Dataset Information

MRI-based radiomic signatures for pretreatment prognostication in cervical cancer.

ABSTRACT:

Background

Accurate pretherapeutic prognostication is important for tailoring treatment in cervical cancer (CC).

Purpose

To investigate whether pretreatment MRI-based radiomic signatures predict disease-specific survival (DSS) in CC.

Study type

Retrospective.

Population

CC patients (n = 133) allocated into training_(T) (n_T = 89)/validation_(V) (n_V = 44) cohorts.

Field strength/sequence

T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) at 1.5T or 3.0T.

Assessment

Radiomic features from segmented tumors were extracted from T2WI and DWI (high b-value DWI and apparent diffusion coefficient (ADC) maps).

Statistical tests

Radiomic signatures for prediction of DSS from T2WI (T2_rad ) and T2WI with DWI (T2 + DWI_rad ) were constructed by least absolute shrinkage and selection operator (LASSO) Cox regression. Area under time-dependent receiver operating characteristics curves (AUC) were used to evaluate and compare the prognostic performance of the radiomic signatures, MRI-derived maximum tumor size ≤/> 4 cm (MAX_size ), and 2018 International Federation of Gynecology and Obstetrics (FIGO) stage (I-II/III-IV). Survival was analyzed using Cox model estimating hazard ratios (HR) and Kaplan-Meier method with log-rank tests.

Results

The radiomic signatures T2_rad and T2 + DWI_rad yielded AUC_T /AUC_V of 0.80/0.62 and 0.81/0.75, respectively, for predicting 5-year DSS. Both signatures yielded better or equal prognostic performance to that of MAX_size (AUC_T /AUC_V : 0.69/0.65) and FIGO (AUC_T /AUC_V : 0.77/0.64) and were significant predictors of DSS after adjusting for FIGO (HR_T /HR_V for T2_rad : 4.0/2.5 and T2 + DWI_rad : 4.8/2.1). Adding T2_rad and T2 + DWI_rad to FIGO significantly improved DSS prediction compared to FIGO alone in cohort_(T) (AUC_T 0.86 and 0.88 vs. 0.77), and FIGO with T2 + DWI_rad tended to the same in cohort_(V) (AUC_V 0.75 vs. 0.64, p = 0.07). High radiomic score for T2 + DWI_rad was significantly associated with reduced DSS in both cohorts.

Data conclusion

Radiomic signatures from T2WI and T2WI with DWI may provide added value for pretreatment risk assessment and for guiding tailored treatment strategies in CC.

SUBMITTER: Wagner-Larsen KS

PROVIDER: S-EPMC10652318 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Publications

MRI-based radiomic signatures for pretreatment prognostication in cervical cancer.

Wagner-Larsen Kari S KS Hodneland Erlend E Fasmer Kristine E KE Lura Njål N Woie Kathrine K Bertelsen Bjørn I BI Salvesen Øyvind Ø Halle Mari K MK Smit Noeska N Krakstad Camilla C Haldorsen Ingfrid S IS

Cancer medicine 20231016 20

<h4>Background</h4>Accurate pretherapeutic prognostication is important for tailoring treatment in cervical cancer (CC).<h4>Purpose</h4>To investigate whether pretreatment MRI-based radiomic signatures predict disease-specific survival (DSS) in CC.<h4>Study type</h4>Retrospective.<h4>Population</h4>CC patients (n = 133) allocated into training<sub>(T)</sub> (n<sub>T</sub> = 89)/validation<sub>(V)</sub> (n<sub>V</sub> = 44) cohorts.<h4>Field strength/sequence</h4>T2-weighted imaging (T2WI) and ...[more]

PMID: 37840437

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Project description:BackgroundRadiomics or computer-extracted texture features derived from MRI have been shown to help quantitatively characterize prostate cancer (PCa). Radiomics have not been explored depth in the context of predicting biochemical recurrence (BCR) of PCa.PurposeTo identify a set of radiomic features derived from pretreatment biparametric MRI (bpMRI) that may be predictive of PCa BCR.Study typeRetrospective.SubjectsIn all, 120 PCa patients from two institutions, I1 and I2 , partitioned into training set D1 (N = 70) from I1 and independent validation set D2 (N = 50) from I2 . All patients were followed for ≥3 years.Sequence3T, T2 -weighted (T2 WI) and apparent diffusion coefficient (ADC) maps derived from diffusion-weighted sequences.AssessmentPCa regions of interest (ROIs) on T2 WI were annotated by two experienced radiologists. Radiomic features from bpMRI (T2 WI and ADC maps) were extracted from the ROIs. A machine-learning classifier (CBCR ) was trained with the best discriminating set of radiomic features to predict BCR (pBCR ).Statistical testsWilcoxon rank-sum tests with P < 0.05 were considered statistically significant. Differences in BCR-free survival at 3 years using pBCR was assessed using the Kaplan-Meier method and compared with Gleason Score (GS), PSA, and PIRADS-v2.ResultsDistribution statistics of co-occurrence of local anisotropic gradient orientation (CoLlAGe) and Haralick features from T2 WI and ADC were associated with BCR (P < 0.05) on D1 . CBCR predictions resulted in a mean AUC = 0.84 on D1 and AUC = 0.73 on D2 . A significant difference in BCR-free survival between the predicted classes (BCR + and BCR-) was observed (P = 0.02) on D2 compared to those obtained from GS (P = 0.8), PSA (P = 0.93) and PIRADS-v2 (P = 0.23).Data conclusionRadiomic features from pretreatment bpMRI can be predictive of PCa BCR after therapy and may help identify men who would benefit from adjuvant therapy.Level of evidence4 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;48:1626-1636.

