A CT-based radiomics nomogram for predicting prognosis of coronavirus disease 2019 (COVID-19) radiomics nomogram predicting COVID-19

Objectives: To identify the value of radiomics method derived from CT images to predict prognosis in patients with COVID-19. Methods: A total of 40 patients with COVID-19 were enrolled in the study. Baseline clinical data, CT images, and laboratory testing results were collected from all patients. We defined that ROIs in the absorption group decreased in the density and scope in GGO, and ROIs in the progress group progressed to consolidation. A total of 180 ROIs from absorption group (n = 118) and consolidation group (n = 62) were randomly divided into a training set (n = 145) and a validation set (n = 35) (8:2). Radiomics features were extracted from CT images, and the radiomics-based models were built with three classifiers. A radiomics score (Rad-score) was calculated by a linear combination of selected features. The Rad-score and clinical factors were incorporated into the radiomics nomogram construction. The prediction performance of the clinical factors model and the radiomics nomogram for prognosis was estimated. Results: A total of 15 radiomics features with respective coefficients were calculated. The AUC values of radiomics models (kNN, SVM, and LR) were 0.88, 0.88, and 0.84, respectively, showing a good performance. The C-index of the clinical factors model was 0.82 [95% CI (0.75–0.88)] in the training set and 0.77 [95% CI (0.59–0.90)] in the validation set. The radiomics nomogram showed optimal prediction performance. In the training set, the C-index was 0.91 [95% CI (0.85–0.95)], and in the validation set, the C-index was 0.85 [95% CI (0.69–0.95)]. For the training set, the C-index of the radiomics nomogram was significantly higher than the clinical factors model (p = 0.0021). Decision curve analysis showed that radiomics nomogram outperformed the clinical model in terms of clinical usefulness. Conclusions: The radiomics nomogram based on CT images showed favorable prediction performance in the prognosis of COVID-19. The radiomics nomogram could be used as a potential biomarker for more accurate categorization of patients into different stages for clinical decision-making process. Advances in knowledge: Radiomics features based on chest CT images help clinicians to categorize the patients of COVID-19 into different stages. Radiomics nomogram based on CT images has favorable predictive performance in the prognosis of COVID-19. Radiomics act as a potential modality to supplement conventional medical examinations.

Download Full-text

Value of contrast-enhanced CT based radiomic machine learning algorithm in differentiating gastrointestinal stromal tumors with KIT exon 11 mutation: a two-center study

10.21203/rs.3.rs-48361/v1 ◽

2020 ◽

Author(s):

Bo Liu ◽

Hexiang Wang ◽

Shunli Liu ◽

Shifeng Yang ◽

Yancheng Song ◽

...

Keyword(s):

Machine Learning ◽

Gastrointestinal Stromal Tumors ◽

Ct Images ◽

Arterial Phase ◽

Training Set ◽

Clinical Model ◽

Stromal Tumors ◽

Contrast Enhanced ◽

Validation Set ◽

Exon 11

Abstract Background Knowing the genetic phenotype of gastrointestinal stromal tumors (GISTs) is essential for patients who receive therapy with tyrosine kinase inhibitors.Methods We enrolled 106 patients (80 in the training set, 26 in the validation set) with clinicopathologically confirmed GISTs from two centers. Preoperative and postoperative clinical characteristics were selected and analyzed to construct the clinical model. Arterial phase (A-phase), venous phase (V-phase), delayed phase (D-phase), and combined radiomics algorithms were generated from the training set based on contrast-enhanced computed tomography (CE-CT) images. Various radiomics feature selection methods were used, namely least absolute shrinkage and selection operator (LASSO); minimum redundancy maximum relevance (mRMR); and generalized linear model (GLM) as a machine-learning classifier. Independent predictive factors were determined to construct preoperative and postoperative radiomics nomograms by multivariate logistic regression analysis. The performances of the clinical model, radiomics algorithm, and radiomics nomogram in distinguishing GISTs with the KIT exon 11 mutation were evaluated by area under the curve (AUC) of the receiver operating characteristic (ROC).Results The combined radiomics algorithm was found to be the best prediction model for differentiating the expression status of the KIT exon 11 mutation (AUC = 0.836; 95% confidence interval (CI), 0.640–0.951) in the validation set. The clinical model, and preoperative and postoperative radiomics nomograms had AUCs of 0.606 (95% CI, 0.397–0.790), 0.715 (95% CI, 0.506–0.873), and 0.679 (95% CI, 0.468–0.847), respectively, with the validation set.Conclusion The radiomics algorithm could distinguish GISTs with the KIT exon 11 mutation based on CE-CT images and could potentially be used for selective genetic analysis to support the precision medicine of GISTs.

