MRI-Based Radiomic Model for Preoperative Risk stratification in Stage I Endometrial Cancer

Chen, Jingya; Gu, Hailei; Fan, Weimin; Wang, Yaohui; Chen, Shuai; Chen, Xiao; Wang, Zhongqiu

doi:10.7150/jca.50872

Cited by 33 publications

(23 citation statements)

References 23 publications

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“…MRI with DWI and DCE sequences can help establish a correct diagnosis [502,503], while 3.0 T multimodal MRI is an important imaging tool for preoperative endometrial cancer staging [504]. MRI quantitative assessments such as tumor area ratio (TAR), tumor volume ratio (TVR), MRI-based wholetumor radiomic signatures, and ADC were found to improve the accuracy of preoperative staging, helping in the risk stratification of endometrial cancer [505][506][507]. The combination of MRI and immunohistochemical examination is a powerful tool for preoperative risk stratification to assist in clinical decision-making for endometrial cancer patients [508].…”

Section: Endometrial Cancermentioning

confidence: 99%

Magnetic Fields and Cancer: Epidemiology, Cellular Biology, and Theranostics

Maffei

2022

IJMS

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Humans are exposed to a complex mix of man-made electric and magnetic fields (MFs) at many different frequencies, at home and at work. Epidemiological studies indicate that there is a positive relationship between residential/domestic and occupational exposure to extremely low frequency electromagnetic fields and some types of cancer, although some other studies indicate no relationship. In this review, after an introduction on the MF definition and a description of natural/anthropogenic sources, the epidemiology of residential/domestic and occupational exposure to MFs and cancer is reviewed, with reference to leukemia, brain, and breast cancer. The in vivo and in vitro effects of MFs on cancer are reviewed considering both human and animal cells, with particular reference to the involvement of reactive oxygen species (ROS). MF application on cancer diagnostic and therapy (theranostic) are also reviewed by describing the use of different magnetic resonance imaging (MRI) applications for the detection of several cancers. Finally, the use of magnetic nanoparticles is described in terms of treatment of cancer by nanomedical applications for the precise delivery of anticancer drugs, nanosurgery by magnetomechanic methods, and selective killing of cancer cells by magnetic hyperthermia. The supplementary tables provide quantitative data and methodologies in epidemiological and cell biology studies. Although scientists do not generally agree that there is a cause-effect relationship between exposure to MF and cancer, MFs might not be the direct cause of cancer but may contribute to produce ROS and generate oxidative stress, which could trigger or enhance the expression of oncogenes.

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Section: Endometrial Cancermentioning

confidence: 99%

Magnetic Fields and Cancer: Epidemiology, Cellular Biology, and Theranostics

Maffei

2022

IJMS

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“…Although most EC can be diagnosed at an early stage, differences in patient characteristics and pathological features can affect the prognosis and treatment choices. Clinically, EC is divided into high-risk and low-risk groups according to FIGO stage, pathological type, pathological grade, and biological behavior [17]. For patients in the low-risk group, lymphadenectomy is not recommended.…”

Section: Discussionmentioning

confidence: 99%

Development of an apparent diffusion coefficient based on nomogram for the preoperative prediction of Ki-67 and p53 expression levels and myometrial infiltration in endometrial cancer

Zhang

Jing

Cao

et al. 2023

Preprint

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Background Endometrial cancer (EC) has been increasing in incidence and mortality rates over the years. To investigate the feasibility of ADC in preoperative non-invasive prediction of myometrial infiltration and Ki-67 and p53 expression levels in patients with EC. Methods we performed the retrospective analysis of 105 patients with EC who underwent preoperative magnetic resonance imaging (MRI) diffusion weighted imaging (DWI) and were confirmed by pathology after operation from January 2017 to December 2021 in our hospital. Two independent radiologists measured the ADC values (ADCmax, ADCmean, and ADCmin) of EC on the ADC image by comparing the MRI enhancement and DWI images, respectively. Statistical methods were used to calculate the correlation between clinical information, ADC values and myometrial infiltration and Ki-67 and p53 expression in EC patients. A nomogram prediction model was constructed and evaluated via receiver operating characteristic (ROC) curve and calibration curve analysis. Results The ADC values were significantly correlated with the myometrial infiltration and Ki-67 and p53 expression levels in EC patients (all P < 0.05). The International Federation of Gynecology and Obstetrics (FIGO) stage only significantly associated with the myometrial infiltration and Ki-67 expression levels in EC patients (all P < 0.05). The ADCmax, ADCmean, and ADCmin were combined with the FIGO stage to construct the nomogram model. The nomogram model, ADCmax, ADCmean, ADCmin, and FIGO stage predicted AUC values of 0.809, 0.707, 0.693, 0.694, and 0.599 for myometrial infiltration, respectively; the AUC values for predicting Ki-67 expression levels were 0.897, 0.879, 0.849, 0.808 and 0.550, respectively. The nomogram model was constructed by combining the ADCmax, ADCmean, ADCmin. The AUC values predicted by the nomogram, ADCmax, ADCmean, ADCmin were 0.665, 0.615, 0.641 and 0.654, respectively. Conclusions The nomogram model based on ADC values combined with the FIGO stage could be a useful method for the preoperative non-invasive assessment of myometrial infiltration and Ki-67 and p53 expression in EC patients.

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“…Veeraravaghan and colleagues proposed an integrated radiomic-clinical classification algorithm that distinguishes MMR-D endometrial tumours from copy number low and copy number high tumours with an AUC of 0.78 (121). Chen and colleagues found that an MRI-based radiomic model had better discrimination than clinical and conventional MRI parameters in predicting low risk endometrial cancer (124). Yan and colleagues showed that radiomic based models can aid the prediction of pelvic lymph node metastasis in endometrial cancer (120).…”

Section: Radiomic Prognostic Profiling Of Endometrial Cancermentioning

confidence: 99%

Current and Emerging Prognostic Biomarkers in Endometrial Cancer

2022

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Endometrial cancer is the most common gynaecological malignancy in high income countries and its incidence is rising. Whilst most women with endometrial cancer are diagnosed with highly curable disease and have good outcomes, a significant minority present with adverse clinico-pathological characteristics that herald a poor prognosis. Prognostic biomarkers that reliably select those at greatest risk of disease recurrence and death can guide management strategies to ensure that patients receive appropriate evidence-based and personalised care. The Cancer Genome Atlas substantially advanced our understanding of the molecular diversity of endometrial cancer and informed the development of simplified, pragmatic and cost-effective classifiers with prognostic implications and potential for clinical translation. Several blood-based biomarkers including proteins, metabolites, circulating tumour cells, circulating tumour DNA and inflammatory parameters have also shown promise for endometrial cancer risk assessment. This review provides an update on the established and emerging prognostic biomarkers in endometrial cancer.

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MRI-Based Radiomic Model for Preoperative Risk stratification in Stage I Endometrial Cancer

Cited by 33 publications

References 23 publications

Magnetic Fields and Cancer: Epidemiology, Cellular Biology, and Theranostics

Magnetic Fields and Cancer: Epidemiology, Cellular Biology, and Theranostics

Development of an apparent diffusion coefficient based on nomogram for the preoperative prediction of Ki-67 and p53 expression levels and myometrial infiltration in endometrial cancer

Current and Emerging Prognostic Biomarkers in Endometrial Cancer

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