Project description:BackgroundEffective diagnostic tools for prompt identification of high-risk locally advanced cervical cancer (LACC) patients are needed to facilitate early, individualized treatment. The aim of this work was to assess temporal changes in tumor radiomics (delta radiomics) from T2-weighted imaging (T2WI) during concurrent chemoradiotherapy (CCRT) in LACC patients, and their association with progression-free survival (PFS). Furthermore, to develop, validate, and compare delta- and pretreatment radiomic signatures for prognostic modeling.MethodsA total of 110 LACC patients undergoing CCRT with MRI at baseline and mid-treatment were divided into training (cohortT: n = 73) and validation (cohortV: n = 37) cohorts. Radiomic features were extracted from tumors segmented on pre-CCRT and mid-CCRT T2WI and radiomic deltas (delta features) were computed. Two radiomic signatures for predicting PFS were constructed by least absolute shrinkage and selection operator (LASSO) Cox regression: Deltarad (from delta features) and Pre-CCRTrad (from pre-CCRT features). Prognostic performance of the radiomic signatures, 2018 International Federation of Gynecology and Obstetrics (FIGO) stage (I-IV), and baseline MRI-derived maximum tumor diameter (Tumormax: ≤2 cm; >2 and ≤ 4 cm; >4 cm) was evaluated by area under time-dependent receiver operating characteristics (tdROC) curves (AUC) in cohortT and cohortV (AUCT/AUCV). Mann-Whitney U tests assessed differences in radiomic delta features. PFS was evaluated using the Kaplan-Meier method with log-rank tests.ResultsDeltarad (AUCT/AUCV: 0.74/0.79) marginally outperformed Pre-CCRTrad (0.72/0.75) for predicting 5-year PFS, and both signatures clearly surpassed that of FIGO (0.61/0.61) and Tumormax (0.58/0.65). In total, four features within Deltarad and Pre-CCRTrad significantly differed in delta feature values between progressors and non-progressors, being consistently lower in progressors (p ≤ 0.03 for all). High Deltarad and Pre-CCRTrad radiomic scores were associated with poor PFS (p ≤ 0.04 for Deltarad in cohortT/Pre-CCRTrad in both cohorts; p = 0.11 for Deltarad in cohortV).ConclusionsDelta- and pretreatment radiomic signatures equally allow early prognostication in LACC, outperforming FIGO stage and MRI-assessed maximum tumor diameter.

Project description:Prognostic biomarkers that can reliably predict the disease-free survival (DFS) of locally advanced cervical cancer (LACC) are needed for identifying those patients at high risk for progression, who may benefit from a more aggressive treatment. In the present study, we aimed to construct a multiparametric MRI-derived radiomic signature for predicting DFS of LACC patients who underwent concurrent chemoradiotherapy (CCRT).MethodsThis multicenter retrospective study recruited 263 patients with International Federation of Gynecology and Obetrics (FIGO) stage IB-IVA treated with CCRT for whom pretreatment MRI scans were performed. They were randomly divided into two groups: primary cohort (n = 178) and validation cohort (n = 85). The LASSO regression and Cox proportional hazard regression were conducted to construct the radiomic signature (RS). According to the cutoff of the RS value, patients were dichotomized into low- and high-risk groups. Pearson's correlation and Kaplan-Meier analysis were conducted to evaluate the association between the RS and DFS. The RS, the clinical model incorporating FIGO stage and lymph node metastasis by the multivariate Cox proportional hazard model, and a combined model incorporating RS and clinical model were constructed to estimate DFS individually.ResultsThe final radiomic signature consisted of four radiomic features: T2W_wavelet-LH_ glszm_Size Zone NonUniformity, ADC_wavelet-HL-first order_ Median, ADC_wavelet-HH-glrlm_Long Run Low Gray Level Emphasis, and ADC_wavelet _LL_gldm_Large Dependence High Gray Emphasis. Higher RS was significantly associated with worse DFS in the primary and validation cohorts (both p<0.001). The RS demonstrated better prognostic performance in predicting DFS than the clinical model in both cohorts (C-index, 0.736-0.758 for RS, and 0.603-0.649 for clinical model). However, the combined model showed no significant improvement (C-index, 0.648, 95% CI, 0.571-0.685).ConclusionsThe present study indicated that the multiparametric MRI-derived radiomic signature could be used as a non-invasive prognostic tool for predicting DFS in LACC patients.

