Can Pre-treatment Quantitative Multi-parametric MRI Predict the Outcome of Radiotherapy in Patients with Prostate Cancer?

Chatterjee, Aritrick; Turchan, William Tyler; Fan, Xiaobing; Griffin, A; Yousuf, Ambereen; Karczmar, Gregory S.; Liauw, S.; Oto, Aytekin

doi:10.1016/j.acra.2021.09.012

Cited by 9 publications

(6 citation statements)

References 37 publications

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“…The perfusion parameters of DCE-MRI to reflect the blood perfusion status include BF and BV. BF represents the BF velocity in the blood vessels of the interest area per unit time, and BV represents the BV, both of which mainly reflect blood perfusion and depend on the microvascular density in the tissue ( Arnoldner et al, 2022 ; Chatterjee et al, 2022 ; Fan et al, 2022 ; Nakayama et al, 2022 ). This study showed that DCE-MRI indices were all decreased after ADT, and K trans , K ep , Ve, BF, and BV in the ORR group were lower than those in the non-ORR group, suggesting that DCE-MRI indices may be used to evaluate the efficacy of ADT.…”

Section: Discussionmentioning

confidence: 99%

Clinical efficacy of different androgen deprivation therapies for prostate cancer and evaluation based on dynamic-contrast enhanced magnetic resonance imaging

Guo,

2024

Acta Biochim. Pol.

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ObjectiveTo evaluate the clinical efficacy of different androgen deprivation therapies for prostate cancer (PCa) based on dynamic-contrast enhanced magnetic resonance imaging (DCE-MRI).Methods104 patients with PCa were studied, all of whom were treated with androgen deprivation therapy. The patients were divided into a continuous group (continuous androgen deprivation therapy) and an intermittent group (intermittent androgen deprivation therapy) by random number table method, 52 cases/group. The therapeutic effect and DCE-MRI indices were compared and the relationship between DCE-MRI indices and clinical efficacy and the evaluation value of therapeutic efficacy were analyzed.ResultsThe objective response rate (ORR) of the intermittent group was higher than that of the continuous group (p < 0.05), and there was no significant difference in disease control rate (DCR) between the two groups (p > 0.05). After treatment, volume transfer coefficient (Ktrans), reverse transfer constant (Kep), volume fraction (Ve), blood volume (BV), and blood flow (BF) in both groups were lowered, and those in the intermittent group were lower than the continuous group (p < 0.05). Ktrans, Kep, Ve, BF, and BV in the ORR group were lower than those in the non-ORR group (p < 0.05). Ktrans, Kep, Ve, BF, and BV were correlated with the therapeutic effect of PCa (p < 0.05). The AUC value of the combined detection of DCE-MRI indices in evaluating the therapeutic effect of PCa was greater than that of each index alone (p < 0.05).ConclusionCompared with continuous androgen deprivation therapy, intermittent androgen deprivation therapy has better clinical efficacy in the treatment of PCa, and DCE-MRI indices are related to the treatment efficacy of PCa and have an evaluation value.

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Section: Discussionmentioning

confidence: 99%

Clinical efficacy of different androgen deprivation therapies for prostate cancer and evaluation based on dynamic-contrast enhanced magnetic resonance imaging

Guo,

2024

Acta Biochim. Pol.

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“…Our study showed that FP and FN lesions were smaller and less conspicuous than TP lesions. Our AI algorithm may have retained prognostic significance, despite missing such lesions, because of the weaker association between smaller lesions [8][9][10][11] and less conspicuous ADC values 39 with biochemical recurrence. Key strengths of this study include the utilization of a well-annotated contemporary patient cohort treated with modern RT approaches.…”

Section: Discussionmentioning

confidence: 99%

Association Between Artificial Intelligence-Derived Tumor Volume and Oncologic Outcomes for Localized Prostate Cancer Treated with Radiation Therapy

Yang

Lee

Tsui³

et al. 2023

Preprint

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Background: Although clinical features of multi-parametric magnetic resonance imaging (mpMRI) have been associated with biochemical recurrence in localized prostate cancer, such features are subject to inter-observer variability. Objective: To evaluate whether the volume of the dominant intraprostatic lesion (DIL), as provided by a deep learning segmentation algorithm, could provide prognostic information for patients treated with definitive radiation therapy (RT). Design, Setting, and Participants: Retrospective study of 438 patients with localized prostate cancer who underwent an endorectal coil, high B-value, 3-Tesla mpMRI and were treated with RT between 2010 and 2017. Intervention: RT. Outcome Measurements and Statistical Analysis: Biochemical recurrence and metastasis risk, assessed with a cause-specific Cox regression and time-dependent receiver operating characteristic analysis. Results and Limitations: The artificial intelligence (AI) model identified DILs with an area under the receiver operating characteristic curve (AUROC) of 0.827 at the patient level. For the 233 patients with available PI-RADS scores, with a median follow-up of 5.6 years, AI-defined DIL volume was significantly associated with biochemical failure (adjusted hazard ratio 1.54, 95% confidence interval 1.09-2.17, p=0.014) after adjustment for PI-RADS score. Among all 438 patients with a median follow-up of 6.9 years, the AUROC for predicting 7-year biochemical failure for AI volume (0.790) was similar to that for an expanded National Comprehensive Cancer Network (NCCN+) category (p=0.17). The AUROC for predicting 7-year metastasis for AI volume trended towards being higher compared to NCCN+ categories (0.854 vs 0.769, p=0.06). Conclusions: A deep learning algorithm could identify the DIL with good performance. AI-defined DIL volume may be able to provide prognostic information independent of the NCCN+ risk group or other radiologic factors for patients with localized prostate cancer treated with RT.