Download Full-text

An easy-to-operate web-based calculator for predicting the progression of chronic kidney disease

Journal of Translational Medicine ◽

10.1186/s12967-021-02942-y ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Qian Xu ◽

Yunyun Wang ◽

Yiqun Fang ◽

Shanshan Feng ◽

Cuiyun Chen ◽

...

Keyword(s):

Chronic Kidney Disease ◽

Kidney Disease ◽

Clinical Decision Making ◽

Area Under The Curve ◽

Progression Free Survival ◽

Clinical Decision ◽

Training Set ◽

Web Based ◽

Free Survival ◽

Validation Set

Abstract Background This study aimed to establish and validate an easy-to-operate novel scoring system based on simple and readily available clinical indices for predicting the progression of chronic kidney disease (CKD). Methods We retrospectively evaluated 1045 eligible CKD patients from a publicly available database. Factors included in the model were determined by univariate and multiple Cox proportional hazard analyses based on the training set. Results Independent prognostic factors including etiology, hemoglobin level, creatinine level, proteinuria, and urinary protein/creatinine ratio were determined and contained in the model. The model showed good calibration and discrimination. The area under the curve (AUC) values generated to predict 1-, 2-, and 3-year progression-free survival in the training set were 0.947, 0.931, and 0.939, respectively. In the validation set, the model still revealed excellent calibration and discrimination, and the AUC values generated to predict 1-, 2-, and 3-year progression-free survival were 0.948, 0.933, and 0.915, respectively. In addition, decision curve analysis demonstrated that the model was clinically beneficial. Moreover, to visualize the prediction results, we established a web-based calculator (https://ncutool.shinyapps.io/CKDprogression/). Conclusion An easy-to-operate model based on five relevant factors was developed and validated as a conventional tool to assist doctors with clinical decision-making and personalized treatment.

Download Full-text

Preoperative Prediction of Lymphovascular Space Invasion in Cervical Cancer With Radiomics –Based Nomogram

Frontiers in Oncology ◽

10.3389/fonc.2021.637794 ◽

2021 ◽

Vol 11 ◽

Author(s):

Wei Du ◽

Yu Wang ◽

Dongdong Li ◽

Xueming Xia ◽

Qiaoyue Tan ◽

...

Keyword(s):

Cervical Cancer ◽

Clinical Decision Making ◽

Area Under The Curve ◽

Predictive Performance ◽

Clinical Decision ◽

Tumor Stage ◽

Support Vector ◽

Training Cohort ◽

Clinical Model ◽

Radiomics Signature

PurposeTo build and evaluate a radiomics-based nomogram that improves the predictive performance of the LVSI in cervical cancer non-invasively before the operation.MethodThis study involved 149 patients who underwent surgery with cervical cancer from February 2017 to October 2019. Radiomics features were extracted from T2 weighted imaging (T2WI). The radiomic features were selected by logistic regression with the least absolute shrinkage and selection operator (LASSO) penalty in the training cohort. Based on the selected features, support vector machine (SVM) algorithm was used to build the radiomics signature on the training cohort. Incorporating radiomics signature and clinical risk factors, the radiomics-based nomogram was developed. The sensitivity, specificity, accuracy, and area under the curve (AUC) and Receiver operating characteristic (ROC) curve were calculated to assess these models.ResultThe radiomics model performed much better than the clinical model in both training (AUCs 0.925 vs. 0.786, accuracies 87.5% vs. 70.5%, sensitivities 83.6% vs. 41.7% and specificities 90.9% vs. 94.7%) and testing (AUCs 0.911 vs. 0.706, accuracies 84.0% vs. 71.3%, sensitivities 81.1% vs. 43.4% and specificities 86.4% vs. 95.0%). The combined model based on the radiomics signature and tumor stage, tumor infiltration depth and tumor pathology yielded the best performance (training cohort, AUC = 0.943, accuracies 89.5%, sensitivities 85.4% and specificities 92.9%; testing cohort, AUC = 0.923, accuracies 84.6%, sensitivities 84.0% and specificities 85.1%).ConclusionRadiomics-based nomogram was a useful tool for predicting LVSI of cervical cancer. This would aid the selection of the optimal therapeutic strategy and clinical decision-making for individuals.

