A Prediction Model for Optimal Primary Debulking Surgery Based on Preoperative Computed Tomography Scans and Clinical Factors in Patients With Advanced Ovarian Cancer: A Multicenter Retrospective Cohort Study

Ultrasonic radiomics-based nomogram for preoperative prediction of residual tumor in advanced epithelial ovarian cancer: a multicenter retrospective study

Objectives

To identify radiomic features extracted from ultrasound images and to develop and externally validate a comprehensive model that combines clinical data with ultrasound radiomics features to predict the residual tumor status in patients with advanced epithelial ovarian cancer (OC).

Methods

The study included 112 patients with advanced epithelial OC who underwent preoperative transvaginal ultrasound. Of these, 78 patients were assigned to the development cohort and 34 to the external validation cohort. Tumor contours were manually delineated as regions of interest (ROI) on the ultrasound images, and radiomic features were extracted. The final set of variables was identified using LASSO (least absolute shrinkage and selection operator) regression. Clinical features were also collected and incorporated into the model. A combination model integrating ultrasound radiomics and clinical variables was developed and externally validated. The performance of the predictive models was assessed.

Results

A total of 1,561 radiomic features and 18 clinical features were extracted. The final model included 10 significant ultrasound radiomic variables and 4 clinical features. The comprehensive model outperformed models based on either clinical or radiomic features alone, achieving an accuracy of 0.84, a sensitivity of 0.80, a specificity of 0.75, a precision of 0.88, a positive predictive value of 0.81, a negative predictive value of 0.86, an F1-score of 0.78, and an AUC of 0.82 in the external validation set.

Conclusions

The comprehensive model, which integrated clinical and ultrasound radiomic features, exhibited strong performance and generalizability in preoperatively identifying patients likely to achieve complete resection of all visible disease.

Radiomics combined with clinical and MRI features may provide preoperative evaluation of suboptimal debulking surgery for serous ovarian carcinoma

PURPOSE

To develop and validate a model for predicting suboptimal debulking surgery (SDS) of serous ovarian carcinoma (SOC) using radiomics method, clinical and MRI features.

METHODS

228 patients eligible from institution A (randomly divided into the training and internal validation cohorts) and 45 patients from institution B (external validation cohort) were collected and retrospectively analyzed. All patients underwent abdominal pelvic enhanced MRI scan, including T2-weighted imaging fat-suppressed fast spin-echo (T2FSE), T1-weighted dual-echo magnetic resonance imaging (T1DEI), diffusion weighted imaging (DWI), and T1 with contrast enhancement (T1CE). We extracted, selected and eliminated highly correlated radiomic features for each sequence. Then, Radiomic models were made by each single sequence, dual-sequence (T1CE + T2FSE), and all-sequence, respectively. Univariate and multivariate analyses were performed to screen the clinical and MRI independent predictors. The radiomic model with the highest area under the curve (AUC) was used to combine the independent predictors as a combined model.

RESULTS

The optimal radiomic model was based on dual sequences (T2FSE + T1CE) among the five radiomic models (AUC = 0.720, P < 0.05). Serum carbohydrate antigen 125, the relationship between sigmoid colon/rectum and ovarian mass or mass implanted in Douglas' pouch, diaphragm nodules, and peritoneum/mesentery nodules were considered independent predictors. The AUC of the radiomic-clinical-radiological model was higher than either the optimal radiomic model or the clinical-radiological model in the training cohort (AUC = 0.908 vs. 0.720/0.854).

CONCLUSIONS

The radiomic-clinical-radiological model has an overall algorithm reproducibility and may help create individualized treatment programs and improve the prognosis of patients with SOC.

Construction of a nomogram model for predicting the outcome of debulking surgery for ovarian cancer on the basis of clinical indicators

Objective

This study aimed to investigate the risk factors affecting satisfaction with debulking surgery for ovarian cancer and establish a preoperative clinical predictive model.

Methods

Clinical data from 131 patients who underwent ovarian cancer debulking surgery at Jiangnan University Affiliated Hospital between 2016 and 2022 were collected. Patients were randomly separated into an experimental group and a control group in a 7:3 ratio. On the basis of intraoperative outcomes, patients were grouped as either surgery-satisfactory or surgery-unsatisfactory. Clinical indicators were compared through single-factor analysis between groups. Significantly different factors (p < 0.1) were further analyzed through multivariate logistic regression. A predictive nomogram model was developed and validated by receiver operating characteristic (ROC), calibration, and clinical decision curves.

