Automated causal inference in application to randomized controlled clinical trials

Randomized controlled trials (RCTs) are considered the gold standard for testing causal hypotheses in the clinical domain; however, the investigation of prognostic variables of patient outcome in a hypothesized cause–effect route is not feasible using standard statistical methods. Here we propose a new automated causal inference method (AutoCI) built on the invariant causal prediction (ICP) framework for the causal reinterpretation of clinical trial data. Compared with existing methods, we show that the proposed AutoCI allows one to clearly determine the causal variables of two real-world RCTs of patients with endometrial cancer with mature outcome and extensive clinicopathological and molecular data. This is achieved via suppressing the causal probability of non-causal variables by a wide margin. In ablation studies, we further demonstrate that the assignment of causal probabilities by AutoCI remains consistent in the presence of confounders. In conclusion, these results confirm the robustness and feasibility of AutoCI for future applications in real-world clinical analysis

Nomograms for prediction of outcome with or without adjuvant radiation therapy for patients with endometrial cancer : A pooled analysis of PORTEC-1 and PORTEC-2 trials

Background Postoperative radiation therapy for stage I endometrial cancer improves locoregional control but is without survival benefit. To facilitate treatment decision support for individual patients, accurate statistical models to predict locoregional relapse (LRR), distant relapse (DR), overall survival (OS), and disease-free survival (DFS) are required. Methods and Materials Clinical trial data from the randomized Post Operative Radiation Therapy for Endometrial Cancer (PORTEC-1; N=714 patients) and PORTEC-2 (N=427 patients) trials and registered group (grade 3 and deep invasion, n=99) were pooled for analysis (N=1240). For most patients (86%) pathology review data were available; otherwise original pathology data were used. Trial variables which were clinically relevant and eligible according to data constraints were age, stage, given treatment (pelvic external beam radiation therapy (EBRT), vaginal brachytherapy (VBT), or no adjuvant treatment, FIGO histological grade, depth of invasion, and lymph-vascular invasion (LVSI). Multivariate analyses were based on Cox proportional hazards regression model. Predictors were selected based on a backward elimination scheme. Model results were expressed by the c-index (0.5-1.0; random to perfect prediction). Two validation sets (n=244 and 291 patients) were used. Results Accuracy of the developed models was good, with training accuracies between 0.71 and 0.78. The nomograms validated well for DR (0.73), DFS (0.69), and OS (0.70), but validation was only fair for LRR (0.59). Ranking of variables as to their predictive power showed that age, tumor grade, and LVSI were highly predictive for all outcomes, and given treatment for LRR and DFS. The nomograms were able to significantly distinguish low- from high-probability patients for these outcomes. Conclusions The nomograms are internally validated and able to accurately predict long-term outcome for endometrial cancer patients with observation, pelvic EBRT, or VBT after surgery. These models facilitate decision support in daily clinical practice and can be used for patient counseling and shared decision making, selecting patients who benefit most from adjuvant treatment, and generating new hypotheses

Nomograms for prediction of outcome with or without adjuvant radiation therapy for patients with endometrial cancer : A pooled analysis of PORTEC-1 and PORTEC-2 trials

Background Postoperative radiation therapy for stage I endometrial cancer improves locoregional control but is without survival benefit. To facilitate treatment decision support for individual patients, accurate statistical models to predict locoregional relapse (LRR), distant relapse (DR), overall survival (OS), and disease-free survival (DFS) are required. Methods and Materials Clinical trial data from the randomized Post Operative Radiation Therapy for Endometrial Cancer (PORTEC-1; N=714 patients) and PORTEC-2 (N=427 patients) trials and registered group (grade 3 and deep invasion, n=99) were pooled for analysis (N=1240). For most patients (86%) pathology review data were available; otherwise original pathology data were used. Trial variables which were clinically relevant and eligible according to data constraints were age, stage, given treatment (pelvic external beam radiation therapy (EBRT), vaginal brachytherapy (VBT), or no adjuvant treatment, FIGO histological grade, depth of invasion, and lymph-vascular invasion (LVSI). Multivariate analyses were based on Cox proportional hazards regression model. Predictors were selected based on a backward elimination scheme. Model results were expressed by the c-index (0.5-1.0; random to perfect prediction). Two validation sets (n=244 and 291 patients) were used. Results Accuracy of the developed models was good, with training accuracies between 0.71 and 0.78. The nomograms validated well for DR (0.73), DFS (0.69), and OS (0.70), but validation was only fair for LRR (0.59). Ranking of variables as to their predictive power showed that age, tumor grade, and LVSI were highly predictive for all outcomes, and given treatment for LRR and DFS. The nomograms were able to significantly distinguish low- from high-probability patients for these outcomes. Conclusions The nomograms are internally validated and able to accurately predict long-term outcome for endometrial cancer patients with observation, pelvic EBRT, or VBT after surgery. These models facilitate decision support in daily clinical practice and can be used for patient counseling and shared decision making, selecting patients who benefit most from adjuvant treatment, and generating new hypotheses