Download Full-text

1204 Analyzing User Journey Data In Digital Health: Predicting Dropout From A Digital CBT-I Intervention

SLEEP ◽

10.1093/sleep/zsaa056.1198 ◽

2020 ◽

Vol 43 (Supplement_1) ◽

pp. A460-A460

Author(s):

V Bremer ◽

P Chow ◽

B Funk ◽

F Thorndike ◽

L Ritterband

Keyword(s):

Clinical Decision Making ◽

Behavioral Therapy ◽

Digital Health ◽

Predictive Performance ◽

Clinical Decision ◽

Prediction Performance ◽

Drop Out ◽

Machine Learning Techniques ◽

Support Vector ◽

Task Support

Abstract Introduction Intervention dropout is an important factor for the evaluation and implementation of digital therapeutics, including in insomnia. Large amounts of individualized data (logins, questionnaires, EMA data) in these interventions can combine to create user journeys - the data generated by the path an individual takes to navigate the digital therapeutic. User journeys can provide insight about how likely users are to drop out of an intervention on an individual level and lead to increased prediction performance. Thus, the goal of this study is to provide a step-by-step guide for the analysis of user journeys and utilize this guide to predict intervention dropout, illustrated with an example from a data in a RCT of digital therapeutic for chronic insomnia, for which outcomes have previously been published. Methods Analysis of user journeys includes data transformation, feature engineering, and statistical model analysis, using machine learning techniques. A framework is established to leverage user journeys to predict various behaviors. For this study, the framework was applied to predict dropouts of 151 participants from a fully automated web-based program (SHUTi) that delivered cognitive behavioral therapy for insomnia. For this task, support vector machines, logistic regression with regularization, and boosted decision trees were applied at different points in 9-week intervention. These techniques were evaluated based on their predictive performance. Results After model evaluation, a decision tree ensemble achieved AUC values ranging between 0.6-0.9 based on application of machine earning techniques. Various handcrafted and theory-driven features (e.g., time to complete certain intervention steps, time to get out of bed after arising, and days since last system interaction contributed to prediction performance. Conclusion Results indicate that utilizing a user journey framework and analysis can predict intervention dropout. Further, handcrafted theory-driven features can increase prediction performance. This prediction of dropout could lead to an enhanced clinical decision-making in digital therapeutics. Support The original study evaluating the efficacy of this intervention has been reported elsewhere and was funded by grant R01 MH86758 from the National Institute of Mental Health.

Download Full-text

Multiple Survival Outcome Prediction of Glioblastoma Patients Based on Multiparametric MRI

Frontiers in Oncology ◽

10.3389/fonc.2021.778627 ◽

2021 ◽

Vol 11 ◽

Author(s):

Bin Wang ◽

Shan Zhang ◽

Xubin Wu ◽

Ying Li ◽

Yueming Yan ◽

...

Keyword(s):