Results

Single-factor analysis revealed the significance of factors such as albumin levels, alkaline phosphatase (ALP), ECOG scores, CA125, HE4, and lymph node metastasis. Multivariate regression analysis identified albumin levels, ALP, ECOG scores, HE4, and lymph node metastasis as independent risk factors for satisfactory surgical outcomes in patients with ovarian cancer undergoing debulking surgery as (p < 0.05). A clinical predictive model was successfully constructed. ROC curves showed AUC values of 0.818 and 0.796 for the experimental and validation groups, respectively. Internal validation through the bootstrap method confirmed the model's fit in both groups. Meanwhile, the clinical decision curve demonstrated the model's high utility.

Conclusion

Independent risk factors associated with satisfactory tumor reduction in patients with ovarian cancer undergoing debulking surgery included decreased albumin levels, ALP > 137 U/L, ECOG = 1 score, HE4 > 140 pmol/L, and lymph node metastasis. Constructing a clinical predictive model through logistic regression analysis enables individualized testing and maximizes clinical benefits.

PARPis response and outcome of ovarian cancer patients with BRCA1/2 germline mutation and a history of breast cancer

OBJECTIVE

The aim of this study was to determine the poly (ADP-ribose) polymerase inhibitors (PARPis) response and outcome of ovarian cancer (OC) patients with BRCA1/2 germline mutation and a history of breast cancer (BC).

METHODS

Thirty-nine OC patients with BRCA1/2 germline mutation and a history of BC were included. The clinicopathological characteristics, PARPis response and prognosis were analyzed.

RESULTS

The median interval from BC to OC diagnosis was 115.3 months (range=6.4-310.1). A total of 38 patients (38/39, 97.4%) received platinum-based chemotherapy after surgical removal. The majority of these patients were reported to be platinum sensitive (92.1%, 35/38). 21 patients (53.8%) received PARPis treatment with 16 patients (76.2%) for maintenance treatment and 5 patients (5/21, 23.8%) for salvage treatment. The median duration for PARPis maintenance and salvage treatment was 14.9 months (range=2.0-56.9) and 8.2 months (range=5.2-20.7), respectively. In the entire cohort, 5-year progression-free survival (PFS) and overall survival (OS) rate was 33.1% and 78.9%, respectively. Patients with BRCA1 mutation had a non-significantly worse 5-year PFS (28.6% vs. 45.8%, p=0.346) and 5-year OS (76.9% vs. 83.3%, p=0.426) than those with BRCA2 mutation. In patients with stage III-IV (n=31), first line PARPis maintenance treatment associated with a non-significantly better PFS (median PFS: NR vs. 22.4 months; 5-year PFS: 64.3% vs. 21.9%, p=0.096).

CONCLUSION

The current study shows that these patients may have a good response to platinum-based chemotherapy and a favorable survival. And these patients can benefit from PARPis treatment and will likely be suitable candidates for PARPis.

Preoperative serum level of CA153 and a new model to predict the sub-optimal primary debulking surgery in patients with advanced epithelial ovarian cancer

OBJECTIVE

The aim of this study was to establish a preoperative model to predict the outcome of primary debulking surgery (PDS) for advanced ovarian cancer (AOC) patients by combing Suidan predictive model with HE4, CA125, CA153 and ROMA index.

METHODS

76 AOC Patients in revised 2014 International Federation of Gynecology and Obstetrics (FIGO) stage III-IV who underwent PDS between 2017 and 2019 from Yunnan Cancer Hospital were included. Clinical data including the levels of preoperative serum HE4, CA125, CA153 and mid-lower abdominal CT-enhanced scan results were collected. The logistics regression analysis was performed to find factors associated with sub-optimal debulking surgery (SDS). The receiver operating characteristic curve was used to evaluate the predictive performances of selected variables in the outcome of primary debulking surgery. The predictive index value (PIV) model was constructed to predict the outcome of SDS.

RESULTS

Optimal surgical cytoreduction was achieved in 61.84% (47/76) patients. The value for CA125, HE4, CA153, ROMA index and Suidan score was lower in optimal debulking surgery (ODS) group than SDS group. Based on the Youden index, which is widely used for evaluating the performance of predictive models, the best cutoff point for the preoperative serum HE4, CA125, CA153, ROMA index and Suidan score to distinguish SDS were 431.55 pmol/l, 2277 KU/L, 57.19 KU/L, 97.525% and 2.5, respectively. Patients with PIV≥5 may not be able to achieve optimal surgical cytoreduction. The diagnostic accuracy, NPV, PPV and specificity for diagnosing SDS were 73.7%, 82.9%, 62.9% and 72.3%, respectively. In the constructed model, the AUC of the SDS prediction was 0.770 (95% confidence interval: 0.654-0.887), P<0.001.

