The clinical and prognostic implication of deep stromal invasion in cervical cancer patients undergoing radical hysterectomy

Zhu, Jun; Cao, Le; Wen, Hao; Bi, Rui; Wu, Xiaohua; Ke, Guihao

doi:10.7150/jca.50752

Cited by 13 publications

(16 citation statements)

References 20 publications

(26 reference statements)

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“…In a cohort of 496 patients, Nanthamongkolkul et al found that deep stromal invasion in radical hysterectomy specimens was an independent factor of lymph node metastasis (OR = 3.5, 95%CI = [1.4–9.1], p = 0.01) [ 8 ]. Zhu et al subdivided 3298 patients according to the ratio of DSI compared to the cervical wall thickness on hysterectomy specimens and they reported rates of lymph node involvement of 24.5% in case of DSI inner full-thickness, 42.8% in case of DSI equal to full-thickness and 66.3% in case of DSI outer full-thickness ( p < 0.01) [ 12 ]. These findings are concordant with our results, which revealed that DSI > 10 mm measured in conization specimens was independently associated with the risk of micrometastatic and macrometastatic SLN.…”

Section: Discussionmentioning

confidence: 99%

“…Although DSI in conization specimens seemed to be correlated with SLN status, this criterion is not predictive for pathologic risk factors on radical hysterectomy specimens [ 25 ]. Nonetheless, measurement of stromal invasion may lack reproducibility whether it is assessed by absolute size in mm [ 4 , 51 ], by third (less than 1/3 or 2/3) [ 12 , 54 ] or subjectively (superficial versus deep) [ 8 ]. While there was correlation with LVSI status and DSI to positive SLN, our results did not indicate any association between tumor size in conization specimens and the risk of micrometastatic/macrometastatic SLN.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have well established risk factors for node involvement, such as tumor size larger than 20 mm, the presence of lymphovascular space invasion (LVSI), deeper stromal invasion and parametrial involvement [ 8 , 9 , 10 , 11 , 12 ]. Nonetheless, results of these studies are based on final pathologic examination of radical hysterectomy specimens and are usually extrapolated for preoperative risk assessment.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Can Conization Specimens Predict Sentinel Lymph Node Status in Early-Stage Cervical Cancer? A SENTICOL Group Study

Balaya

Guani

Mereaux

et al. 2021

Cancers

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Background: The prognosis of patients with cervical cancer is significantly worsened in case of lymph node involvement. The goal of this study was to determine whether pathologic features in conization specimens can predict the sentinel lymph node (SLN) status in early-stage cervical cancer. Methods: An ancillary analysis of two prospective multicentric database on SLN biopsy for cervical cancer (SENTICOL I and II) was carried out. Patients with IA to IB2 2018 FIGO stage, who underwent preoperative conization before SLN biopsy were included. Results: Between January 2005 and July 2012, 161 patients from 25 French centers fulfilled the inclusion criteria. Macrometastases, micrometastases and Isolated tumor cells (ITCs) were found in 4 (2.5%), 6 (3.7%) and 5 (3.1%) patients respectively. Compared to negative SLN patients, patients with micrometastatic and macrometastatic SLN were more likely to have lymphovascular space invasion (LVSI) (60% vs. 29.5%, p = 0.04) and deep stromal invasion (DSI) ≥ 10 mm (50% vs. 17.8%, p = 0.04). Among the 93 patients with DSI < 10 mm and absence of LVSI on conization specimens, three patients (3.2%) had ITCs and only one (1.1%) had micrometastases. Conclusions: Patients with DSI < 10 mm and no LVSI in conization specimens had lower risk of micro- and macrometastatic SLN. In this subpopulation, full node dissection may be questionable in case of SLN unilateral detection.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Can Conization Specimens Predict Sentinel Lymph Node Status in Early-Stage Cervical Cancer? A SENTICOL Group Study

Balaya

Guani

Mereaux

et al. 2021

Cancers

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show abstract

Section: Discussionmentioning

confidence: 99%

“…The definition of deep interstitial invasion of cervical cancer has not been completely unified in different societies or regions (14). The most common cutoff value of deep cervical infiltration is the 1/2 or 1/3 infiltration depth, which is widely used by many studies (6,15,16) With careful consideration of the aforementioned adverse pathological risk factors related to tumor volume, our study showed that the tumor volume had a great predictive value for high-risk patients with a cutoff tumor volume of 3,505 mm 3 . This result reminds us that cervical cancer patients with a tumor volume ≥3.5 cm 3 who underwent surgery as the initial treatment had a high possibility of receiving adjuvant chemoradiotherapy postoperatively.…”