Substantial lymph-vascular space invasion (LVSI) is a significant risk factor for recurrence in endometrial cancer - A pooled analysis of PORTEC 1 and 2 trials

Abstract Background Lymph-vascular space invasion (LVSI) is an important adverse prognostic factor in endometrial cancer (EC). However, its role in relation to type of recurrence and adjuvant treatment is not well defined, and there is significant interobserver variation. This study aimed to quantify LVSI and correlate this to risk and type of recurrence. Methods In the post operative radiation therapy in endometrial carcinoma (PORTEC)-trials stage I EC patients were randomised to receive external beam radiotherapy (EBRT) versus no additional treatment after surgery (PORTEC-1, n = 714), or to EBRT versus vaginal brachytherapy (PORTEC-2, n = 427). In tumour samples of 926 (81.2%) patients with endometrioid tumours LVSI was quantified using 2-, 3- and 4-tiered scoring systems. Cox proportional hazard models were used for time-to-event analysis. Results Any degree of LVSI was identified in 129 cases (13.9%). Substantial LVSI (n = 44, 4.8%) using the 3-tiered approach had the strongest impact on the risk of distant metastasis (hazard ratio (HR) 4.5 confidence interval (CI) 2.4-8.5). In multivariate analysis (including: age, depth of myometrial invasion, grade, treatment) substantial LVSI remained the strongest independent prognostic factor for pelvic regional recurrence (HR 6.2 CI 2.4-16), distant metastasis (HR 3.6 CI 1.9-6.8) and overall survival (HR 2.0 CI 1.3-3.1). Only EBRT (HR 0.3 CI 0.1-0.8) reduced the risk of pelvic regional recurrence. Conclusions Substantial LVSI, in contrast to focal or no LVSI, was the strongest independent prognostic factor for pelvic regional recurrence, distant metastasis and overall survival. Therapeutic decisions should be based on the presence of substantial, not 'any' LVSI. Adjuvant EBRT and/or chemotherapy should be considered for stage I EC with substantial LVSI

Substantial lymph-vascular space invasion (LVSI) is a significant risk factor for recurrence in endometrial cancer - A pooled analysis of PORTEC 1 and 2 trials

Abstract Background Lymph-vascular space invasion (LVSI) is an important adverse prognostic factor in endometrial cancer (EC). However, its role in relation to type of recurrence and adjuvant treatment is not well defined, and there is significant interobserver variation. This study aimed to quantify LVSI and correlate this to risk and type of recurrence. Methods In the post operative radiation therapy in endometrial carcinoma (PORTEC)-trials stage I EC patients were randomised to receive external beam radiotherapy (EBRT) versus no additional treatment after surgery (PORTEC-1, n = 714), or to EBRT versus vaginal brachytherapy (PORTEC-2, n = 427). In tumour samples of 926 (81.2%) patients with endometrioid tumours LVSI was quantified using 2-, 3- and 4-tiered scoring systems. Cox proportional hazard models were used for time-to-event analysis. Results Any degree of LVSI was identified in 129 cases (13.9%). Substantial LVSI (n = 44, 4.8%) using the 3-tiered approach had the strongest impact on the risk of distant metastasis (hazard ratio (HR) 4.5 confidence interval (CI) 2.4-8.5). In multivariate analysis (including: age, depth of myometrial invasion, grade, treatment) substantial LVSI remained the strongest independent prognostic factor for pelvic regional recurrence (HR 6.2 CI 2.4-16), distant metastasis (HR 3.6 CI 1.9-6.8) and overall survival (HR 2.0 CI 1.3-3.1). Only EBRT (HR 0.3 CI 0.1-0.8) reduced the risk of pelvic regional recurrence. Conclusions Substantial LVSI, in contrast to focal or no LVSI, was the strongest independent prognostic factor for pelvic regional recurrence, distant metastasis and overall survival. Therapeutic decisions should be based on the presence of substantial, not 'any' LVSI. Adjuvant EBRT and/or chemotherapy should be considered for stage I EC with substantial LVSI

