A multicenter validation of computerized tomography models as predictors of non- optimal primary cytoreduction of advanced epithelial ovarian cancer

Development of a prediction model for gross residual in high-grade serous ovarian cancer by combining preoperative assessments of abdominal and pelvic metastases and multiparametric MRI

RATIONALE AND OBJECTIVES

To preoperatively predict residual tumor (RT) in patients with high-grade serous ovarian carcinoma (HGSOC) via a radiomic-clinical nomogram.

METHODS

A total of 128 patients with advanced HGSOC were enrolled (training cohort: n=106; validation cohort: n=22). Serum cancer antigen-125 (CA125), serum human epididymis protein 4 (HE-4) level, and neutrophil-to-lymphocyte ratio (NLR) were obtained from the medical records. Metastases in abdomen and pelvis (MAP) of HGSOC patients was evaluated and scored based on preoperative abdominal and pelvic enhanced CT, MRI and/or PET-CT. A volume of interest (VOI) of each tumor was manually contoured along the boundary slice-by-slice. Radiomic features were extracted from the T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images. Univariate and multivariate analyses were used to determine the independent predictors of RT status. Least absolute shrinkage and selection operator (LASSO) logistic regression was performed to select optimal features and construct radiomic models. A radiomic-clinical nomogram incorporating radiomic signature and clinical parameters was developed and evaluated in training and validation cohorts.

RESULTS

MAP score (p = 0.002), HE-4 level (p = 0.001) and NLR (p = 0.008) were independent predictors of RT status. The final radiomic-clinical nomogram showed satisfactory prediction performance in training (AUC = 0.936), cross validation (AUC = 0.906) and separate validation cohorts (AUC = 0.900), and fitted well in calibration curves (p > 0.05). Decision curve further confirmed the clinical application value of the nomogram.

CONCLUSION

The proposed MRI-based radiomic-clinical nomogram achieved excellent preoperative prediction of the RT status in HGSOC.

The BUMPy road of peritoneal metastases in ovarian cancer

Ovarian cancer is the most common cause of death due to gynecologic malignancies, with more than 70% of patients presenting with advanced stage disease at the time of diagnosis. The extent and distribution of tumor guide primary treatment selection and clinical management. While primary cytoreductive surgery with complete tumor resection improves survival, patients with extensive peritoneal disease may benefit from neoadjuvant chemotherapy first to reduce tumor burden followed by interval cytoreductive surgery. Imaging plays an essential role in triaging patients including selecting patients who may benefit from neoadjuvant chemotherapy before cytoreductive surgery. Interestingly, there are no universally established criteria to predict resectability and local practices depend on local guidelines and surgeon preferences. Nevertheless, certain anatomical tumor locations are known to be difficult to resect and are associated with suboptimal cytoreduction or require special surgical considerations. This review discusses the recent advances in the initial management of patients with ovarian cancer, a practical approach to the assessment and communication of peritoneal metastases locations on CT and MRI. It also explores recent advances in genomics profiling and radiomics that may influence the initial management of these patients.

Construction and Verification of a Predictive Model for Risk Factors in Children With Severe Adenoviral Pneumonia

OBJECTIVE

To construct and validate a predictive model for risk factors in children with severe adenoviral pneumonia based on chest low-dose CT imaging and clinical features.

METHODS

A total of 177 patients with adenoviral pneumonia who underwent low-dose CT examination were collected between January 2019 and August 2019. The assessment criteria for severe pneumonia were divided into mild group (N = 125) and severe group (N = 52). All cases divided into training cohort (N = 125) and validation cohort (N = 52). We constructed a prediction model by drawing a nomogram and verified the predictive efficacy of the model through the ROC curve, calibration curve and decision curve analysis.

