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Prognostic models for locally advanced cervical cancer: external validation of the published models.

ABSTRACT: To externally validate the prognostic models for predicting the time-dependent outcome in patients with locally advanced cervical cancer (LACC) who were treated with concurrent chemoradiotherapy in an independent cohort.A historical cohort of 297 women with LACC who were treated with radical concurrent chemoradiotherapy from 1999 to 2014 at the 12 de Octubre University Hospital (H12O), Madrid, Spain. The external validity of prognostic models was quantified regarding discrimination, calibration, measures of overall performance, and decision curve analyses.The review identified 8 studies containing 13 prognostic models. Different (International Federation of Gynecology and Obstetrics [FIGO] stages, parametrium involvement, hydronephrosis, location of positive nodes, and race) but related cohorts with validation cohort (5-year overall survival [OS]=70%; 5-year disease-free survival [DFS]=64%; average age of 50; and over 79% squamous cell) were evaluated. The following models exhibited good external validity in terms of discrimination and calibration but limited clinical utility: the OS model at 3 year from Kidd et al.'s study (area under the receiver operating characteristic curve [AUROC]=0.69; threshold of clinical utility [TCU] between 36% and 50%), the models of DFS at 1 year from Kidd et al.'s study (AUROC=0.64; TCU between 24% and 32%) and 2 years from Rose et al.'s study (AUROC=0.70; TCU between 19% and 58%) and the distant recurrence model at 5 years from Kang et al.'s study (AUROC=0.67; TCU between 12% and 36%).The external validation revealed the statistical and clinical usefulness of 4 prognostic models published in the literature.

SUBMITTER: Lora D

PROVIDER: S-EPMC5540718 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Prognostic models for locally advanced cervical cancer: external validation of the published models.

Lora David D Gómez de la Cámara Agustín A Fernández Sara Pedraza SP Enríquez de Salamanca Rafael R Gómez José Fermín Pérez Regadera JFPR

Journal of gynecologic oncology 20170526 5

<h4>Objective</h4>To externally validate the prognostic models for predicting the time-dependent outcome in patients with locally advanced cervical cancer (LACC) who were treated with concurrent chemoradiotherapy in an independent cohort.<h4>Methods</h4>A historical cohort of 297 women with LACC who were treated with radical concurrent chemoradiotherapy from 1999 to 2014 at the 12 de Octubre University Hospital (H12O), Madrid, Spain. The external validity of prognostic models was quantified rega ...[more]

PMID: 28657220

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Project description:Radiomics analyses commonly apply imaging features of different complexity for the prediction of the endpoint of interest. However, the prognostic value of each feature class is generally unclear. Furthermore, many radiomics models lack independent external validation that is decisive for their clinical application. Therefore, in this manuscript we present two complementary studies. In our modelling study, we developed and validated different radiomics signatures for outcome prediction after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) based on computed tomography (CT) and T2-weighted (T2w) magnetic resonance (MR) imaging datasets of 4 independent institutions (training: 122, validation 68 patients). We compared different feature classes extracted from the gross tumour volume for the prognosis of tumour response and freedom from distant metastases (FFDM): morphological and first order (MFO) features, second order texture (SOT) features, and Laplacian of Gaussian (LoG) transformed intensity features. Analyses were performed for CT and MRI separately and combined. Model performance was assessed by the area under the curve (AUC) and the concordance index (CI) for tumour response and FFDM, respectively. Overall, intensity features of LoG transformed CT and MR imaging combined with clinical T stage (cT) showed the best performance for tumour response prediction, while SOT features showed good performance for FFDM in independent validation (AUC = 0.70, CI = 0.69). In our external validation study, we aimed to validate previously published radiomics signatures on our multicentre cohort. We identified relevant publications on comparable patient datasets through a literature search and applied the reported radiomics models to our dataset. Only one of the identified studies could be validated, indicating an overall lack of reproducibility and the need of further standardization of radiomics before clinical application.

