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

Tian, Xun; Sun, Chao; Liu, Zhenyu; Li, Weili; Duan, Hui; Wang, Lu; Fan, Huijian; Li, Mingwei; Li, Pengfei; Wang, Lihui; Liu, Ping; Tian, Jie; Chen, Chunlin

doi:10.3389/fonc.2020.00077

Cited by 31 publications

(31 citation statements)

References 35 publications

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“…Existing radiomics models for predicting response to systemic chemotherapy used both pre-treatment and post-treatment CT images ( 35 ). However, the post-treatment nature could narrow its extensive utility in clinical therapy decision-making ( 36 ). Thus, pre-treatment images were selected to construct prediction models in the current study.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, pre-treatment images were selected to construct prediction models in the current study. Using a pre-treatment predictive model, clinicians can identify the chemosensitivity of patients, thereby better stratifying patients for more appropriate treatment regimens ( 36 ). As a result, the pre-treatment predictive model may broaden its application in the clinical settings and help personalize treatment and improve prognosis of AGC patients.…”

Section: Discussionmentioning

confidence: 99%

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Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study

et al. 2021

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ObjectiveTo build and assess a pre-treatment dual-energy CT-based clinical-radiomics nomogram for the individualized prediction of clinical response to systemic chemotherapy in advanced gastric cancer (AGC).MethodsA total of 69 pathologically confirmed AGC patients who underwent dual-energy CT before systemic chemotherapy were enrolled from two centers in this retrospective study. Treatment response was determined with follow-up CT according to the RECIST standard. Quantitative radiomics metrics of the primary lesion were extracted from three sets of monochromatic images (40, 70, and 100 keV) at venous phase. Univariate analysis and least absolute shrinkage and selection operator (LASSO) were used to select the most relevant radiomics features. Multivariable logistic regression was performed to establish a clinical model, three monochromatic radiomics models, and a combined multi-energy model. ROC analysis and DeLong test were used to evaluate and compare the predictive performance among models. A clinical-radiomics nomogram was developed; moreover, its discrimination, calibration, and clinical usefulness were assessed.ResultAmong the included patients, 24 responded to the systemic chemotherapy. Clinical stage and the iodine concentration (IC) of the tumor were significant clinical predictors of chemotherapy response (all p < 0.05). The multi-energy radiomics model showed a higher predictive capability (AUC = 0.914) than two monochromatic radiomics models and the clinical model (AUC: 40 keV = 0.747, 70 keV = 0.793, clinical = 0.775); however, the predictive accuracy of the 100-keV model (AUC: 0.881) was not statistically different (p = 0.221). The clinical-radiomics nomogram integrating the multi-energy radiomics signature with IC value and clinical stage showed good calibration and discrimination with an AUC of 0.934. Decision curve analysis proved the clinical usefulness of the nomogram and multi-energy radiomics model.ConclusionThe pre-treatment DECT-based clinical-radiomics nomogram showed good performance in predicting clinical response to systemic chemotherapy in AGC, which may contribute to clinical decision-making and improving patient survival.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study

et al. 2021

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“…A model for predicting chemosensitivity in cervical cancer was developed by analyzing CT image features and chemosensitivity information of 221 patients with locally advanced cervical cancer using the random forest method. The area under the curve (AUC) value of this approach was 0.821 26 …”

Section: Application Of Ai In Cervical Cancer and Cervical Intraepithelial Neoplasiamentioning

confidence: 99%

Application of artificial intelligence in gynecologic malignancies: A review

Sone

Toyohara

Taguchi

et al. 2021

J of Obstet and Gynaecol

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With the development of machine learning and deep learning models, artificial intelligence is now being applied to the field of medicine. In oncology, the use of artificial intelligence for the diagnostic evaluation of medical images such as radiographic images, omics analysis using genome data, and clinical information has been increasing in recent years. There have been increasing numbers of reports on the use of artificial intelligence in the field of gynecologic malignancies, and we introduce and review these studies. For cervical and endometrial cancers, the evaluation of medical images, such as colposcopy, hysteroscopy, and magnetic resonance images, using artificial intelligence is frequently reported. In ovarian cancer, many reports combine the assessment of medical images with the multi‐omics analysis of clinical and genomic data using artificial intelligence. However, few study results can be implemented in clinical practice, and further research is needed in the future.

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“…We are building a cervical cancer imaging database (33,39) to collect the Dicom file data of computed tomography (CT)/ magnetic resonance imaging (MRI) examinations before treatment of cervical cancer. As of September 10, 2020, CT data of 3042 patients with cervical cancer and MRI data of 2843 patients with cervical cancer have been collected, among which 670 patients have both CT and MRI data.…”

Section: Future Plans?mentioning

confidence: 99%

Cohort Profile: Chinese Cervical Cancer Clinical Study

Zhang

Sun

et al. 2021

Front. Oncol.

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Cervical cancer is the fourth most common cancer worldwide, but its incidence varies greatly in different countries. Regardless of incidence or mortality, the burden of cervical cancer in China accounts for approximately 18% of the global burden. The Chinese Cervical Cancer Clinical Study is a hospital-based multicenter open cohort. The major aims of this study include (i) to explore the associations of therapeutic strategies with complications as well as mid- and long-term clinical outcomes; (ii) to widely assess the factors which may have an influence on the prognosis of cervical cancer and then guide the treatment options, and to estimate prognosis using a prediction model for precise post-treatment care and follow-up; (iii) to develop a knowledge base of cervical clinical auxiliary diagnosis and prognosis prediction using artificial intelligence and machine learning approaches; and (iv) to roughly map the burden of cervical cancer in different districts and monitoring the trend in incidence of cervical cancer to potentially inform prevention and control strategies. Patients eligible for inclusion were those diagnosed with cervical cancer, whether during an outpatient visit or hospital admission, at 47 different types of medical institutions in 19 cities of 11 provinces across mainland China between 2004 and 2018. In a total, 63 926 patients with cervical cancer were enrolled in the cohort. Since the project inception, a large number of standardized variables have been collected, including epidemiological characteristics, cervical cancer-related symptoms, physical examination results, laboratory testing results, imaging reports, tumor biomarkers, tumor staging, tumor characteristics, comorbidities, co-infections, treatment and short-term complications. Follow-up was performed at least once every 6 months within the first 5 years after receiving treatment and then annually thereafter. At present, we are developing a cervical cancer imaging database containing Dicom files with data of computed tomography/magnetic resonance imaging examination. Additionally, we are also collecting original pathological specimens of patients with cervical cancer. Potential collaborators are welcomed to contact the corresponding authors, and anyone can submit at least one specific study proposal describing the background, objectives and methods of the study.

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

Cited by 31 publications

References 35 publications

Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study

Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study

Application of artificial intelligence in gynecologic malignancies: A review

Cohort Profile: Chinese Cervical Cancer Clinical Study

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