RESULTS

The difference was statistically significant (P < 0.05) between the mild adenovirus pneumonia group and the severe adenovirus pneumonia group in gender, age, weight, body temperature, L/N ratio, LDH, ALT, AST, CK-MB, ADV DNA, bronchial inflation sign, emphysema, ground glass sign, bronchial wall thickening, bronchiectasis, pleural effusion, consolidation score, and lobular inflammation score. Multivariate logistic regression analysis showed that gender, LDH value, emphysema, consolidation score, and lobular inflammation score were severe independent risk factors for adenovirus pneumonia in children. Logistic regression was employed to construct clinical model, imaging semantic feature model, and combined model. The AUC values of the training sets of the three models were 0.85 (0.77-0.94), 0.83 (0.75-0.91), and 0.91 (0.85-0.97). The AUC of the validation set was 0.77 (0.64-0.91), 0.83 (0.71-0.94), and 0.85 (0.73-0.96), respectively. The calibration curve fit good of the three models. The clinical decision curve analysis demonstrates the clinical application value of the nomogram prediction model.

CONCLUSION

The prediction model based on chest low-dose CT image characteristics and clinical characteristics has relatively clear predictive value in distinguishing mild adenovirus pneumonia from severe adenovirus pneumonia in children and might provide a new method for early clinical prediction of the outcome of adenovirus pneumonia in children.

ESGO/ISUOG/IOTA/ESGE Consensus Statement on preoperative diagnosis of ovarian tumors

The European Society of Gynaecological Oncology (ESGO), the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG), the International Ovarian Tumour Analysis (IOTA) group and the European Society for Gynaecological Endoscopy (ESGE) jointly developed clinically relevant and evidence-based statements on the preoperative diagnosis of ovarian tumors, including imaging techniques, biomarkers and prediction models. ESGO/ISUOG/IOTA/ESGE nominated a multidisciplinary international group, including expert practising clinicians and researchers who have demonstrated leadership and expertise in the preoperative diagnosis of ovarian tumors and management of patients with ovarian cancer (19 experts across Europe). A patient representative was also included in the group. To ensure that the statements were evidence-based, the current literature was reviewed and critically appraised. Preliminary statements were drafted based on the review of the relevant literature. During a conference call, the whole group discussed each preliminary statement and a first round of voting was carried out. Statements were removed when consensus among group members was not obtained. The voters had the opportunity to provide comments/suggestions with their votes. The statements were then revised accordingly. Another round of voting was carried out according to the same rules to allow the whole group to evaluate the revised version of the statements. The group achieved consensus on 18 statements. This Consensus Statement presents these ESGO/ISUOG/IOTA/ESGE statements on the preoperative diagnosis of ovarian tumors and the assessment of carcinomatosis, together with a summary of the evidence supporting each statement.

ESGO/ISUOG/IOTA/ESGE Consensus Statement on pre-operative diagnosis of ovarian tumors

The European Society of Gynaecological Oncology (ESGO), the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG), the International Ovarian Tumour Analysis (IOTA) group, and the European Society for Gynaecological Endoscopy (ESGE) jointly developed clinically relevant and evidence-based statements on the pre-operative diagnosis of ovarian tumors, including imaging techniques, biomarkers, and prediction models. ESGO/ISUOG/IOTA/ESGE nominated a multidisciplinary international group, including expert practising clinicians and researchers who have demonstrated leadership and expertise in the pre-operative diagnosis of ovarian tumors and management of patients with ovarian cancer (19 experts across Europe). A patient representative was also included in the group. To ensure that the statements were evidence-based, the current literature was reviewed and critically appraised. Preliminary statements were drafted based on the review of the relevant literature. During a conference call, the whole group discussed each preliminary statement and a first round of voting was carried out. Statements were removed when a consensus among group members was not obtained. The voters had the opportunity to provide comments/suggestions with their votes. The statements were then revised accordingly. Another round of voting was carried out according to the same rules to allow the whole group to evaluate the revised version of the statements. The group achieved consensus on 18 statements. This Consensus Statement presents these ESGO/ISUOG/IOTA/ESGE statements on the pre-operative diagnosis of ovarian tumors and the assessment of carcinomatosis, together with a summary of the evidence supporting each statement.

