Influence of menstrual status and pathological type on the apparent diffusion coefficient in cervical cancer: a primary study

Dong, Yue; Dong, Rui Tong; Zhang, Xiao Miao; Song, Qing Ling; Yu, Tao; Luo, Ya

doi:10.1177/0284185120926897

Cited by 4 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Notably, ADC mean values for cervical cancer according to pathological type, histological grade, depth of stromal infiltration, tumor diameter, lymphovascular invasion, and pelvic lymph node metastasis were significantly different (P<0.05) and clinicopathological characteristics were closely associated with staging and prognosis, with important clinical implications. While earlier reports have supported the predictive value of ADC mean of cervical cancer in pathological type and histological grade (19)(20)(21), consistent with our conclusions, this information was not considered the most valuable for preoperative cervical cancer patients with available tissue pathology. Our experiments showed that depth of stromal infiltration, tumor diameter, lymphovascular invasion, and pelvic lymph node metastasis evaluation with DWI is superior to MRI, and ADC mean values of these pathological factors provide important predictive parameters that can be effectively used to assess the need for postoperative supplementary radiotherapy or chemotherapy, which has important clinical implications.…”

Section: Discussionsupporting

confidence: 82%

Value of diffusion-weighted imaging in preoperative evaluation and prediction of postoperative supplementary therapy for patients with cervical cancer

Liu¹,

Wang²,

Yu³

et al. 2022

Ann Transl Med

Self Cite

View full text Add to dashboard Cite

Background: With the continuous progress of medical imaging technology, evaluation of cervical cancer is increasingly dependent on imaging methods. Diffusion-weighted imaging (DWI) plays an important role, and apparent diffusion coefficient (ADC) value is a unique quantitative parameter in the research of cervical cancer. Methods:In this prospective study, a total of 273 patients diagnosed with stage IB1 to IIIC1 cervical cancer based on the International Federation of Gynecology and Obstetrics (FIGO) 2018 staging guidelines who underwent pelvic 3.0T magnetic resonance imaging (MRI), including MRI and DWI, were enrolled, and the diagnostic value of preoperative staging of cervical cancer was compared between the MRI and DWI groups.The DWI group was used to explore the potential association of mean ADC (ADC mean ) with different pathological characteristics and receiver operating characteristic (ROC) curves of ADC mean generated to predict the appropriate postoperative supplementary therapy. Results:The diagnostic coincidence rate of DWI was higher than that of MRI in preoperative staging of cervical cancer (χ 2 , P<0.05) and determined as stages IB1 + IB2 + IIA1 (90.91%), IB3 + IIA2 (93.48%), and IIIC1p (95.16%). The DWI staging results were consistent with postoperative pathological staging (Kappa value =0.865, P<0.001). We observed significant differences in ADC mean values in relation to pathological type, histological grade, depth of stromal infiltration, tumor diameter, lymphovascular invasion, and pelvic lymph node metastasis of cervical cancer (all P<0.05). The area under the ROC curve (AUC) was 0.815, with the best predictive value for postoperative supplementary therapy in cervical cancer (sensitivity 80.0%, specificity 74.0%) at ADC mean of 0.910×10 −3 mm 2 /s. Conclusions:The DWI is a useful tool for preoperative evaluation of cervical cancer. In local cervical lesions, ADC mean varies in relation to different clinicopathological characteristics and a reference index of <0.910×10 −3 mm 2 /s can be effectively applied to predict the need for postoperative supplementary therapy.

show abstract

Section: Discussionsupporting

confidence: 82%

Value of diffusion-weighted imaging in preoperative evaluation and prediction of postoperative supplementary therapy for patients with cervical cancer

Liu¹,

Wang²,

Yu³

et al. 2022

Ann Transl Med

Self Cite

View full text Add to dashboard Cite

show abstract

“…Given that previous studies suggested the age and menopausal status of patients were also independent factors in predicting the stage of CC [33][34][35], these two clinical factors were incorporated into the clinical-radiomics model. Finally, the clinical-radiomics model containing the rad-score, age, and menopausal status was established to predict the stage in patients with cervical cancer.…”

