Dataset Information

Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analysis.

ABSTRACT: Objective: To investigate whether pre-treatment CT-derived radiomic features could be applied for prediction of clinical response to neoadjuvant chemotherapy (NACT) in locally advanced cervical cancer (LACC). Patients and Methods: Two hundred and seventy-seven LACC patients treated with NACT followed by surgery/radiotherapy were included in this multi-institution retrospective study. One thousand and ninety-four radiomic features were extracted from venous contrast enhanced and non-enhanced CT imaging for each patient. Five combined methods of feature selection were used to reduce dimension of features. Radiomics signature was constructed by Random Forest (RF) method in a primary cohort of 221 patients. A combined model incorporating radiomics signature with clinical factors was developed using multivariable logistic regression. Prediction performance was then tested in a validation cohort of 56 patients. Results: Radiomics signature containing pre- and post-contrast imaging features can adequately distinguish chemotherapeutic responders from non-responders in both primary and validation cohorts [AUCs: 0.773 (95% CI, 0.701-0.845) and 0.816 (95% CI, 0.690-0.942), respectively] and remain relatively stable across centers. The combined model has a better predictive performance with an AUC of 0.803 (95% CI, 0.734-0.872) in the primary set and an AUC of 0.821 (95% CI, 0.697-0.946) in the validation set, compared to radiomics signature alone. Both models showed good discrimination, calibration. Conclusion: Newly developed radiomic model provided an easy-to-use predictor of chemotherapeutic response with improved predictive ability, which might facilitate optimal treatment strategies tailored for individual LACC patients.

SUBMITTER: Tian X

PROVIDER: S-EPMC7010718 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analysis.

Tian Xin X Sun Caixia C Liu Zhenyu Z Li Weili W Duan Hui H Wang Lu L Fan Huijian H Li Mingwei M Li Pengfei P Wang Lihui L Liu Ping P Tian Jie J Chen Chunlin C

Frontiers in oncology 20200204

<b>Objective:</b> To investigate whether pre-treatment CT-derived radiomic features could be applied for prediction of clinical response to neoadjuvant chemotherapy (NACT) in locally advanced cervical cancer (LACC). <b>Patients and Methods:</b> Two hundred and seventy-seven LACC patients treated with NACT followed by surgery/radiotherapy were included in this multi-institution retrospective study. One thousand and ninety-four radiomic features were extracted from venous contrast enhanced and non ...[more]

PMID: 32117732

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Project description:ObjectivesTo develop and evaluate machine learning models using baseline and restaging computed tomography (CT) for predicting and early detecting pathological downstaging (pDS) with neoadjuvant chemotherapy in advanced gastric cancer (AGC).MethodsWe collected 292 AGC patients who received neoadjuvant chemotherapy. They were classified into (a) primary cohort (206 patients with 3-4 cycles chemotherapy) for model development and internal validation, (b) testing cohort I (46 patients with 3-4 cycles chemotherapy) for evaluating models' predictive ability before and after the complete course, and (c) testing cohort II (n = 40) for model evaluation on its performance at early treatment. We extracted 1,231 radiomics features from venous phase CT at baseline and restaging. We selected radiomics models based on 28 cross-combination models and measured the areas under the curve (AUC). Our prediction radiomics (PR) model is designed to predict pDS outcomes using baseline CT. Detection radiomics (DR) model is applied to restaging CT for early pDS detection.ResultsPR model achieved promising outcomes in two testing cohorts (AUC 0.750, p = .009 and AUC 0.889, p = .000). DR model also showed a good predictive ability (AUC 0.922, p = .000 and AUC 0.850, p = .000), outperforming the commonly used RECIST method (NRI 39.5% and NRI 35.4%). Furthermore, the improved DR model with averaging outcome scores of PR and DR models showed boosted results in two testing cohorts (AUC 0.961, p = .000 and AUC 0.921, p = .000).ConclusionsCT-based radiomics models perform well on prediction and early detection tasks of pDS and can potentially assist surgical decision-making in AGC patients.Key points• Baseline contrast-enhanced computed tomography (CECT)-based radiomics features were predictive of pathological downstaging, allowing accurate identification of non-responders before therapy. • Restaging CECT-based radiomics features were predictive to achieve pDS after and even at an early stage of neoadjuvant chemotherapy. • Combination of baseline and restaging CECT-based radiomics features was promising for early detection and preoperative evaluation of pathological downstaging of AGC.

Project description:Concurrent chemoradiotherapy is the standard treatment for locally advanced uterine cervical cancer. However, effective neoadjuvant chemotherapy (NAC) can reduce tumor size and facilitate hysterectomy for locally advanced uterine cervical cancer. NAC treatment could improve the prognosis of patients with locally advanced cervical cancer. However, if NAC is ineffective, radiotherapy must be pursued. This causes a delay in initiating the core treatment and results in a worse prognosis. Therefore, the identification of predictive markers of whether NAC is likely to be effective for the treatment of locally advanced uterine cervical cancer could improve patient prognosis. Uncoupling protein 2 (UCP2) is broadly expressed in cancer cells, and suppresses mitochondrial reactive oxygen species (ROS) production. UCP2 contributes to both carcinogenesis and chemoresistance by reducing ROS. Downregulation of UCP2 results in significantly increased cell death following chemotherapy. The present study investigated the association between UCP2 expression and NAC effectiveness. A total of 58 patients with locally advanced uterine cervical cancer (stage IIIA or IIIB) treated at Osaka City University Hospital between April 1995 and March 2010 were examined. Tumor tissue samples were obtained by punch biopsy prior to NAC. UCP2 expression was examined immunohistochemically and scored using a weighted scoring system. Patients were divided into NAC effective (n=34) and ineffective (n=24) groups. Furthermore, UCP2 expression in human uterine cervical cancer cells was inhibited by genipin, and changes in cisplatin sensitivity were examined. UCP2 weighted score was higher in the NAC ineffective group than in the NAC effective group (P=0.038). Additionally, the low UCP2 expression group was more sensitive to NAC than the high UCP2 expression group (P=0.041). Sensitivity to cisplatin was significantly increased when UCP2 was inhibited in human uterine cervical cancer cells in vitro. UCP2 expression may become a predictive marker of whether NAC is effective for patients with locally advanced uterine cervical cancer, which could improve patient prognosis.

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

Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analysis.

Publications

Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analysis.

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