Automated causal inference in application to randomized controlled clinical trials

Randomized controlled trials (RCTs) are considered the gold standard for testing causal hypotheses in the clinical domain; however, the investigation of prognostic variables of patient outcome in a hypothesized cause–effect route is not feasible using standard statistical methods. Here we propose a new automated causal inference method (AutoCI) built on the invariant causal prediction (ICP) framework for the causal reinterpretation of clinical trial data. Compared with existing methods, we show that the proposed AutoCI allows one to clearly determine the causal variables of two real-world RCTs of patients with endometrial cancer with mature outcome and extensive clinicopathological and molecular data. This is achieved via suppressing the causal probability of non-causal variables by a wide margin. In ablation studies, we further demonstrate that the assignment of causal probabilities by AutoCI remains consistent in the presence of confounders. In conclusion, these results confirm the robustness and feasibility of AutoCI for future applications in real-world clinical analysis

Defining Substantial Lymphovascular Space Invasion in Endometrial Cancer

Lymphovascular space invasion (LVSI) occurs in a minority of endometrial cancer (EC) cases, and the extent of LVSI is an important risk factor for recurrence and/or metastases. Our aim was to improve the reproducibility of measuring clinically meaningful LVSI by performing a quantitative analysis of the correlation between LVSI and the risk of pelvic lymph node recurrence in EC. EC samples from PORTEC-1 and PORTEC-2 trials were retrieved and used to collect quantitative data, including the number of LVSI-positive vessels per H&E-stained slide. Using a predefined threshold for clinical relevance, the risk of pelvic lymph node recurrence risk was calculated (Kaplan-Meier method, with Cox regression) using a stepwise adjustment for the number of LVSI-positive vessels. This analysis was then repeated in the Danish Gynecological Cancer Database (DGCD) cohort. Among patients in PORTEC-1 and PORTEC-2 trials who did not receive external beam radiotherapy, the 5-yr pelvic lymph node recurrence risk was 3.3%, 6.7% (P=0.51), and 26.3% (P<0.001), respectively when 0, 1 to 3, or ≥4 vessels had LVSI involvement; similar results were obtained for the DGCD cohort. Furthermore, both the average number of tumor cells in the largest embolus and the number of LVSI-positive H&E slides differed significantly between focal LVSI and substantial LVSI. On the basis of these results, we propose a numeric threshold (≥4 LVSI-involved vessels in at least one H&E slide) for defining clinically relevant LVSI in EC, thereby adding supportive data to the semiquantitative approach. This will help guide gynecologic pathologists to differentiate between focal and substantial LVSI, especially in borderline cases

Molecular Classification Predicts Response to Radiotherapy in the Randomized PORTEC-1 and PORTEC-2 Trials for Early-Stage Endometrioid Endometrial Cancer