Section: Discussionmentioning

confidence: 99%

Tumor Volume Predicts High-Risk Patients and Guides Initial Chemoradiotherapy for Early Cervical Cancer

et al. 2021

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We evaluated the relationship between the minimum tumor-free margin, tumor volume, and adverse pathological risk factors in early cervical cancer and explored the predictive value of these parameters for different types of risk patients to guide individualized therapeutic strategies. Patients who received the initial treatment of radical operation of cervical cancer and their postoperative pathological reports in our hospital from July 1, 2017, to June 30, 2019, were reviewed. Their minimum tumor-free margin and tumor volume were measured on preoperative magnetic resonance imaging. Student’s t-test and the receiver operating characteristic curve analysis were used for data analysis. A total of 240 patients were included. Adverse pathological risk factors were as follows: deep cervical infiltration, 95 (39.6%) cases; lymph vascular space invasion, 91 (37.9%); lymph node metastasis, 20 (8.3%); parametrial infiltration, 8 (3.3%); tumor diameter ≥4 cm, 7 (2.9%); and positive surgical margin, 1 (0.4%). According to the adverse pathological factors, there were 20 (8.3%) high-risk patients, 50 (20.8%) medium-risk patients, and 170 (70.8%) low-risk patients. The ranges of the minimum tumor-free margin and tumor volume were 0.01–13.5 mm and 105–27,990 mm3, respectively. The minimum tumor-free margin with lymph node metastasis was significantly smaller than that without (P <0.05). The tumor volume with parametrial infiltration, deep cervical infiltration, or lymph vascular space invasion was significantly greater than that without (P < 0.05). The tumor volume was significantly different among low-, medium-, and high-risk patients (P <0.05). Tumor volume was of predictive value for high-risk patients (P < 0.05). With 3,505 mm3 as the cutoff value, the sensitivity and specificity for the prediction of high-risk patients were 88.9% and 84.8%, respectively. Tumor volume can be used as a great predictor of high-risk patients (cutoff value, 3,505 mm3), which could be an indication of initial chemoradiotherapy for early cervical cancer.

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Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics Model for Preoperative Predicting the Deep Stromal Invasion in Patients with Early Cervical Cancer

Yan,

Huang,

Yang

et al. 2024

J Digit Imaging. Inform. med.

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Deep stromal invasion is an important pathological factor associated with the treatments and prognosis of cervical cancer patients. Accurate determination of deep stromal invasion before radical hysterectomy (RH) is of great value for early clinical treatment decision-making and improving the prognosis of these patients. Machine learning is gradually applied in the construction of clinical models to improve the accuracy of clinical diagnosis or prediction, but whether machine learning can improve the preoperative diagnosis accuracy of deep stromal invasion in patients with cervical cancer was still unclear. This cross-sectional study was to construct three preoperative diagnostic models for deep stromal invasion in patients with early cervical cancer based on clinical, radiomics, and clinical combined radiomics data using the machine learning method. We enrolled 229 patients with early cervical cancer receiving RH combined with pelvic lymph node dissection (PLND). The least absolute shrinkage and selection operator (LASSO) and the fivefold cross-validation were applied to screen out radiomics features. Univariate and multivariate logistic regression analyses were applied to identify clinical predictors. All subjects were divided into the training set (n = 160) and testing set (n = 69) at a ratio of 7:3. Three light gradient boosting machine (LightGBM) models were constructed in the training set and verified in the testing set. The radiomics features were statistically different between deep stromal invasion < 1/3 group and deep stromal invasion ≥ 1/3 group. In the training set, the area under the curve (AUC) of the prediction model based on radiomics features was 0.951 (95% confidence interval (CI) 0.922–0.980), the AUC of the prediction model based on clinical predictors was 0.769 (95% CI 0.703–0.835), and the AUC of the prediction model based on radiomics features and clinical predictors was 0.969 (95% CI 0.947–0.990). The AUC of the prediction model based on radiomics features and clinical predictors was 0.914 (95% CI 0.848–0.980) in the testing set. The prediction model for deep stromal invasion in patients with early cervical cancer based on clinical and radiomics data exhibited good predictive performance with an AUC of 0.969, which might help the clinicians early identify patients with high risk of deep stromal invasion and provide timely interventions.

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The clinical and prognostic implication of deep stromal invasion in cervical cancer patients undergoing radical hysterectomy

Cited by 13 publications

References 20 publications

Can Conization Specimens Predict Sentinel Lymph Node Status in Early-Stage Cervical Cancer? A SENTICOL Group Study

Can Conization Specimens Predict Sentinel Lymph Node Status in Early-Stage Cervical Cancer? A SENTICOL Group Study

Tumor Volume Predicts High-Risk Patients and Guides Initial Chemoradiotherapy for Early Cervical Cancer

Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics Model for Preoperative Predicting the Deep Stromal Invasion in Patients with Early Cervical Cancer

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