CONCLUSION

Preoperative serum CA153 level is an important non-invasive predictor of primary SDS in advanced AOC, which has not been reported before. The constructed PIV model based on Suidan's predictive model plus HE4, CA125, CA153 and ROMA index can noninvasively predict SDS in AOC patients, the accuracy of this prediction model still needs to be validated in future studies.

Multitask prediction models for serous ovarian cancer by preoperative CT image assessments based on radiomics

Objective

High-grade serous ovarian cancer (HGSOC) has the highest mortality rate among female reproductive system tumors. Accurate preoperative assessment is crucial for treatment planning. This study aims to develop multitask prediction models for HGSOC using radiomics analysis based on preoperative CT images.

Methods

This study enrolled 112 patients diagnosed with HGSOC. Laboratory findings, including serum levels of CA125, HE-4, and NLR, were collected. Radiomic features were extracted from manually delineated ROI on CT images by two radiologists. Classification models were developed using selected optimal feature sets to predict R0 resection, lymph node invasion, and distant metastasis status. Model evaluation was conducted by quantifying receiver operating curves (ROC), calculating the area under the curve (AUC), De Long's test.

Results

The radiomics models applied to CT images demonstrated superior performance in the testing set compared to the clinical models. The area under the curve (AUC) values for the combined model in predicting R0 resection were 0.913 and 0.881 in the training and testing datasets, respectively. De Long's test indicated significant differences between the combined and clinical models in the testing set (p = 0.003). For predicting lymph node invasion, the AUCs of the combined model were 0.868 and 0.800 in the training and testing datasets, respectively. The results also revealed significant differences between the combined and clinical models in the testing set (p = 0.002). The combined model for predicting distant metastasis achieved AUCs of 0.872 and 0.796 in the training and test datasets, respectively. The combined model displayed excellent agreement between observed and predicted results in predicting R0 resection, while the radiomics model demonstrated better calibration than both the clinical model and combined model in predicting lymph node invasion and distant metastasis. The decision curve analysis (DCA) for predicting R0 resection favored the combined model over both the clinical and radiomics models, whereas for predicting lymph node invasion and distant metastasis, DCA favored the radiomics model over both the clinical model and combined model.

Conclusion

The identified radiomics signature holds potential value in preoperatively evaluating the R0, lymph node invasion and distant metastasis in patients with HGSC. The radiomics nomogram demonstrated the incremental value of clinical predictors for surgical outcome and metastasis estimation.

Prognostic Significance of Preoperative NLR, MLR, and PLR Values in Predicting the Outcome of Primary Cytoreductive Surgery in Serous Epithelial Ovarian Cancer

(1) The degree of cytoreduction achieved during primary debulking surgery (PDS) is an important prognostic factor for the survival of patients with epithelial ovarian cancer (EOC). Our aim was to investigate the prognostic value of preoperative laboratory parameters for the outcome of PDS. (2) We analyzed the preoperative laboratory parameters of 150 serous EOC patients who underwent PDS between 2006 and 2013. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal cut-off values of the variables for predicting the PDS outcome. We used binary logistic regression to examine the independent predictive value of the factors for incomplete cytoreduction. (3) Among the parameters, we established optimal cut-off values for cancer antigen (Ca)-125, neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) to predict the outcome of PDS. The results of binary logistic regression showed that stage (FIGO III-IV), MLR (>0.305), and Ca-125 (>169.15 kU/L) were independent significant predictors of the degree of tumor reduction achieved during PDS. (4) In the future, MLR, especially in combination with other parameters, may be useful in determining prognosis and selecting the best treatment option (PDS or neoadjuvant chemotherapy + interval debulking surgery) for ovarian cancer patients.

Validation of models in predicting residual disease in ovarian cancer: comparing CT urography with PET/CT

BACKGROUND

Many ovarian cancer (OC) residual-disease prediction models were not externally validated after being constructed, the clinical applicability needs to be evaluated.

PURPOSE

To compare computed tomography urography (CTU) with PET/CT in validating models for predicting residual disease in OC.

