokinetic features, can be considered for the prediction of clinically significant PCa, with the pharmacokinetic MRDI A feature being the most correlated with the underlying transcriptomic information. These results highlight a link between quantitative imaging features and the underlying transcriptomic landscape of prostate tumors.

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Section: Discussionsupporting

confidence: 87%

Radiogenomics Analysis Linking Multiparametric MRI and Transcriptomics in Prostate Cancer

Fernandes

Schaap

Kant

et al. 2023

Cancers

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“…A change of ~15ms in TE resulted in errors 5-fold (csPCa cases) or 10-fold (benign prostates), compared to the errors incurred by simply repeating the acquisition with a consistent TE. The implementation of a simple linear calibration proves effective in generating comparable quantitative biomarker values across acquisitions with (6), 800 (6), 1500 (12), 3000 (18) 0 (1), 50 (6), 800 (6), 1500 (12), 3000 (18) N/A cases, for the RSIrs TE90 (1,4), RSIrs TE90corr (2,5) and RSIrs Temin2 (3,6) acquisitions. Standard deviation (indicate well as gray color wash) and mean difference of the reference, 98th percentile of RSIrs TEmin1 , to the 98th pe RSIrs TE90 , RSIrs TEmin2 and RSIrs TE90corr…”

Section: Discussionmentioning

confidence: 99%

“…Around 288,300 new cases of prostate cancer were expected in the United States in 2023 alone, accounting for nearly 29% of all cancer cases in men [1]. The standard procedure to diagnose clinically significant prostate cancer (csPCa) includes multiparametric MRI (mpMRI) prior to biopsy [2]. A biopsy is an expensive and invasive procedure, which has the potential for both overdiagnosis and underdiagnosis of csPCa, given that only a small part of the prostate gland [2] is sampled, which is why accurate imaging of the whole prostate gland is valuable.…”

Section: Introductionmentioning

confidence: 99%

“…The standard procedure to diagnose clinically significant prostate cancer (csPCa) includes multiparametric MRI (mpMRI) prior to biopsy [2]. A biopsy is an expensive and invasive procedure, which has the potential for both overdiagnosis and underdiagnosis of csPCa, given that only a small part of the prostate gland [2] is sampled, which is why accurate imaging of the whole prostate gland is valuable. A large percentage of men suspected to have csPCa can be safely reassured without biopsy if the prostate gland appears normal on mpMRI, and further, when biopsy is needed, needles can be directed to the most suspicious lesions.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative MRI biomarker for classification of clinically significant prostate cancer: calibration for reproducibility across echo times

Kallis,

Conlin,

Ollison

et al. 2024

Preprint

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Background: Restriction Spectrum Imaging restriction score (RSIrs) is a quantitative biomarker for detecting clinically significant prostate cancer (csPCa). However, the quantitative value of the RSIrs is affected by imaging parameters such as echo time (TE). Purpose: The purpose of the present study is to develop a calibration method to account for differences in echo times and facilitate use of RSIrs as a quantitative biomarker for the detection of csPCa. Methods: This study included 197 consecutive patients who underwent MRI and biopsy examination; 97 were diagnosed with csPCa (grade group ≥ 2). RSI data were acquired three times during the same session: twice at minimum TE~75ms and once at TE=90ms (TEmin</1>, TEmin</2>, and TE90, respectively). A proposed calibration method, trained on patients without csPCa, estimated a linear scaling factor (f) for each of the four diffusion compartments (C) of the RSI signal model. A linear regression model was determined to match C-maps of TE90 to the reference C-maps of TEmin1 within the interval ranging from 95th to 99th percentile of signal intensity within the prostate. RSIrs comparisons were made at 98th percentile within the prostate. We compared RSIrs from calibrated TE90 (RSIrs</TE90corr>) and uncorrected TE90 (RSIrs</TE90>) to RSIrs from reference TEmin1 (RSIrs</TEmin1>) and repeated TEmin2 (RSIrs</TEmin2>). Calibration performance was evaluated with sensitivity, specificity, area under the ROC curve, positive predicted value, negative predicted value, and F1-score. Results: Scaling factors for C</1>, C</2>, C</3>, and C</4> were estimated as 1.70, 1.38, 1.03, and 1.19, respectively. In non-csPCa cases, the 98th percentile of RSIrs</TEmin2> and RSIrs</TEmin1> differed by 0.27±0.86SI (mean±standard deviation), whereas RSIrs</TE90> differed from RSIrs</TEmin1> by 1.81±1.20SI. After calibration, this bias was reduced to -0.41±1.20SI, representing a 78% reduction in absolute error. For patients with csPCa, the difference was 0.54±1.98SI between RSIrs</TEmin2> and RSIrs</TEmin1> and 2.28±2.06SI between RSIrs</TE90> and RSIrs</TEmin1>. After calibration, the mean difference decreased to -0.86SI, a 38% reduction in absolute error. At the Youden index for patient-level classification of csPCa (8.94SI), RSIrs</TEmin1> has a sensitivity of 66% and a specificity of 72%. Prior to calibration, RSIrs</TE90> at the same threshold tended to over-diagnose benign cases (sensitivity 44%, specificity 88%). Post-calibration, RSIrs</TE90corr> performs more similarly to the reference (sensitivity 71%, specificity 62%). Conclusion: The proposed linear calibration method produces similar quantitative biomarker values for acquisitions with different TE, reducing TE-induced error by 78% and 38% for non-csPCa and csPCa, respectively.