ESGO/ISUOG/IOTA/ESGE Consensus Statement on preoperative diagnosis of ovarian tumours

The European Society of Gynaecological Oncology (ESGO), the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG), the International Ovarian Tumour Analysis (IOTA) group and the European Society for Gynaecological Endoscopy (ESGE) jointly developed clinically relevant and evidence-based statements on the preoperative diagnosis of ovarian tumours, including imaging techniques, biomarkers and prediction models. ESGO/ISUOG/IOTA/ESGE nominated a multidisciplinary international group, including expert practising clinicians and researchers who have demonstrated leadership and expertise in the preoperative diagnosis of ovarian tumours and management of patients with ovarian cancer (19 experts across Europe). A patient representative was also included in the group. To ensure that the statements were evidence-based, the current literature was reviewed and critically appraised. Preliminary statements were drafted based on the review of the relevant literature. During a conference call, the whole group discussed each preliminary statement and a first round of voting was carried out. Statements were removed when a consensus among group members was not obtained. The voters had the opportunity to provide comments/suggestions with their votes. The statements were then revised accordingly. Another round of voting was carried out according to the same rules to allow the whole group to evaluate the revised version of the statements. The group achieved consensus on 18 statements. This Consensus Statement presents these ESGO/ISUOG/IOTA/ESGE statements on the preoperative diagnosis of ovarian tumours and the assessment of carcinomatosis, together with a summary of the evidence supporting each statement.

The Significance of Preoperative Computed Tomography for Predicting Optimal Cytoreduction in Advanced Ovarian Cancer

Introduction Optimal cytoreduction is the most important prognostic factor in advanced ovarian cancer. Although staging and assessment of operability are made by exploratory surgery, preoperative computed tomography (CT) of the abdomen is regarded as standard. The aim of this study was to examine various CT parameters with regard to prediction of optimal cytoreduction. Patients and Methods The retrospective study included 131 patients with ovarian cancer newly diagnosed between 2010 and 2014. Of these, n = 36 with FIGO stage I to IIB were excluded from the study. A preoperative abdominal CT was available for n = 75 of the 95 patients with FIGO stage IIC to IV. The CT scans underwent blinded review. The 11 evaluated CT parameters were examined by means of χ 2 test and logistic regression analysis with regard to the endpoints of macroscopic residual tumour and residual tumour > 1 cm. Survival analyses used the Kaplan-Meier method and log rank test. Results Of 75 patients, 28 (37.3%) had complete tumour resection and 26 (34.7%) had residual tumour ≤ 1 cm. Residual tumours > 1 cm were found in 21 (28%) patients, five of which were not resectable. Overall survival with residual tumour > 1 cm differed significantly from the group with no macroscopic residual tumour (p = 0.003) and with residual tumour ≤ 1 cm (p = 0.04). The CT parameters tumour foci in the diaphragm, mesocolon, greater omentum and peritoneum as well as ascites correlated with macroscopic residual tumour. In the multivariate logistic regression analysis only the CT parameter intraparenchymal liver metastasis was statistically significant with regard to prediction of suboptimal tumour resection (> 1 cm) (OR 8.04; 95% CI 1.57 - 42.4; p = 0.0134). The sensitivity, specificity, PPV and NPV were 37.5, 89.7, 66.7 and 72.2%. Conclusion Although risk parameters for suboptimal tumour reduction can be identified by CT of the abdomen, surgical exploration with histological confirmation of the diagnosis is essential because of the poor diagnostic accuracy.

Molecular Imaging of Peritoneal Carcinomatosis in Ovarian Carcinoma

OBJECTIVE. This article discusses the emerging roles of ¹⁸F-FDG PET/CT and DWI in the assessment of peritoneal carcinomatosis in ovarian carcinoma from diagnostic accuracy to disease prognostication with gross pathologic correlation. CONCLUSION. PET/CT and DWI have incremental clinical values over conventional modalities with high predictive values of incomplete cytoreduction in ovarian carcinoma. The respective quantitative metrics offer evaluation of tumor burden with prognostic value in ovarian carcinoma.