Clinical Decision Making ◽

Predictive Accuracy ◽

Clinical Decision ◽

Multiparametric Mri ◽

Cox Proportional Hazards Model ◽

The Cancer Genome Atlas ◽

Training Set ◽

Term Survival ◽

Mri Features ◽

Validation Set

PurposeConstruction of radiomics models for the individualized estimation of multiple survival stratification in glioblastoma (GBM) patients using the multiregional information extracted from multiparametric MRI that could facilitate clinical decision-making for GBM patients.Materials and MethodsA total of 134 eligible GBM patients were selected from The Cancer Genome Atlas. These patients were separated into the long-term and short-term survival groups according to the median of individual survival indicators: overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS). Then, the patients were divided into a training set and a validation set in a ratio of 2:1. Radiomics features (n = 5,152) were extracted from multiple regions of the GBM using multiparametric MRI. Then, radiomics signatures that are related to the three survival indicators were respectively constructed using the analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) regression for each patient in the training set. Based on a Cox proportional hazards model, the radiomics model was further constructed by combining the signature and clinical risk factors.ResultsThe constructed radiomics model showed a promising discrimination ability to differentiate in the training set and validation set of GBM patients with survival indicators of OS, PFS, and DSS. Both the four MRI modalities and five tumor subregions have different effects on the three survival indicators of GBM. The favorable calibration and decision curve analysis indicated the clinical decision value of the radiomics model. The performance of models of the three survival indicators was different but excellent; the best model achieved C indexes of 0.725, 0.677, and 0.724, respectively, in the validation set.ConclusionOur results show that the proposed radiomics models have favorable predictive accuracy on three survival indicators and can provide individualized probabilities of survival stratification for GBM patients by using multiparametric and multiregional MRI features.

Download Full-text

Radiomics for intracerebral hemorrhage: are all small hematomas benign?

British Journal of Radiology ◽

10.1259/bjr.20201047 ◽

2020 ◽

pp. 20201047

Author(s):

Chenyi Zhan ◽

Qian Chen ◽

Mingyue Zhang ◽

Yilan Xiang ◽

Jie Chen ◽

...

Keyword(s):

Intracerebral Hemorrhage ◽

Clinical Decision Making ◽

Characteristic Curve ◽

Clinical Information ◽

Clinical Decision ◽

Hematoma Expansion ◽

Clinical Factors ◽

Combined Model ◽

Clinical Model ◽

Discrimination Ability

Objectives: We hypothesized that not all small hematomas are benign and that radiomics could predict hematoma expansion (HE) and short-term outcomes in small hematomas. Methods: We analyzed 313 patients with small (<10 ml) intracerebral hemorrhage (ICH) who underwent baseline non-contrast CT within 6 h of symptom onset between September 2013 and February 2019. Poor outcome was defined as a Glasgow Outcome Scale score ≤3. A radiomic model and a clinical model were built using least absolute shrinkageand selection operator algorithm or multivariate analysis. A combined model that incorporated the developed radiomic score and clinical factors was then constructed. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of these models. Results: The addition of radiomics to clinical factors significantly improved the prediction performance of HE compared with the clinical model alone in both the training {AUC, 0.762 [95% CI (0.665–0.859)] versus AUC, 0.651 [95% CI (0.556–0.745)], p = 0.007} and test {AUC, 0.776 [95% CI (0.655–0.897) versus AUC, 0.631 [95% CI (0.451–0.810)], p = 0.001} cohorts. Moreover, the radiomic-based model achieved good discrimination ability of poor outcomes in the 3–10 ml group (AUCs 0.720 and 0.701). Conclusion: Compared with clinical information alone, combined model had greater potential for discriminating between benign and malignant course in patients with small ICH, particularly 3–10 ml hematomas. Advances in knowledge: Radiomics can be used as a supplement to conventional medical imaging, improving clinical decision-making and facilitating personalized treatment in small ICH.

Download Full-text

A Nomogram Combining a Four-Gene Biomarker and Clinical Factors for Predicting Survival of Melanoma

Frontiers in Oncology ◽

10.3389/fonc.2021.593587 ◽

2021 ◽

Vol 11 ◽

Author(s):

Chuan Zhang ◽

Dan Dang ◽

Yuqian Wang ◽

Xianling Cong

Keyword(s):