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Addressing intra‐ and inter‐institution variability of a radiomic framework based on Apparent Diffusion Coefficient in prostate cancer

Morelli,

Paganelli,

Marvaso

et al. 2024

Medical Physics

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BackgroundProstate cancer (PCa) is a highly heterogeneous disease, making tailored treatment approaches challenging. Magnetic resonance imaging (MRI), notably diffusion‐weighted imaging (DWI) and the derived Apparent Diffusion Coefficient (ADC) maps, plays a crucial role in PCa characterization. In this context, radiomics is a very promising approach able to disclose insights from MRI data. However, the sensitivity of radiomic features to MRI settings, encompassing DWI protocols and multicenter variations, requires the development of robust and generalizable models.PurposeTo develop a comprehensive radiomics framework for noninvasive PCa characterization using ADC maps, focusing on identifying reliable imaging biomarkers against intra‐ and inter‐institution variations.Materials and methodsTwo patient cohorts, including an internal cohort (118 PCa patients) used for both training (75%) and hold‐out testing (25%), and an external cohort (50 PCa patients) for independent testing, were employed in the study. DWI images were acquired with three different DWI protocols on two different MRI scanners: two DWI protocols acquired on a 1.5‐T scanner for the internal cohort, and one DWI protocol acquired on a 3‐T scanner for the external cohort. One hundred and seven radiomics features (i.e., shape, first order, texture) were extracted from ADC maps of the whole prostate gland. To address variations in DWI protocols and multicenter variability, a dedicated pipeline, including two‐way ANOVA, sequential‐feature‐selection (SFS), and ComBat features harmonization was implemented. Mann–Whitney U‐tests (α = 0.05) were performed to find statistically significant features dividing patients with different tumor characteristics in terms of Gleason score (GS) and T‐stage. Support‐Vector‐Machine models were then developed to predict GS and T‐stage, and the performance was assessed through the area under the curve (AUC) of receiver‐operating‐characteristic curves.ResultsDownstream of ANOVA, two subsets of 38 and 41 features stable against DWI protocol were identified for GS and T‐stage, respectively. Among these, SFS revealed the most predictive features, yielding an AUC of 0.75 (GS) and 0.70 (T‐stage) in the hold‐out test. Employing ComBat harmonization improved the external‐test performance of the GS model, raising AUC from 0.72 to 0.78.ConclusionBy incorporating stable features with a harmonization procedure and validating the model on an external dataset, model robustness, and generalizability were assessed, highlighting the potential of ADC and radiomics for PCa characterization.

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

Cited by 6 publications

References 31 publications

Radiogenomics Analysis Linking Multiparametric MRI and Transcriptomics in Prostate Cancer

Radiogenomics Analysis Linking Multiparametric MRI and Transcriptomics in Prostate Cancer

Quantitative MRI biomarker for classification of clinically significant prostate cancer: calibration for reproducibility across echo times

Addressing intra‐ and inter‐institution variability of a radiomic framework based on Apparent Diffusion Coefficient in prostate cancer

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