A Nomogram Combined Radiomic and Semantic Features as Imaging Biomarker for Classification of Ovarian Cystadenomas

Objective: To construct and validate a combined Nomogram model based on radiomic and semantic features to preoperatively classify serous and mucinous pathological types in patients with ovarian cystadenoma.

Methods: A total of 103 patients with pathology-confirmed ovarian cystadenoma who underwent CT examination were collected from two institutions. All cases divided into training cohort (N = 73) and external validation cohort (N = 30). The CT semantic features were identified by two abdominal radiologists. The preprocessed initial CT images were used for CT radiomic features extraction. The LASSO regression were applied to identify optimal radiomic features and construct the Radscore. A Nomogram model was constructed combining the Radscore and the optimal semantic feature. The model performance was evaluated by ROC analysis, calibration curve and decision curve analysis (DCA).

Result: Five optimal features were ultimately selected and contributed to the Radscore construction. Unilocular/multilocular identification was significant difference from semantic features. The Nomogram model showed a better performance in both training cohort (AUC = 0.94, 95%CI 0.86–0.98) and external validation cohort (AUC = 0.92, 95%CI 0.76–0.98). The calibration curve and DCA analysis indicated a better accuracy of the Nomogram model for classification than either Radscore or the loculus alone.

Conclusion: The Nomogram model combined radiomic and semantic features could be used as imaging biomarker for classification of serous and mucinous types of ovarian cystadenomas.

Functional tumour burden of peritoneal carcinomatosis derived from DWI could predict incomplete tumour debulking in advanced ovarian carcinoma

OBJECTIVES

To investigate the predictive value of peritoneal carcinomatosis (PC) quantification by DWI in determining incomplete tumour debulking in ovarian carcinoma (OC).

METHODS

Prospective patients with suspected stage III-IV or recurrent OC were recruited for DWI before surgery. PC on DWI was segmented semi-automatically by k-means clustering, retaining voxels with intermediate apparent diffusion coefficient (ADC) to quantify PC burden. A scoring system, functional peritoneal cancer index (fPCI), was proposed based on the segmentation of tumour volume in 13 abdominopelvic regions with additional point given to involvement of critical sites. ADC of the largest PC was recorded. The surgical complexity and outcomes (complete vs. incomplete tumour debulking) were documented. fPCI was correlated with surgical PCI (sPCI), surgical complexity, and its ability to predict incomplete tumour debulking.

RESULTS

Fifty-three patients with stage III-IV or recurrent OC were included with a mean age of 56.1 ± 11.8 years old. Complete tumour debulking was achieved in 38/53 patients (71.7%). Significant correlation was found between fPCI and sPCI (r > 0.757, p < 0.001). Patients with high-fPCI (fPCI ≥ 6) had a high surgical complexity score (p = 0.043) with 84.2% received radical or supra-radical surgery. The mean fPCI was significantly higher in patients with incomplete tumour debulking than in those with complete debulking (10.27 vs. 4.71, p < 0.001). fPCI/ADC combined with The International Federation of Gynecology and Obstetrics stage achieved 92.5% accuracy in predicting incomplete tumour debulking (AUC 0.947).

CONCLUSIONS

DWI-derived fPCI offered a semi-automated estimation of PC burden. fPCI/ADC could predict the likelihood of incomplete tumour debulking with high accuracy.

KEY POINTS

• Functional peritoneal cancer index (fPCI) derived from DWI offered a semi-automated estimation of tumour burden in ovarian carcinoma. • fPCI was highly correlated with surgical PCI (sPCI). • fPCI/ADC could predict the likelihood of incomplete tumour debulking with high accuracy.

Prognostic Significance of Log(CA125)/PCI for the Resectability of Epithelial Ovarian Cancer: A Retrospective Study

Objective

This study aimed to evaluate the roles of the ratio of log(serum CA125 level)/PCI in epithelial ovarian cancer.