Predictive Model ◽

Clinical Data ◽

Predictive Performance ◽

Rank Test ◽

Concordance Index ◽

Rna Seq ◽

Clinical Factors ◽

Training Set ◽

Test Set ◽

Validation Set

BackgroundCurrently there is no effective prognostic indicator for melanoma, the deadliest skin cancer. Thus, we aimed to develop and validate a nomogram predictive model for predicting survival of melanoma.MethodsFour hundred forty-nine melanoma cases with RNA sequencing (RNA-seq) data from TCGA were randomly divided into the training set I (n = 224) and validation set I (n = 225), 210 melanoma cases with RNA-seq data from Lund cohort of Lund University (available in GSE65904) were used as an external test set. The prognostic gene biomarker was developed and validated based on the above three sets. The developed gene biomarker combined with clinical characteristics was used as variables to develop and validate a nomogram predictive model based on 379 patients with complete clinical data from TCGA (Among 470 cases, 91 cases with missing clinical data were excluded from the study), which were randomly divided into the training set II (n = 189) and validation set II (n = 190). Area under the curve (AUC), concordance index (C-index), calibration curve, and Kaplan-Meier estimate were used to assess predictive performance of the nomogram model.ResultsFour genes, i.e., CLEC7A, CLEC10A, HAPLN3, and HCP5 comprise an immune-related prognostic biomarker. The predictive performance of the biomarker was validated using tROC and log-rank test in the training set I (n = 224, 5-year AUC of 0.683), validation set I (n = 225, 5-year AUC of 0.644), and test set I (n = 210, 5-year AUC of 0.645). The biomarker was also significantly associated with improved survival in the training set (P < 0.01), validation set (P < 0.05), and test set (P < 0.001), respectively. In addition, a nomogram combing the four-gene biomarker and six clinical factors for predicting survival in melanoma was developed in the training set II (n = 189), and validated in the validation set II (n = 190), with a concordance index of 0.736 ± 0.041 and an AUC of 0.832 ± 0.071.ConclusionWe developed and validated a nomogram predictive model combining a four-gene biomarker and six clinical factors for melanoma patients, which could facilitate risk stratification and treatment planning.

Download Full-text

A Combined-Radiomics Approach of CT Images to Predict Response to Anti-PD-1 Immunotherapy in NSCLC: A Retrospective Multicenter Study

Frontiers in Oncology ◽

10.3389/fonc.2021.688679 ◽

2022 ◽

Vol 11 ◽

Author(s):

Minghao Wu ◽

Yanyan Zhang ◽

Jianing Zhang ◽

Yuwei Zhang ◽

Yina Wang ◽

...

Keyword(s):

Predictive Performance ◽

Ct Images ◽

Clinical Factors ◽

Training Set ◽

Predictive Capacity ◽

Clinical Factor ◽

Contrast Enhanced ◽

Test Sets ◽

Individualized Decision ◽

Retrospective Multicenter Study

ObjectiveBased on non-contrast-enhanced (NCE)/contrast-enhanced (CE) computed tomography (CT) images, we try to identify a combined-radiomics model and evaluate its predictive capacity regarding response to anti-PD1 immunotherapy of patients with non-small-cell lung cancer (NSCLC).Methods131 patients with NSCLC undergoing anti-PD1 immunotherapy were retrospectively enrolled from 7 institutions. Using largest lesion (LL) and target lesions (TL) approaches, we performed a radiomics analysis based on pretreatment NCE-CT (NCE-radiomics) and CE-CT images (CE-radiomics), respectively. Meanwhile, a combined-radiomics model based on NCE-CT and CE-CT images was constructed. Finally, we developed their corresponding nomograms incorporating clinical factors. ROC was used to evaluate models’ predictive performance in the training and testing set, and a DeLong test was employed to compare the differences between different models.ResultsFor TL approach, both NCE-radiomics and CE-radiomics performed poorly in predicting response to immunotherapy. For LL approach, NCE-radiomics nomograms and CE-radiomics nomograms incorporating with clinical factor of distant metastasis all showed satisfactory results, reflected by the AUCs in the training (AUC=0.84, 95% CI: 0.75-0.92; AUC=0.77, 95% CI: 0.67-0.87) and test sets (AUC=0.78, 95% CI: 0.64-0.92, AUC=0.73, 95% CI: 0.57-0.88), respectively. Compared with the NCE-radiomics nomograms, the combined-radiomics nomogram showed incremental predictive capacity in the training set (AUC=0.85, 95% CI: 0.77-0.92) and test set (AUC=0.81, 95% CI: 0.67-0.94), respectively, but no statistical difference (P=0.86, P=0.79).ConclusionCompared with radiomics based on single NCE or CE-CT images, the combined-radiomics model has potential advantages to identify patients with NSCLC most likely to benefit from immunotherapy, and may effectively improve more precise and individualized decision support.

Download Full-text

Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice

Scientific Reports ◽

10.1038/s41598-021-88440-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Imogen Schofield ◽

David C. Brodbelt ◽

Noel Kennedy ◽

Stijn J. M. Niessen ◽

David B. Church ◽

...