MATERIAL AND METHODS

A total of 250 patients were included during 2018-2021. The CTU and PET/CT scans were analyzed, generating CT-Suidan, PET-Suidan, CT-Peking Union Medical College Hospital (PUMC), and PET-PUMC models. All imagings were evaluated by two readers independently, then compared to pathology. According to surgical outcomes, all patients were divided into the R0 group, with no visible residual disease, and the R1 group, with any visible residual disease. Logistic regression was used to assess the discrimination and calibration abilities of each model.

RESULTS

CTU and PET/CT showed good diagnostic performance in predicting OC peritoneal metastases based on the Suidan and PUMC model (all the accuracies >0.8). As for model evaluation, the value of correct classification of the CT-Suidan, PET-Suidan, CT-PUMC, and PET-PUMC models was 0.89, 0.84, 0.88, and 0.83, respectively, representing stable calibration. The areas under the curve (AUC) of these models were 0.95, 0.90, 0.91, and 0.90, respectively. Furthermore, the accuracy of these models at the optimal threshold value (score 3) was 0.75, 0.78, 0.80, and 0.80, respectively. All two-paired comparisons of the AUCs and accuracies did not show a significant difference (all P > 0.05).

CONCLUSION

CT-Suidan, CT-PUMC, PET-Suidan, and PET-PUMC models had equal abilities in predicting the residual disease of OC. The CT-PUMC model was recommended for its economic and user-friendly characteristics.

A prediction nomogram for suboptimal debulking surgery in patients with serous ovarian carcinoma based on MRI T1 dual-echo imaging and diffusion-weighted imaging

BACKGROUND

Serous ovarian carcinoma (SOC) has the highest morbidity and mortality among ovarian carcinoma. Accurate identification of the probability of suboptimal debulking surgery (SDS) is critical. This study aimed to develop a preoperative prediction nomogram of SDS for patients with SOC.

METHODS

A prediction model was established including 205 patients of SOC from institution A, and 45 patients from institution B were enrolled for external validation. Multivariate logistic regression was used to screen independent predictors and establish a nomogram to predict the occurrence of SDS.

RESULTS

Multivariate logistic regression demonstrated that the CA-125 level (odds ratio [OR] 8.260, 95% confidence interval [CI] 2.003-43.372), relationship between the sigmoid colon/rectum and ovarian mass (OR 28.701, 95% CI 4.561-286.070), diaphragmatic metastasis (OR 12.369, 95% CI 1.675-274.063), and FIGO stage (OR 32.990, 95% CI 6.623-274.509) were independent predictors for SDS. The area under the curve, concordance index, and 95% CI of the nomogram constructed from the above four factors were 0.951, 0.934, and 0.919-0.982, respectively. The model showed a good fit by the Hosmer-Lemeshow test (training set, p = 0.2475; internal validation set, p = 0.2355; external validation set, p = 0.2707). The external validation proved the reliability of the prediction nomogram. The calibration curve was close to the ideal diagonal line. The decision curve analysis demonstrated a significantly better net benefit. The clinical impact curve indicated good effectiveness in clinical application.

CONCLUSION

A prediction nomogram for SDS in patients with SOC provides gynecologists with an accurate and effective tool for appropriate management.

Evaluation of serum CA125-Tn glycoform in peritoneal dissemination and surgical completeness of high-grade serous ovarian cancer

BACKGROUND

Peritoneal dissemination is the predominant feature of malignant progression in ovarian cancer and is a major cause of poor surgical outcomes and clinical prognoses. Abnormal glycosylation of carbohydrate antigen 125 (CA125) may be involved in peritoneal implantation and metastasis. Here, we evaluated the clinical relevance of CA125-Tn glycoform in the assessment of high-grade serous ovarian cancer (HGSOC).

METHODS

A total of 72 patients diagnosed with HGSOC were included. Pre-treatment serum CA125-Tn levels were measured using an antibody-lectin enzyme-linked immunosorbent assay. The association of CA125-Tn with clinical factors was analyzed in all cases, whereas its association with peritoneal dissemination, residual disease, and progression-free survival was analyzed in stage III-IV cases.

RESULTS

Pre-treatment serum CA125-Tn levels were significantly higher in advanced-stage HGSOC patients than in early-stage patients (P = 0.029). In advanced-stage patients, the pre-treatment CA125-Tn level increased with an increase in Fagotti's score (P = 0.004) and with the extension of peritoneal dissemination (P = 0.011). The pre-treatment CA125-Tn level increased with the volume of residual disease (P = 0.005). The association between CA125-Tn level and suboptimal surgery remained significant even after adjustment for treatment type and stage. Pre-treatment CA125-Tn levels were also related to disease recurrence.