Methods

This is a retrospective study. Data were retrieved for patients with epithelial ovarian cancer who received primary debulking surgeries (PDS) between January 2014 and December 2017 in Zhongnan Hospital of Wuhan University. The PCI and CA125 were determined retrospectively using surgical reports, histological findings, and intraoperative photographic documentation. Survival analysis and ROC curves were applied to evaluate the roles of the ratio of log(serum CA125 level)/PCI in epithelial ovarian cancer.

Results

A total of 69 patients were included. Of these, serous ovarian cancer and mucinous carcinoma accounted for 63.8% (n=44) and 31.9% (n=22), respectively. The remaining patients had clear cell carcinoma (2.9%, n=2) and endometrioid carcinoma ( 1.4%, n= 1). Kaplan-Meier survival analysis showed that log(serum CA125 level)/PCI (log-rank p=0.018) were prognostic factors for OS. Cox regression analysis, otherwise, suggested that only stages were an independent factor of PFS (P=0.02, 95% CI 0.043-0.763); outcomes of cytoreductive surgery could only affect OS significantly (P=0.009, 95% CI 1.639-31.016). Binary logistic regression discovered that only log(serum CA125 level)/PCI was an independent risk factor of PDS. We further used the ROC curve to find that log(serum CA125 level)/PCI could correctly predict the resectability of PDS with AUC 0.781.

Conclusion

The ratio of log(CA125)/PCI that combined the tumor burden and characteristics of peritoneal carcinoma of ovarian origin can predict the resectability of PDS in epithelial ovarian cancer.

CT Scan in the Prediction of Lymph Node Involvement in Ovarian Cancer - a Retrospective Analysis of a Tertiary Gyneco-Oncological Unit

Background The prognostic value of lymph node removal in ovarian cancer varies depending on the tumor stage. While in the advanced stage the removal of clinically normal lymph nodes does not improve the prognosis, this is still unclear in the early stages. Evaluation of the lymph nodes based on preoperative imaging influences the surgical procedure.

Methods This retrospective analysis was performed by analyzing data from 114 patients with ovarian cancer, treated in our university hospital in the years 2000 – 2012. Diagnostic performance of imaging by computer tomography with respect to the correct prediction of lymph node status was analyzed in terms of sensitivity, specificity, positive predictive value and negative predictive value.

Results Imaging by computer tomography showed a rather limited diagnostic performance with regard to the detection of lymph node metastases in ovarian cancer, with a sensitivity of 40.7%, a specificity of 89.1%, a positive predictive value of 80.0%, and a negative predictive value of 58.3%. A separate analysis for pelvic and paraaortic lymph node involvement showed a better diagnostic performance of computer tomography for the detection of positive paraaortic lymph nodes (41.2, 93.1, 84.0, and 64.3% for sensitivity, specificity, positive predictive value and negative predictive value, respectively) as compared to the detection of positive pelvic lymph nodes (25.6, 91.8, 62.5, and 69.8%).

Conclusion The preoperative prediction of lymph node status by computer tomography is limited. A decision for or against lymphadenectomy should not be made solely on the basis of this approach.

CT prediction of surgical outcome in patients with advanced epithelial ovarian carcinoma undergoing neoadjuvant chemotherapy

OBJECTIVE

A scoring system has been proposed to predict gross residual disease at primary debulking surgery (PDS) for advanced epithelial ovarian cancer. This scoring system has not been assessed in patients undergoing neoadjuvant chemotherapy (NACT). The aim of this study is to assess the reproducibility and prognostic significance of the scoring system when applied to women undergoing NACT followed by interval debulking surgery (IDS).