Project description:ObjectiveHigh-risk prostate cancer (PCa) is often treated by prostate-only radiotherapy (PORT) owing to its favourable toxicity profile compared to whole-pelvic radiotherapy. Unfortunately, more than 50% patients still developed disease progression following PORT. Conventional clinical factors may be unable to identify at-risk subgroups in the era of precision medicine. In this study, we aimed to investigate the prognostic value of pre-treatment planning computed tomography (pCT)-based radiomic features and clinical attributes to predict 5-year progression-free survival (PFS) in high-risk PCa patients following PORT.Materials and methodsA total of 176 biopsy-confirmed PCa patients who were treated at the Hong Kong Princess Margaret Hospital were retrospectively screened for eligibility. Clinical data and pCT of one hundred eligible high-risk PCa patients were analysed. Radiomic features were extracted from the gross-tumour-volume (GTV) with and without applying Laplacian-of-Gaussian (LoG) filter. The entire patient cohort was temporally stratified into a training and an independent validation cohort in a ratio of 3:1. Radiomics (R), clinical (C) and radiomic-clinical (RC) combined models were developed by Ridge regression through 5-fold cross-validation with 100 iterations on the training cohort. A model score was calculated for each model based on the included features. Model classification performance on 5-year PFS was evaluated in the independent validation cohort by average area-under-curve (AUC) of receiver-operating-characteristics (ROC) curve and precision-recall curve (PRC). Delong's test was used for model comparison.ResultsThe RC combined model which contains 6 predictive features (tumour flatness, root-mean-square on fine LoG-filtered image, prostate-specific antigen serum concentration, Gleason score, Roach score and GTV volume) was the best-performing model (AUC = 0.797, 95%CI = 0.768-0.826), which significantly outperformed the R-model (AUC = 0.795, 95%CI = 0.774-0.816) and C-model (AUC = 0.625, 95%CI = 0.585-0.665) in the independent validation cohort. Besides, only the RC model score significantly classified patients in both cohorts into progression and progression-free groups regarding their 5-year PFS (p< 0.05).ConclusionCombining pCT-based radiomic and clinical attributes provided superior prognostication value regarding 5-year PFS in high-risk PCa patients following PORT. A large multi-centre study will potentially aid clinicians in implementing personalised treatment for this vulnerable subgroup in the future.

Project description:BackgroundTo investigate the prognostic role of pretreatment squamous cell carcinoma antigen (SCCA) in early-stage cervical cancer (CC).MethodsWe enrolled 487 cases of pathology-proven early-stage [International Federation of Gynecology and Obstetrics (FIGO) I/II] squamous or adenosquamous CC that were treated from 2012 to 2015. Restricted cubic splines (RCS) with a full Cox regression model were used to evaluate the association between SCCA levels and survival outcomes. Recursive partitioning analysis (RPA) was used to construct a risk stratification model for overall survival (OS). The performance of the RPA-based model was assessed using a receiver operating characteristic (ROC) curve.ResultsRCS analysis revealed an association between SCCA and OS and disease-free survival (DFS); SCCA ⩾2.5 ng/mL was robust for risk discrimination in our cohort. SCCA had an interaction effect with FIGO classification: Patients with FIGO I and SCCA ⩾2.5 ng/mL overlapped with those with FIGO II and SCCA < 2.5 ng/mL for OS [hazard ratio, 1.04 (95% confidence interval (CI): 0.49-2.24), p = 0.903] and DFS [1.05 (0.56-1.98), p = 0.876]. RPA modeling incorporating SCCA (<2.5 ng/mL and ⩾2.5 ng/mL) and FIGO classification divided CC into three prognostic groups: RPA I, FIGO stage I, and SCCA < 2.5 ng/mL; RPA II, FIGO stage I, and SCCA ⩾ 2.5 ng/mL, or FIGO stage II and SCCA < 2.5 ng/mL; and RPA III, FIGO stage II, and SCCA ⩾ 2.5 ng/mL; with 5-year OS of 94.0%, 85.1%, and 73.5%, respectively (p < 0.001). ROC analysis confirmed that the RPA model outperformed the FIGO 2018 stage with significantly improved accuracy for survival prediction [area under the curve: RPA versus FIGO, 0.663 (95% CI: 0.619-0.705] versus 0.621 (0.576-0.664), p = 0.045]. Importantly, the RPA groupings were associated with the efficacy of treatment regimens. Surgery followed by adjuvant treatment had a higher OS (p < 0.01) and DFS (p = 0.024) than other treatments for RPA III, whereas outcomes were comparable among treatment regimens for RPA I-II.ConclusionHerein, the role of SCCA for prognostication was confirmed, and a robust clinicomolecular risk stratification system that outperforms conventional FIGO classification in early-stage squamous and adenosquamous CC was presented. The model correlated with the efficacy of different treatment regimes.