Keyword(s):

Machine Learning ◽

Cushing’S Syndrome ◽

Clinical Decision Making ◽

Predictive Performance ◽

Clinical Decision ◽

Cushing's Syndrome ◽

Machine Learning Algorithms ◽

Learning Methods ◽

Machine Learning Methods ◽

Clinical Records

AbstractCushing’s syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing’s syndrome can be a challenging diagnosis to confirm, therefore new methods to aid diagnosis are warranted. Four machine-learning algorithms were applied to predict a future diagnosis of Cushing's syndrome, using structured clinical data from the VetCompass programme in the UK. Dogs suspected of having Cushing's syndrome were included in the analysis and classified based on their final reported diagnosis within their clinical records. Demographic and clinical features available at the point of first suspicion by the attending veterinarian were included within the models. The machine-learning methods were able to classify the recorded Cushing’s syndrome diagnoses, with good predictive performance. The LASSO penalised regression model indicated the best overall performance when applied to the test set with an AUROC = 0.85 (95% CI 0.80–0.89), sensitivity = 0.71, specificity = 0.82, PPV = 0.75 and NPV = 0.78. The findings of our study indicate that machine-learning methods could predict the future diagnosis of a practicing veterinarian. New approaches using these methods could support clinical decision-making and contribute to improved diagnosis of Cushing’s syndrome in dogs.

Download Full-text

Use of plasma androgen receptor (AR) testing to optimize docetaxel chemotherapy in castration-resistant prostate cancer (CRPC): A multicenter biomarker study.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.5546 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. 5546-5546

Author(s):

Vincenza Conteduca ◽

Elena Castro ◽

Daniel Wetterskog ◽

Emanuela Scarpi ◽

Nuria Romero-Laorden ◽

...

Keyword(s):

Clinical Decision Making ◽

Clinical Decision ◽

Digital Pcr ◽

Castration Resistant Prostate Cancer ◽

Standard Regimen ◽

Internal Validation ◽

Castration Resistant ◽

Potential Biomarker ◽

Prospective Trials ◽

Physician Patient

5546 Background: Plasma AR status has been identified as a potential biomarker of response in CRPC patients receiving docetaxel or the AR-targeted therapies abiraterone or enzalutamide. However, the relevance of plasma AR in the overall management of CRPC patients (pts) receiving docetaxel at different dose due to the toxicity profiles and physician-patient preferences is unknown. Methods: This was a multi-institution study of associations between baseline plasma AR-copy-number status assessed by droplet digital PCR and outcome in 325 CRPC pts. Between September 2011 and July 2019 pts started treatment with docetaxel administered at standard regimen 75mg/m2 every three weeks or adapted regimen (75-80% of standard recommended dose or 30mg/m2 weekly administration) at the discretion of the treating physician. Patients were assigned randomly into 2 sets with a ratio 2:1 to either training (n=217) and internal validation (n=108) cohorts. Results: In our study, adapted regimen of docetaxel was administered in 68 (31.3%) and 35 (32.4%) of training and validation cohorts, respectively. Based on plasma AR status, 67 (30.9%) and 39 (36.1%) validation and training set pts were classified as AR gain, respectively. In men treated with standard docetaxel regimen, no difference in progression-free/overall survival (PFS/OS) was seen between plasma AR normal and gain in both cohorts. In patients treated with adapted docetaxel regimen, we observed a significantly shorter median PFS (3.9 vs. 6.4 months, HR 4.77, 95%CI 1.48-3.80, p=0.0003) and median OS (11.2 vs . 20.4 months, HR 2.87, 95%CI 1.73-2.13, p=0.0008) in the training cohort. This finding was confirmed in the validation cohort (median PFS: 4.8 vs. 7.4 months, HR 2.54, 95%CI 1.40-4.58, p=0.005, and median OS: 11.8 vs. 26.4 months, HR 5.00, 95%CI 2.59-9.65, p<0.0001). In addition, AR-gained patients were less likely than AR normal to have a PSA decline when receiving an adapted regimen in both cohorts (p=0.010 e p=0.003, respectively). Conclusions: This study suggests that plasma AR may improve clinical decision making in choosing not only between AR-directed therapies and taxanes, but also between adapted and standard regimen of docetaxel in first- and subsequent-therapy lines, providing promising clinical implications to select the proper timing and dose of docetaxel. Prospective trials to validate these findings are warranted.

Download Full-text