METHODS

A retrospective cohort study was conducted of patients with advanced ovarian cancer who underwent NACT and IDS between 2005 and 2014. Change in tumor burden using computed tomography (CT) at diagnosis (T0) and after initiation of NACT but before IDS (T1) was independently assessed by two radiologists blinded to outcomes using two read criteria: a scoring system utilizing clinical and radiologic criteria and RECIST 1.1. Relationship between CT assessments to surgical outcome, progression free survival (PFS) and overall survival (OS) were evaluated. Reader agreement was measured using Fleiss's kappa (ĸ).

RESULTS

76 patients were analyzed. Optimal surgical outcome was achieved in 69 (91%) of patients. Median progression free survival was 13.2 months and overall survival was 32.6 months, respectively. Predictive score change from T0 to T1 of >1 (denoting an improvement in disease burden) was associated with optimal cytoreduction (p = 0.02 and 0.01 for readers 1 and 2, respectively). Neither predictive score nor RECIST 1.1 assessment was predictive of OS or PFS. Reader agreement was substantial for predictive score (κ = 0.77) and moderate for RECIST (κ = 0.51) assessments.

CONCLUSIONS

A change in score before and after neoadjuvant chemotherapy minimizes reader variability and predicts surgical outcome.

Predictive modeling for determination of microscopic residual disease at primary cytoreduction: An NRG Oncology/Gynecologic Oncology Group 182 Study

OBJECTIVE

Microscopic residual disease following complete cytoreduction (R0) is associated with a significant survival benefit for patients with advanced epithelial ovarian cancer (EOC). Our objective was to develop a prediction model for R0 to support surgeons in their clinical care decisions.

METHODS

Demographic, pathologic, surgical, and CA125 data were collected from GOG 182 records. Patients enrolled prior to September 1, 2003 were used for the training model while those enrolled after constituted the validation data set. Univariate analysis was performed to identify significant predictors of R0 and these variables were subsequently analyzed using multivariable regression. The regression model was reduced using backward selection and predictive accuracy was quantified using area under the receiver operating characteristic area under the curve (AUC) in both the training and the validation data sets.

RESULTS

Of the 3882 patients enrolled in GOG 182, 1480 had complete clinical data available for the analysis. The training data set consisted of 1007 patients (234 with R0) while the validation set was comprised of 473 patients (122 with R0). The reduced multivariable regression model demonstrated several variables predictive of R0 at cytoreduction: Disease Score (DS) (p<0.001), stage (p=0.009), CA125 (p<0.001), ascites (p<0.001), and stage-age interaction (p=0.01). Applying the prediction model to the validation data resulted in an AUC of 0.73 (0.67 to 0.78, 95% CI). Inclusion of DS enhanced the model performance to an AUC of 0.83 (0.79 to 0.88, 95% CI).

CONCLUSIONS

We developed and validated a prediction model for R0 that offers improved performance over previously reported models for prediction of residual disease. The performance of the prediction model suggests additional factors (i.e. imaging, molecular profiling, etc.) should be explored in the future for a more clinically actionable tool.

Diagnostic value of whole body diffusion-weighted MRI compared to computed tomography for pre-operative assessment of patients suspected for ovarian cancer

BACKGROUND

Despite excellent per-lesion performance for peritoneal staging, the additional clinical value of diffusion-weighted magnetic resonance imaging (DWI/MRI) compared to computed tomography (CT) remains to be established in ovarian cancer. Our purpose was to evaluate whole body (WB)-DWI/MRI for diagnosis, staging and operability assessment of patients suspected for ovarian cancer compared to CT.

METHODS

One hundred and sixty-one patients suspected for ovarian carcinoma underwent 3 T WB-DWI/MRI and contrast-enhanced CT. WB-DWI/MRI and CT were compared for confirmation of the malignant nature and primary origin of the ovarian mass, Fédération Internationale de Gynécologie et d'Obstétrique (FIGO) staging and prediction of incomplete resection using institutional operability criteria. Interobserver agreement between two readers was determined for WB-DWI/MRI and CT.

