Dataset Information

Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study.

ABSTRACT:

Background

We aimed to investigate whether pre-therapeutic radiomic features based on magnetic resonance imaging (MRI) can predict the clinical response to neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC).

Methods

A total of 275 patients with LACC receiving NACT were enrolled in this study from eight hospitals, and allocated to training and testing sets (2:1 ratio). Three radiomic feature sets were extracted from the intratumoural region of T1-weighted images, intratumoural region of T2-weighted images, and peritumoural region of T2-weighted images before NACT for each patient. With a feature selection strategy, three single sequence radiomic models were constructed, and three additional combined models were constructed by combining the features of different regions or sequences. The performance of all models was assessed using receiver operating characteristic curve.

Findings

The combined model of the intratumoural zone of T1-weighted images, intratumoural zone of T2-weighted images,and peritumoural zone of T2-weighted images achieved an AUC of 0.998 in training set and 0.999 in testing set, which was significantly better (p?InterpretationThis study demonstrated that MRI-based radiomic features hold potential in the pretreatment prediction of response to NACT in LACC, which could be used to identify rightful patients for receiving NACT avoiding unnecessary treatment.

SUBMITTER: Sun C

PROVIDER: S-EPMC6712288 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Publications

Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study.

Sun Caixia C Tian Xin X Liu Zhenyu Z Li Weili W Li Pengfei P Chen Jiaming J Zhang Weifeng W Fang Ziyu Z Du Peiyan P Duan Hui H Liu Ping P Wang Lihui L Chen Chunlin C Tian Jie J

EBioMedicine 20190806

<h4>Background</h4>We aimed to investigate whether pre-therapeutic radiomic features based on magnetic resonance imaging (MRI) can predict the clinical response to neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC).<h4>Methods</h4>A total of 275 patients with LACC receiving NACT were enrolled in this study from eight hospitals, and allocated to training and testing sets (2:1 ratio). Three radiomic feature sets were extracted from the intratumoural region of ...[more]

PMID: 31395503

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Project description:BackgroundTo investigate whether poor glycemic control status has a negative impact on survival outcomes and tumor response to chemotherapy in patients receiving neoadjuvant chemotherapy (NACT) for locally advanced cervical cancer (LACC).MethodsA retrospective cohort study was conducted to examine LACC patients undergoing NACT and radical hysterectomy between 2002 and 2011. Patients were divided into three groups: patients without diabetes mellitus (DM), diabetic patients with good glycemic control, and diabetic patients with poor glycemic control. Hemoglobin A1c (HbA1c) levels were used to indicate glycemic control status. Recurrence-free survival (RFS), cancer-specific survival (CSS) and overall survival (OS) were analyzed using log-rank tests and Cox proportional hazards models.ResultsIn total, 388 patients were included and had a median follow-up time of 39 months (range: 4-67 months). Diabetes mellitus (DM) was diagnosed in 89 (22.9%) patients, only 35 (39.3%) of whom had good glycemic control prior to NACT (HbA1c < 7.0%). In survival analysis, compared with patients with good glycemic control and patients without DM, patients with poor glycemic control (HbA1c ? 7.0%) exhibited decreased recurrence-free survival (RFS), cancer-specific survival (CSS) and overall survival (OS). In multivariate analysis, HbA1c ? 7.0% was identified as an independent predictor for decreased RFS (hazard ratio [HR] = 3.33, P < 0.0001), CSS (HR = 3.60, P < 0.0001) and OS (HR = 4.35, P < 0.0001). In the subgroup of diabetic patients, HbA1c ? 7.0% prior to NACT had an independent negative effect on RFS (HR = 2.18, P = 0.044) and OS (HR = 2.29, P = 0.012). When examined as a continuous variable, the HbA1c level was independently associated with decreased RFS (HR = 1.39, P = 0.002), CSS (HR = 1.28, P = 0.021) and OS (HR = 1.27, P = 0.004). Both good (odds ratio [OR] = 0.06, P < 0.0001) and poor glycemic control (OR = 0.04, P < 0.0001) were independently associated with a decreased likelihood of complete response following NACT.ConclusionsPoor glycemic control is an independent predictor of survival and tumor response to chemotherapy for patients receiving NACT for LACC.

Project description:BackgroundSensitivity to neoadjuvant chemotherapy in locally advanced gastric cancer patients varies; however, an effective predictive marker is currently lacking. We aimed to propose and validate a practical treatment efficacy prediction method based on contrast-enhanced computed tomography (CECT) radiomics.MethodData of l24 locally advanced gastric carcinoma patients who underwent neoadjuvant chemotherapy were acquired retrospectively between December 2012 and August 2020 from three different cancer centers. In total, 1216 radiomics features were initially extracted from each lesion's pretreatment portal venous phase computed tomography image. Subsequently, a radiomics predictive model was constructed using machine learning software. Clinicopathological data and radiological parameters of the enrolled patients were collected and analyzed retrospectively. Univariate and multivariate logistic regression analyses were performed to screen for independent predictive indices. Finally, we developed an integrated model combining clinicopathological predictive parameters and radiomics features.ResultIn the training set, 10 (14.9%) patients achieved a good response (GR) after preoperative neoadjuvant chemotherapy (n = 77), whereas in the testing set, seven (17.5%) patients achieved a GR (n = 47). The radiomics predictive model showed competitive prediction efficacy in both the training and independent external validation sets. The areas under the curve (AUC) values were 0.827 (95% confidence interval [CI]: 0.609-1.000) and 0.854 (95% CI: 0.610-1.000), respectively. Similarly, when only the single hospital data were included as an independent external validation set (testing set 2), AUC values of the models were 0.827 (95% CI: 0.650-0.952) and 0.889 (95% CI: 0.663-1.000) in the training set and testing set 2, respectively.ConclusionOur study is the first to discover that CECT radiomics could provide powerful and consistent predictions of therapeutic sensitivity to neoadjuvant chemotherapy among gastric cancer patients across different hospitals.

Dataset Information

Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study.

Background

Methods

Findings

Publications

Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study.

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