Dataset Information

MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins.

ABSTRACT:

Purpose

To investigate the performance of magnetic resonance imaging (MRI)-based radiomics models for benign and malignant prostate lesion discrimination and extracapsular extension (ECE) and positive surgical margins (PSM) prediction.

Methods and materials

In total, 459 patients who underwent multiparametric MRI (mpMRI) before prostate biopsy were included. Radiomic features were extracted from both T2-weighted imaging (T2WI) and the apparent diffusion coefficient (ADC). Patients were divided into different training sets and testing sets for different targets according to a ratio of 7:3. Radiomics signatures were built using radiomic features on the training set, and integrated models were built by adding clinical characteristics. The areas under the receiver operating characteristic curves (AUCs) were calculated to assess the classification performance on the testing sets.

Results

The radiomics signatures for benign and malignant lesion discrimination achieved AUCs of 0.775 (T2WI), 0.863 (ADC) and 0.855 (ADC + T2WI). The corresponding integrated models improved the AUC to 0.851/0.912/0.905, respectively. The radiomics signatures for ECE achieved the highest AUC of 0.625 (ADC), and the corresponding integrated model achieved the highest AUC (0.728). The radiomics signatures for PSM prediction achieved AUCs of 0.614 (T2WI) and 0.733 (ADC). The corresponding integrated models reached AUCs of 0.680 and 0.766, respectively.

Conclusions

The MRI-based radiomics models, which took advantage of radiomic features on ADC and T2WI scans, showed good performance in discriminating benign and malignant prostate lesions and predicting ECE and PSM. Combining radiomics signatures and clinical factors enhanced the performance of the models, which may contribute to clinical diagnosis and treatment.

SUBMITTER: He D

PROVIDER: S-EPMC8259026 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:INTRODUCTION:Extracapsular extension (ECE) in regional lymph nodes and positive surgical margins (PSM) are considered high-risk adverse pathologic features in patients with oropharyngeal squamous cell carcinoma (OPSCC) that each constitute an indication for postoperative adjuvant chemoradiation. We identify pre-operative clinical factors that can predict post-operative ECE and/or PSM and create a nomogram to help clinical decision making. METHODS:Adult patients with non-metastatic OPSCC with initial surgical treatment and confirmed HPV status diagnosed between 2010 and 2014 were selected from the National Cancer Database. Clinical staging was modified to American Joint Committee on Cancer 8th edition parameters. Logistic regression was used for multivariate analysis to identify predictors of pathologic ECE and/or PSM. RESULTS:5065 patients were included. 47.5% of the 3336 HPV-positive (HPV+) patients had ECE/PSM. 40.4% of the 1729 HPV-negative (HPV-) patients with had ECE/PSM. A model was built that included age, clinical ECE, tumor grade, and clinical T and N staging for HPV+ patients. Increasing N-classification was highly predictive of pathologic ECE and/or PSM (N1 OR?=?3.6, N2 OR?=?7.0, N3 OR?=?11.2, p?<?0.01). Clinical ECE (OR?=?4.1, p?<?0.01), tumor grade (ORs 2.2-4.4 with p?<?0.05), and increasing clinical T-classification (ORs 1.2-1.8, p?<?0.05) were also associated with ECE and/or PSM. A similar model was built for HPV- with similar predictive capability. Two internally validated nomograms were designed that demonstrated good discrimination (HPV+ AUC?=?0.66, 95% CI: 0.64-0.68, and HPV- AUC?=?0.70, 95% CI: 0.67-0.72) and good calibration (goodness-of-fit statistic of HPV+ 6.32, p?=?0.61 and HPV- 11.66, p?=?0.17). CONCLUSIONS:These are the first nomograms designed to help predict ECE or PSM for both HPV+ and HPV- OPSCC. The nomograms can facilitate shared decision-making between clinicians and patients as they consider upfront treatment selection for OPSCC.

Project description:The objective of this review is to survey radiomics signatures for detecting pathological extracapsular extension (pECE) on magnetic resonance imaging (MRI) in patients with prostate cancer (PCa) who underwent prostatectomy. Scientific Literature databases were used to search studies published from January 2007 to October 2023. All studies related to PCa MRI staging and using radiomics signatures to detect pECE after prostatectomy were included. Systematic review was performed according to Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA). The risk of bias and certainty of the evidence was assessed using QUADAS-2 and the radiomics quality score. From 1247 article titles screened, 16 reports were assessed for eligibility, and 11 studies were included in this systematic review. All used a retrospective study design and most of them used 3 T MRI. Only two studies were performed in more than one institution. The highest AUC of a model using only radiomics features was 0.85, for the test validation. The AUC for best model performance (radiomics associated with clinical/semantic features) varied from 0.72-0.92 and 0.69-0.89 for the training and validation group, respectively. Combined models performed better than radiomics signatures alone for detecting ECE. Most of the studies showed a low to medium risk of bias. After thorough analysis, we found no strong evidence supporting the clinical use of radiomics signatures for identifying extracapsular extension (ECE) in pre-surgery PCa patients. Future studies should adopt prospective multicentre approaches using large public datasets and combined models for detecting ECE.Critical relevant statementThe use of radiomics algorithms, with clinical and AI integration, in predicting extracapsular extension, could lead to the development of more accurate predictive models, which could help improve surgical planning and lead to better outcomes for prostate cancer patients.Protocol of systematic review registrationPROSPERO CRD42021272088. Published: https://doi.org/10.1136/bmjopen-2021-052342 .Key pointsRadiomics can extract diagnostic features from MRI to enhance prostate cancer diagnosis performance. The combined models performed better than radiomics signatures alone for detecting extracapsular extension. Radiomics are not yet reliable for extracapsular detection in PCa patients.