RESULTS

WB-DWI/MRI showed a significantly higher accuracy than CT (93 versus 82%, p = 0.001) to confirm the malignant nature of the ovarian mass and correctly identified 26 of 32 (81%) cancers of non-ovarian origin compared to 10/32 (31%) for CT (p < 0.001). WB-DWI/MRI assigned more ovarian carcinoma patients to the correct FIGO stage (82/94, 87%) compared with CT (33/94, 35%). For prediction of incomplete resection, WB-DWI/MRI showed significantly higher sensitivity (94 versus 66%), specificity (97.7 versus 77.3%) and accuracy (95.7 versus 71.3%) compared to CT (p < 0.001). Interobserver agreement was almost perfect (κ = 0.90) for WB-DWI/MRI and moderate (κ = 0.52) for CT for prediction of incomplete resection.

CONCLUSIONS

WB-DWI/MRI was superior to CT for primary tumour characterisation, staging and prediction of incomplete resection in patients suspected for ovarian cancer.

Advanced Primary Epithelial Ovarian and Peritoneal Carcinoma-Does Diagnostic Accuracy of Preoperative CT Scan for Detection of Peritoneal Metastatic Sites Reflect into Prediction of Suboptimal Debulking? A Prospective Study

Ovarian cancer is the seventh most common cancer in females worldwide. Optimal debulking is the standard treatment but possible only in 30-85% of advanced stages. Knowing exactly the disease extent preoperatively may predict suboptimal debulking. We analyzed diagnostic accuracy of preoperative CT scan in disease mapping and prediction of suboptimal debulking in a prospective observational study from March 2013 to May 2015 in a tertiary hospital. Adults below the age of 75 years with ECOG PS-0, 1, 2, clinically/radiologically newly diagnosed stage IIIc epithelial ovarian (EOC), and primary peritoneal carcinoma (PPC) were included. Neoadjuvant chemotherapy recipients were excluded. Preoperative multidetector CT (MDCT) scan showing deposits at 19 predetermined abdominopelvic sites were compared with the same sites seen at laparotomy and corresponding accuracies of CT scan calculated. Primary debulking surgery was done to achieve debulking to nil or less than 1-cm residual disease. Stepwise logistic regression models were used to determine the frequent suboptimal debulking sites and the predictive performance of the clinical and CT scan findings. A total of 36 patients were enrolled. The optimal debulking rate was 50%. The CT scan could detect the disease-bearing sites with overall sensitivity of 68.29%, specificity of 89%, accuracy of 78.07%, and positive and negative predictive values of 99 and 50.1%, respectively. Upon multivariate analysis, bowel mesentery (p 0.011) and omental extension (p 0.025) were associated with suboptimal debulking. CT scan accuracy at these sites (predictive performance) was 86.1%. We identified small bowel mesentery and omental extension (to spleen/stomach/colon) as sites associated with suboptimal debulking. MDCT accurately depicts peritoneal metastases, although sensitivity is reduced in certain areas of significance for optimal debulking. Further validation with more number of patients is warranted.

Correlation of pre-operative CT findings with surgical & histological tumor dissemination patterns at cytoreduction for primary advanced and relapsed epithelial ovarian cancer: A retrospective evaluation

OBJECTIVES

Computed tomography (CT) is an essential part of preoperative planning prior to cytoreductive surgery for primary and relapsed epithelial ovarian cancer (EOC). Our aim is to correlate pre-operative CT results with intraoperative surgical and histopathological findings at debulking surgery.

METHODS

We performed a systematic comparison of intraoperative tumor dissemination patterns and surgical resections with preoperative CT assessments of infiltrative disease at key resection sites, in women who underwent multivisceral debulking surgery due to EOC between January 2013 and December 2014 at a tertiary referral center. The key sites were defined as follows: diaphragmatic involvement(DI), splenic disease (SI), large (LBI) and small (SBI) bowel involvement, rectal involvement (RI), porta hepatis involvement (PHI), mesenteric disease (MI) and lymph node involvement (LNI).

