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Cervical cancer is the fourth most common malignancy in the world and the fourth leading cause of cancer-related death in women .
According to statistics , cervical cancer is the fourth most common malignant tumor in the world and the fourth leading cause of cancer-related death in women .
Currently, in clinical practice, imaging examination and sentinel lymph node biopsy are the main means of preoperative evaluation .
Radiomics, as a new imaging tool, can transform medical images into high-dimensional mineable data, and conduct joint analysis of radiomics-derived data and other relevant clinical data to assist clinical treatment strategies .
A study published today in the journal European Radiology develops and validates a clinico- radiomic model to assist in preoperative screening of multimodal treatment candidates and help clinicians select the best treatment strategy for patients with early stage cervical cancer .
Between January 2017 and February 2021, 235 patients with IB1-IIA1 cervical cancer who underwent radical hysterectomy were enrolled and divided into training according to the duration of surgery (n=194, training:validation=8:2) and the test (n=41) set
The clinical-radiological model showed good predictive performance with area under the curve, sensitivity and specificity in the test set of 0.
A Radiomics Framework for Graph Prediction Model Construction
A Radiomics Framework for Graph Prediction Model Construction A Radiomics Framework for GraphPrediction Model ConstructionIn conclusion, this study proposes a clinico-radiomic model combining radiomic features and preoperative clinicopathological factors that can predict the probability of clinical adjuvant radiotherapy preoperatively and assist clinical Screening of multimodal therapeutic candidates in patients with early stage cervical cancer .
In conclusion, this study proposes a clinico-radiomic model combining radiomic features and preoperative clinicopathological factors that can predict the probability of clinical adjuvant radiotherapy preoperatively and assist clinical Screening of multimodal therapeutic candidates in patients with early stage cervical cancer .
Original source :
Yuan Li , Jing Ren , Jun-Jun Yang ,et al .
Yuan Li Jing Ren Jun-Jun Yang ,et al MRI-derived radiomics analysis improves the noninvasive pretreatment identification of multimodality therapy candidates with early-stage cervical cancer 10.
1007/s00330-021-08463-y 10.
1007/s00330-021-08463-y
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