PURPOSE: The molecular classification of endometrial cancer (EC) has proven to have prognostic value and is predictive of response to adjuvant chemotherapy. Here, we investigate its predictive value for response to external beam radiotherapy (EBRT) and vaginal brachytherapy (VBT) in early-stage endometrioid EC (EEC). METHODS: Data of the randomized PORTEC-1 trial (n = 714) comparing pelvic EBRT with no adjuvant therapy in early-stage intermediate-risk EC and the PORTEC-2 trial (n = 427) comparing VBT with EBRT in early-stage high-intermediate-risk EC were used. Locoregional (including vaginal and pelvic) recurrence-free survival was compared between treatment groups across the four molecular classes using Kaplan-Meier's methodology and log-rank tests. RESULTS: A total of 880 molecularly classified ECs, 484 from PORTEC-1 and 396 from PORTEC-2, were included. The majority were FIGO-2009 stage I EEC (97.2%). The median follow-up was 11.3 years. No locoregional recurrences were observed in EC with a pathogenic mutation of DNA polymerase-e (POLEmut EC). In mismatch repair-deficient (MMRd) EC, locoregional recurrence-free survival was similar after EBRT (94.2%), VBT (94.2%), and no adjuvant therapy (90.3%; P = .74). In EC with a p53 abnormality (p53abn EC), EBRT (96.9%) had a substantial benefit over VBT (64.3%) and no adjuvant therapy (72.2%; P = .048). In EC with no specific molecular profile (NSMP EC), both EBRT (98.3%) and VBT (96.2%) yielded better locoregional control than no adjuvant therapy (87.7%; P &lt; .0001). CONCLUSION: The molecular classification of EC predicts response to radiotherapy in stage I EEC and may guide adjuvant treatment decisions. Omitting radiotherapy seems to be safe in POLEmut EC. The benefit of radiotherapy seems to be limited in MMRd EC. EBRT yields a significantly better locoregional recurrence-free survival than VBT or no adjuvant therapy in p53abn EC. VBT is the treatment of choice for NSMP EC as it is as effective as EBRT and significantly better than no adjuvant therapy for locoregional tumor control

Risk Factors for Late Persistent Fatigue After Chemoradiotherapy in Patients With Locally Advanced Cervical Cancer: An Analysis From the EMBRACE-I Study

Purpose: This study aimed to evaluate patient- and treatment-related risk factors for late persistent fatigue within the prospective, multicenter EMBRACE-I study. Methods and Materials: Fatigue was prospectively assessed (Common Terminology Criteria for Adverse Events, version 3) at baseline and during regular follow up in 993 patients with locally advanced cervical cancer after treatment with chemoradiotherapy and magnetic resonance imaging-guided brachytherapy. Risk factors for baseline and late persistent fatigue were evaluated with multivariable logistic regression. Late persistent fatigue was defined when either grade ≥1 or ≥2 was scored in at least half of the follow ups. Results: The median follow-up time was 57 months. Baseline fatigue grade ≥1/≥2 (35.8%/6.3%, respectively) was associated with preexisting comorbidities, World Health Organization performance status, being underweight, severe pain, and tumor volume. Late persistent grade ≥1/≥2 fatigue (36.3%/5.8%, respectively) was associated with patient-related factors (baseline fatigue, younger age, obesity) along with the size of irradiated volumes and the level of radiation doses from external beam radiation therapy (EBRT) and brachytherapy (EBRT: V43Gy, V57Gy; EBRT + brachytherapy: V60Gy equivalent dose in 2-Gy fractions). Large-volume lymph node (LN) boost increased the risk for late persistent fatigue grade ≥2 by 18% and 5% in patients with and without baseline fatigue, respectively, compared with no LN boost. The risk for late persistent fatigue grade ≥1 increased by 7% and 4% with V43Gy 3000 cm³ in patients with and without baseline fatigue, respectively. Late persistent grade ≥1 fatigue occurred in 13% of patients without late persistent organ-related symptoms (gastrointestinal, genitourinary, and vaginal) versus 34% to 43%, 50% to 58%, and 73% in patients suffering from persistent symptoms involving 1, 2, or 3 organs, respectively. Conclusions: Late persistent fatigue occurs in a considerable number of patients after chemoradiotherapy, and is associated with patient-related factors, the size of volumes irradiated to intermediate and high EBRT and brachytherapy doses, and other persistent organ-related morbidity. These findings support the importance of ongoing efforts to better tailor the target dose and reduce irradiation of healthy tissue without compromising target coverage, using highly conformal EBRT and brachytherapy techniques