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“…The power of mpMRI features in predicting RT outcome using PSA-based endpoints has been investigated before. Chatterjee et al identified a strong association of ADC values with BF, defined using the Phoenix criteria [ 16 ]. Using mpMRI from 51 patients (7 failures) with a median follow-up of 65 months, an ADC cutoff of 960 mm 2 /s was determined with a sensitivity of 100% and a specificity of 48% for predicting biochemical failure.…”

Section: Discussionmentioning

confidence: 99%

“…It is apparent that the strong prognostic signal in the ADC values drives the high performance of our baseline model (AUC = 0.98). Interestingly, when DCE features were investigated, Chatterjee et al did not identify any associations of these features with failure [ 16 ]. Using biochemical recurrence (BCR) after IMRT, Yamaguchi et al identified significant association of the ADC ratio of tumor to normal prostate tissue analyzing DWI from 101 patients (10 failures) with a median follow up of 29 months [ 50 ].…”

Section: Discussionmentioning

confidence: 99%

“…Quantitative imaging features derived from multiparametric MRI (mpMRI) play an undisputed role in discriminating between tumor and normally-appearing prostate tissue (NAT) and are good candidates to fill this space. However, very few studies have investigated their role as early surrogate biomarkers of RT response [ 16 ]. Similarly, RT-induced tissue changes in prostate cancer and normal prostate tissue identified in serial mpMRI of the prostate before and after RT have not been thoroughly investigated.…”

Section: Introductionmentioning

confidence: 99%

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Longitudinal Changes and Predictive Value of Multiparametric MRI Features for Prostate Cancer Patients Treated with MRI-Guided Lattice Extreme Ablative Dose (LEAD) Boost Radiotherapy

Algohary

Alhusseini

Breto

et al. 2022

Cancers

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Simple Summary In this study, we investigated the longitudinal changes and predictive value of multiparametric MRI (mpMRI) features of prostate cancer patients receiving Lattice Extreme Ablative Dose (LEAD) boost radiotherapy (RT). Ninety-five mpMRI from 25 patients, acquired pre-RT and at 3 time points following RT were analyzed. Five regions of interest were analyzed, related to tumor, peritumoral, and normally-appearing tissues. We identified significant changes in the imaging parameters following RT in all regions. By using selected features at the four scan points and their differences (Δ radiomics), we were able to build viable predictive models for endpoint biopsy positivity. Our study demonstrates that RT causes significant changes to quantitative imaging features in both tumorous and normally-appearing tissues. Because of the noninvasive nature of the mpMRI, acquiring multiple exams post-RT is feasible to monitor treatment response. Several quantitative imaging features are promising predictors of treatment failure based on post-treatment positive biopsy, a strong marker for clinically relevant endpoints. Abstract We investigated the longitudinal changes in multiparametric MRI (mpMRI) (T2-weighted, Apparent Diffusion Coefficient (ADC), and Dynamic Contrast Enhanced (DCE-)MRI) of prostate cancer patients receiving Lattice Extreme Ablative Dose (LEAD) radiotherapy (RT) and the capability of their imaging features to predict RT outcome based on endpoint biopsies. Ninety-five mpMRI exams from 25 patients, acquired pre-RT and at 3-, 9-, and 24-months post-RT were analyzed. MRI/Ultrasound-fused biopsies were acquired pre- and at two-years post-RT (endpoint). Five regions of interest (ROIs) were analyzed: Gross tumor volume (GTV), normally-appearing tissue (NAT) and peritumoral volume in both peripheral (PZ) and transition (TZ) zones. Diffusion and perfusion radiomics features were extracted from mpMRI and compared before and after RT using two-tailed Student t-tests. Selected features at the four scan points and their differences (Δ radiomics) were used in multivariate logistic regression models to predict the endpoint biopsy positivity. Baseline ADC values were significantly different between GTV, NAT-PZ, and NAT-TZ ( p -values < 0.005). Pharmaco-kinetic features changed significantly in the GTV at 3-month post-RT compared to baseline. Several radiomics features at baseline and three-months post-RT were significantly associated with endpoint biopsy positivity and were used to build models with high predictive power of this endpoint (AUC = 0.98 and 0.89, respectively). Our study characterized the RT-induced changes in perfusion and diffusion. Quantitative imaging features from mpMRI show promise as being predictive of endpoint biopsy positivity.

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Can Pre-treatment Quantitative Multi-parametric MRI Predict the Outcome of Radiotherapy in Patients with Prostate Cancer?

Cited by 9 publications

References 37 publications

Clinical efficacy of different androgen deprivation therapies for prostate cancer and evaluation based on dynamic-contrast enhanced magnetic resonance imaging

Clinical efficacy of different androgen deprivation therapies for prostate cancer and evaluation based on dynamic-contrast enhanced magnetic resonance imaging

Association Between Artificial Intelligence-Derived Tumor Volume and Oncologic Outcomes for Localized Prostate Cancer Treated with Radiation Therapy

Longitudinal Changes and Predictive Value of Multiparametric MRI Features for Prostate Cancer Patients Treated with MRI-Guided Lattice Extreme Ablative Dose (LEAD) Boost Radiotherapy

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