Project description:BackgroundLymph node metastasis is an important factor affecting the treatment and prognosis of patients with cervical cancer. However, the comparison of different algorithms and features to predict lymph node metastasis is not well understood. This study aimed to construct a non-invasive model for predicting lymph node metastasis in patients with cervical cancer based on clinical features combined with the radiomic features of magnetic resonance imaging (MRI) images.MethodsA total of 180 cervical cancer patients were divided into the training set (n = 126) and testing set (n = 54). In this cross-sectional study, radiomic features of MRI images and clinical features of patients were collected. The least absolute shrinkage and selection operator (LASSO) regression was used to filter the features. Seven machine learning methods, including eXtreme Gradient Boosting (XGBoost), Logistic Regression, Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting Decision Tree (GBDT) are used to build the models. Receiver operating characteristics (ROC) curve and area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the performance of the models.ResultsOf these 180 patients, 49 (27.22%) patients had lymph node metastases. Five of the 122 radiomic features and 3 clinical features were used to build predictive models. Compared with other models, the MNB model was the most robust, with its AUC, specificity, and accuracy on the testing set of 0.745 (95%CI: 0.740-0.750), 0.900 (95%CI: 0.807-0.993), and 0.778 (95%CI: 0.667-0.889), respectively. Furthermore, the AUCs of the MNB models with clinical features only, radiomic features only, and combined features were 0.698 (95%CI: 0.692-0.704), 0.632 (95%CI: 0.627-0.637), and 0.745 (95%CI: 0.740-0.750), respectively.ConclusionThe MNB model, which combines the radiomic features of MRI images with the clinical features of the patient, can be used as a non-invasive tool for the preoperative assessment of lymph node metastasis.

Project description:Pre-treatment survival prediction plays a key role in many diseases. We aimed to determine the prognostic value of pre-treatment Magnetic Resonance Imaging (MRI) based radiomic score for disease-free survival (DFS) in patients with early-stage (IB-IIA) cervical cancer. Methods: A total of 248 patients with early-stage cervical cancer underwent radical hysterectomy were included from two institutions between January 1, 2011 and December 31, 2017, whose MR imaging data, clinicopathological data and DFS data were collected. Patients data were randomly divided into the training cohort (n = 166) and the validation cohort (n=82). Radiomic features were extracted from the pre-treatment T2-weighted (T2w) and contrast-enhanced T1-weighted (CET1w) MR imagings for each patient. Least absolute shrinkage and selection operator (LASSO) regression and Cox proportional hazard model were applied to construct radiomic score (Rad-score). According to the cutoff of Rad-score, patients were divided into low- and high- risk groups. Pearson's correlation and Kaplan-Meier analysis were used to evaluate the association of Rad-score with DFS. A combined model incorporating Rad-score, lymph node metastasis (LNM) and lymphovascular space invasion (LVI) by multivariate Cox proportional hazard model was constructed to estimate DFS individually. Results: Higher Rad-scores were significantly associated with worse DFS in the training and validation cohorts (P<0.001 and P=0.011, respectively). The Rad-score demonstrated better prognostic performance in estimating DFS (C-index, 0.753; 95% CI: 0.696-0.805) than the clinicopathological features (C-index, 0.632; 95% CI: 0.567-0.700). However, the combined model showed no significant improvement (C-index, 0.714; 95%CI: 0.642-0.784). Conclusion: The results demonstrated that MRI-derived Rad-score can be used as a prognostic biomarker for patients with early-stage (IB-IIA) cervical cancer, which can facilitate clinical decision-making.

Dataset Information

MRI-based radiomic signatures for pretreatment prognostication in cervical cancer.

Background

Purpose

Study type

Population

Field strength/sequence

Assessment

Statistical tests

Results

Data conclusion

Publications

MRI-based radiomic signatures for pretreatment prognostication in cervical cancer.

Similar Datasets

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