Section: Discussionmentioning

confidence: 99%

Reduced field-of-view DWI‑derived clinical–radiomics model for the prediction of stage in cervical cancer

et al. 2023

View full text Add to dashboard Cite

Background Pretreatment prediction of stage in patients with cervical cancer (CC) is vital for tailoring treatment strategy. This study aimed to explore the feasibility of a model combining reduced field-of-view (rFOV) diffusion-weighted imaging (DWI)-derived radiomics with clinical features in staging CC. Methods Patients with pathologically proven CC were enrolled in this retrospective study. The rFOV DWI with b values of 0 and 800 s/mm2 was acquired and the clinical characteristics of each patient were collected. Radiomics features were extracted from the apparent diffusion coefficient maps and key features were selected subsequently. A clinical–radiomics model combining radiomics with clinical features was constructed. The receiver operating characteristic curve was introduced to evaluate the predictive efficacy of the model, followed by comparisons with the MR-based subjective stage assessment (radiological model). Results Ninety-four patients were analyzed and divided into training (n = 61) and testing (n = 33) cohorts. In the training cohort, the area under the curve (AUC) of clinical–radiomics model (AUC = 0.877) for staging CC was similar to that of radiomics model (AUC = 0.867), but significantly higher than that of clinical model (AUC = 0.673). In the testing cohort, the clinical–radiomics model yielded the highest predictive performance (AUC = 0.887) of staging CC, even without a statistically significant difference when compared with the clinical model (AUC = 0.793), radiomics model (AUC = 0.846), or radiological model (AUC = 0.823). Conclusions The rFOV DWI-derived clinical–radiomics model has the potential for staging CC, thereby facilitating clinical decision-making.

show abstract

“…The previous study revealed that there was significant difference for progression-free survival between patients with squamous cell carcinoma and those with adenocarcinoma/others [ 10 ]. Moreover, the ADC values of the cervical cancers were different in different pathological types [ 17 ]. The purpose of this study was to investigate the effect of NAT + RH and CCRT on the prognosis of patients with stage IB2 and IIA2 squamous cervical cancer (SCC), and the value of ADC in outcome evaluation of different treatment strategies in the patients with IB2 and IIA2 SCC.…”

Section: Introductionmentioning

confidence: 99%

MRI outcome evaluation in patients with IB2 and IIA2 squamous cervical cancer stages: preliminary results

et al. 2022

View full text Add to dashboard Cite

Objectives To evaluate the therapeutic effect of neoadjuvant therapy (NAT) followed by radical hysterectomy and concurrent chemoradiotherapy (CCRT) in stage IB2 and IIA2 squamous cervical cancer (SCC) and investigate the value of apparent diffusion coefficient (ADC) in outcome evaluation of different treatment strategies in the patients. Methods A total of 149 patients with IB2 and IIA2 SCC who underwent pretreatment MRI and DWI scan were included. Patients were treated with NAT + RH or CCRT. Clinical indices and pathological factors were recorded. The imaging indices were measured including tumor size and tumor ADC values. Intraclass correlation coefficient was employed to evaluate the consistency of the indices measured by two observers. ROC curves were used to evaluate the cutoff values of clinical and imaging indices. Kaplan–Meier and Cox proportional hazard model were used to analyze the independent factors of disease-free survival (DFS). Results The median follow-up period was 42.3 months. SCC-Ag, ADCmax and ADCmin were independent factors for DFS in the entire cohort. SCC-Ag, ADCmin and vascular invasion were independent factors for DFS in NAT + RH group. ADCmax and ADCmin were independent factors for DFS in CCRT group. ADCmin was the strongest independent factor for DFS in NAT + RH group, while ADCmax was that in CCRT group. Conclusion The NAT + RH patients had similar DFS to that of CCRT in IB2 and IIA2 SCC, which could be a potential feasible alternative treatment. ADCmin and ADCmax were more valuable in evaluating the outcome of patients who underwent NAT + RH or CCRT, respectively.

show abstract

Influence of menstrual status and pathological type on the apparent diffusion coefficient in cervical cancer: a primary study

Cited by 4 publications

References 29 publications

Value of diffusion-weighted imaging in preoperative evaluation and prediction of postoperative supplementary therapy for patients with cervical cancer

Value of diffusion-weighted imaging in preoperative evaluation and prediction of postoperative supplementary therapy for patients with cervical cancer

Reduced field-of-view DWI‑derived clinical–radiomics model for the prediction of stage in cervical cancer

MRI outcome evaluation in patients with IB2 and IIA2 squamous cervical cancer stages: preliminary results

Contact Info

Product

Resources

About