Project description:Introduction and objectiveMuscle invasive bladder cancer with extravesical extension is an aggressive disease entity that requires multimodal therapy. The benefits of adjuvant chemotherapy (AC) in patients with a positive soft-tissue surgical margin (STSM), however, are relatively unknown due to exclusion of this population in randomized controlled trials of AC. We sought to define survival benefits in this patient population through our institutional bladder cancer database.MethodsRetrospective review of all patients undergoing radical cystectomy for urothelial carcinoma of the bladder from 2004-2020 with ≥pT3b disease irrespective of neoadjuvant chemotherapy (NAC) use was conducted. Progression-free survival (PFS) and overall survival (OS) estimates were obtained using the Kaplan-Meier method with log-rank test, and the Cox-proportional hazards model was used to identify predictors of improved PFS and OS. AC was defined by any chemotherapy use within 90 days of cystectomy, regardless of STSM status.Results476 patients with pT3b disease or worse were identified. Median follow-up was 12.3 months. An amount of 21% of patients were treated with AC. An amount of 24% of patients had positive STSM. Median OS for patients with positive STSM was 8.4 months [95% CI 7-11.5] and 18.3 months [95% CI 15.6-20.8] (p < 0.001) for patients with negative STSM. In the overall cohort, positive STSM (HR 1.93, 95% CI 1.45-2.57, p < 0.001), AC use (HR 0.68, 95% CI 0.51-0.90, p = 0.007), and pN1-3 disease (HR 1.47, 95% CI 1.16-1.87, p = 0.002) were independent predictors of OS when adjusted for performance status, pT-stage, and neoadjuvant chemotherapy use. In patients with positive STSM, median survival was seven months [95% CI 5.2-8.4] without AC, compared to 16.2 months [95% CI 11.5-52.5] with AC (p = 0.0038). For patients with negative STSM, median survival was 17.4 months [95% CI 14-20.1] without AC compared to 22.3 months [95% CI 17.2-36.9] with AC (p = 0.23). In patients with positive STSM, AC use was the only factor associated with an OS benefit with a HR of 0.41 (95% CI 0.21-0.78, p = 0.007). In patients with negative STSM, pT4 and pN1-3 disease were the only factors associated with worse overall survival with a HR of 1.32 (95% CI 1.00-1.74, p = 0.050) and 1.97 (95% CI 1.49-2.60, p < 0.001), respectively.ConclusionsAdministration of adjuvant chemotherapy is of particular benefit in patients with positive STSM following radical cystectomy for gross extravesical disease. Positive STSM may be a representative of "early metastatic" or micrometastatic disease.

Project description:BackgroundPrediction of extracapsular extension (ECE) is essential to achieve a balance between oncologic resection and neural tissue preservation. Microultrasound (MUS) is an attractive alternative to multiparametric magnetic resonance imaging (mpMRI) in the staging scenario.ObjectiveTo create a side-specific nomogram integrating clinicopathologic parameters and MUS findings to predict ipsilateral ECE and guide nerve sparing.Design setting and participantsProspective data were collected from consecutive patients who underwent robotic-assisted radical prostatectomy from June 2021 to May 2022 and had preoperative MUS and mpMRI. A total of 391 patients and 612 lobes were included in the analysis.Outcome measurements and statistical analysisECE on surgical pathology was the primary outcome. Multivariate regression analyses were carried out to identify predictors for ECE. The resultant multivariable model's performance was visualized using the receiver-operating characteristic curve. A nomogram was developed based on the coefficients of the logit function for the MUS-based model. A decision curve analysis (DCA) was performed to assess clinical utility.Results and limitationsThe areas under the receiver-operating characteristic curve (AUCs) of the MUS-based model were 81.4% and 80.9% (95% confidence interval [CI] 75.6, 84.6) after internal validation. The AUC of the mpMRI-model was also 80.9% (95% CI 77.2, 85.7). The DCA demonstrated the net clinical benefit of the MUS-based nomogram and its superiority compared with MUS and MRI alone for detecting ECE. Limitations of our study included its sample size and moderate inter-reader agreement.ConclusionsWe developed a side-specific nomogram to predict ECE based on clinicopathologic variables and MUS findings. Its performance was comparable with that of a mpMRI-based model. External validation and prospective trials are required to corroborate our results.Patient summaryThe integration of clinical parameters and microultrasound can predict extracapsular extension with similar results to models based on magnetic resonance imaging findings. This can be useful for tailoring the preservation of nerves during surgery.