Project description:BackgroundSeveral prognostic models for outcomes after chronic subdural hematoma (CSDH) treatment have been published in recent years. However, these models are not sufficiently validated for use in daily clinical practice. We aimed to assess the performance of existing prediction models for outcomes in patients diagnosed with CSDH.MethodsWe systematically searched relevant literature databases up to February 2021 to identify prognostic models for outcome prediction in patients diagnosed with CSDH. For the external validation of prognostic models, we used a retrospective database, containing data of 2384 patients from three Dutch regions. Prognostic models were included if they predicted either mortality, hematoma recurrence, functional outcome, or quality of life. Models were excluded when predictors were absent in our database or available for < 150 patients in our database. We assessed calibration, and discrimination (quantified by the concordance index C) of the included prognostic models in our retrospective database.ResultsWe identified 1680 original publications of which 1656 were excluded based on title or abstract, mostly because they did not concern CSDH or did not define a prognostic model. Out of 18 identified models, three could be externally validated in our retrospective database: a model for 30-day mortality in 1656 patients, a model for 2 months, and another for 3-month hematoma recurrence both in 1733 patients. The models overestimated the proportion of patients with these outcomes by 11% (15% predicted vs. 4% observed), 1% (10% vs. 9%), and 2% (11% vs. 9%), respectively. Their discriminative ability was poor to modest (C of 0.70 [0.63-0.77]; 0.46 [0.35-0.56]; 0.59 [0.51-0.66], respectively).ConclusionsNone of the examined models showed good predictive performance for outcomes after CSDH treatment in our dataset. This study confirms the difficulty in predicting outcomes after CSDH and emphasizes the heterogeneity of CSDH patients. The importance of developing high-quality models by using unified predictors and relevant outcome measures and appropriate modeling strategies is warranted.

Project description:This study is to compare the survival outcomes of laparoscopic radical hysterectomy (LRH) to those of abdominal radical hysterectomy (ARH) for patients with locally advanced cervical cancer (LACC). Patients with the International Federation of Gynecology and Obstetrics (FIGO) 2009 stage IB2 to IIB LACC who underwent radical hysterectomy between 2001 and 2015 were identified. The disease-free survival (DFS) and overall survival (OS) were compared according to the surgical approach and were adjusted based on clinicopathologic characteristics. A total of 396 patients were included in the study, with 179 (45.2%) and 217 (54.8%) patients in the ARH and LRH groups, respectively. The LRH group showed a significantly lower amount of estimated blood loss, lower blood transfusion rate and shorter length of hospital stay. Overall, there were no significant differences in the 5-year DFS and 5-year OS between the LRH and ARH groups with the Kaplan-Meier method. However, multivariate analyses identified LRH as an independent prognostic factor for a poor DFS (hazard ratio [HR] 2.5; 95% confidence interval [95% CI] 0.19 to 0.87; p = 0.02). The analysis of stage IB2 disease and the squamous subtype (61.9% and 87.9% of all participants, respectively) reached the same conclusion. When stratifying by FIGO stage, the patients with IB2 (n = 348) in the ARH group had a significantly better DFS (HR 0.14, 95% CI 0.05-0.42, p < 0.01) and OS (HR 0.17, 95% CI 0.04-0.67, p = 0.11) than those in the LRH group in the Cox regression model. However, no differences were found in patient with stage IIA1, IIA2, or IIB in Cox regression model. When stratifying by histological types, for the patients with squamous carcinomas (n = 375), in Cox model, ARH had a significantly superior DFS compared with those who underwent LRH (HR 0.45, 95% CI 0.25-0.82, p = 0.01), but the OS was not statistically significant (HR 0.57, 95% CI 0.27-1.20, p = 0.14). However, no differences were found in patient with adenocarcinoma and adenosquamous carcinomas in the Cox model. Therefore, ARH was associated with a higher DFS than LRH in patients with LACC, especially in patients with stage IB2 disease or the squamous subtype.

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Prognostic models for locally advanced cervical cancer: external validation of the published models.

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Prognostic models for locally advanced cervical cancer: external validation of the published models.

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