CONCLUSION

Serum CA125-Tn level could be a novel biomarker for peritoneal dissemination and a promising predictor of surgical completeness in ovarian cancer. Patients with lower CA125-Tn levels were more likely to have no residual disease. CA125-Tn could help surgeons to adopt optimized treatment strategies for patients with advanced ovarian cancer as a pre-treatment evaluator.

30-Year Experience With 22 Cases of Malignant Transformation Arising From Ovarian Mature Cystic Teratoma: A Rare Disease

Objective

To investigate the clinical characteristics and survival outcomes of patients with malignant transformation arising from ovarian mature cystic teratoma (MT-MCT).

Methods

This retrospective study included patients with ovarian MCTs at Peking Union Medical College Hospital (PUMCH) during 1990.01-2020.12. When the pathologic histology was MT-MCT, detailed information was collected.

Results

Overall, 7229 ovarian MCT patients and 22 patients with MT-MCT were enrolled. The rate of malignant transformation of all ovarian MCTs was 0.30%. Most patients with MT-MCT were 51 (21–75) years old, and the tumor mass size was 10 (3–30) cm. The typical clinical symptoms were mainly abdominal pain and distension. The levels of tumor markers were elevated on preoperative examination. Early diagnosis could be made by ultrasonic examination, pelvic enhanced MRI and CT. Most patients underwent debulking surgery and adjuvant chemotherapy. The most common histological type to exhibit malignant transformation was squamous cell carcinoma (59.1%), followed by adenocarcinoma (13.6%), carcinoid (9.1%), and borderline tumor (18.2%). The 5-year RFS and OS rates were 54.5% and 81.8%, respectively. Patients with FIGO stage I had the best RFS (P=0.047) and OS (P=0.018), followed by those with FIGO stage II-IV.

Conclusion

MT-MCTs mainly occur in elderly females, are rare and have a poor prognosis. Advanced FIGO stage is a risk factor for survival. Although there is no standard treatment, cytoreductive debulking surgery and adjuvant chemotherapy could be considered. Perimenopausal and menopausal women with MCT should receive surgical treatment.

Overall Survival and Related Factors of Advanced-stage Epithelial Ovarian Cancer Patients Underwent Debulking Surgery in Jakarta, Indonesia: A Single-center Experience

AIM: The worrisome prognosis of advanced-stage epithelial ovarian cancer (EOC) needs a new perspective from developing countries. Thus, we attempted to study the 5-year overall survival (OS) of advanced-stage EOC patients who underwent debulking surgery in an Indonesian tertiary hospital. METHODS: A retrospective study recruited forty-eight subjects between 2013 and 2015. We conducted multiple logistic regression analyses to predict risk factors leading to unwanted disease outcomes. The OS was evaluated through the Kaplan–Meier curve and Log-rank test. Cox proportional hazards regression examined prognostic factors of patients. RESULTS: Prominent characteristics of our patients were middle age (mean: 51.9 ± 8.9 years), obese, with normal menarche onset, multiparous, not using contraception, premenopausal, with serous EOC, and FIGO stage IIIC. The subjects mainly underwent primary debulking surgery (66.8%), with 47.9% of all individuals acquiring optimal results, 77.1% of patients treated had the residual disease (RD), and 52.1% got adjuvant chemotherapy. The risk factor for serous EOC was menopause (odds ratio [OR] = 4.82). The predictors of suboptimal surgery were serous EOC (OR = 8.25) and FIGO stage IV (OR = 11.13). The different OS and median survival were observed exclusively in RD, making it an independent prognostic factor (hazard ratio = 3.50). 5-year A five year OS and median survival for patients with advanced-stage EOC who underwent debulking surgery was 37.5% and 32 months, respectively. Optimal versus suboptimal debulking surgery yielded OS 43.5% versus 32% and median survival of 39 versus 29 months. Both optimal and suboptimal debulking surgery followed with chemotherapy demonstrated an OS 40% lower than those not administered (46.2% and 20%, respectively). The highest 5-year OS was in serous EOC (50%). Meanwhile, the most extended median survival was with mucinous EOC (45 months). CONCLUSION: Chemotherapy following optimal and suboptimal debulking surgery has the best OS among approaches researched in this study. RD is a significant prognostic factor among advanced-stage EOC. Suboptimal surgery outcomes can be predicted by stage and histological subtype.