RESULTS

A total of 155 patients, mostly with FIGO stage IIIC disease (65%) were evaluated (primary=105, relapsed=50). Total macroscopic cytoreduction rates were: 89%. Pre-operative CT findings displayed high specificity across all tumor sites apart from the retroperitoneal lymph node status, with a specificity of 65%. The ability however of the CT to accurately identify sites affected by invasive disease was relatively low with the following sensitivities as relating to final histology: 32% (DI), 26% (SI), 46% (LBI), 44% (SBI), 39% (RI), 57% (PHI), 31% (MI), 63% (LNI).

CONCLUSION

Pre-operative CT imaging shows high specificity but low sensitivity in detecting tumor involvement at key sites in ovarian cancer surgery. CT findings alone should not be used for surgical decision making.

Functional MR Imaging in Gynecologic Cancer

Dynamic-contrast enhanced (DCE) and diffusion-weighted (DW) MR imaging are invaluable in the detection, staging, and characterization of uterine and ovarian malignancies, for monitoring treatment response, and for identifying disease recurrence. When used as adjuncts to morphologic T2-weighted (T2-W) MR imaging, these techniques improve accuracy of disease detection and staging. DW-MR imaging is preferred because of its ease of implementation and lack of need for an extrinsic contrast agent. MR spectroscopy is difficult to implement in the clinical workflow and lacks both sensitivity and specificity. If used quantitatively in multicenter clinical trials, standardization of DCE- and DW-MR imaging techniques and rigorous quality assurance is mandatory.

Predicting surgical outcome in patients with International Federation of Gynecology and Obstetrics stage III or IV ovarian cancer using computed tomography: a systematic review of prediction models

OBJECTIVE

Maximal cytoreduction to no residual disease is an important predictor of prognosis in patients with advanced-stage epithelial ovarian cancer. Preoperative prediction of outcome of surgery should guide treatment decisions, for example, primary debulking or neoadjuvant chemotherapy followed by interval debulking surgery. The objective of this study was to systematically review studies evaluating computed tomography imaging based models predicting the amount of residual tumor after cytoreductive surgery for advanced-stage epithelial ovarian cancer.

METHODS

We systematically searched the literature for studies investigating multivariable models that predicted the amount of residual disease after cytoreductive surgery in advanced-stage epithelial ovarian cancer using computed tomography imaging. Detected studies were scored for quality and classified as model derivation or validation studies. We summarized their performance in terms of discrimination when possible.

RESULTS

We identified 11 studies that described 13 models. The 4 models that were externally validated all had a poor discriminative capacity (sensitivity, 15%-79%; specificity, 32%-64%). The only internal validated model had an area under the receiver operating characteristic curve of 0.67. Peritoneal thickening, mesenterial and diaphragm disease, and ascites were most often used as predictors in the final models. We did not find studies that assessed the impact of prediction model on outcomes.

CONCLUSIONS

Currently, there are no external validated studies with a good predictive performance for residual disease. Studies of better quality are needed, especially studies that focus on predicting any residual disease after surgery.

Prediction of incomplete primary debulking surgery in patients with advanced ovarian cancer: An external validation study of three models using computed tomography

OBJECTIVE

To test the ability of three prospectively developed computed tomography (CT) models to predict incomplete primary debulking surgery in patients with advanced (International Federation of Gynecology and Obstetrics stages III-IV) ovarian cancer.

METHODS

Three prediction models to predict incomplete surgery (any tumor residual >1cm in diameter) previously published by Ferrandina (models A and B) and by Gerestein were applied to a validation cohort consisting of 151 patients with advanced epithelial ovarian cancer. All patients were treated with primary debulking surgery in the Eastern part of the Netherlands between 2000 and 2009 and data were retrospectively collected. Three individual readers evaluated the radiographic parameters and gave a subjective assessment. Using the predicted probabilities from the models, the area under the curve (AUC) was calculated which represents the discriminative ability of the model.

RESULTS

The AUC of the Ferrandina models was 0.56, 0.59 and 0.59 in model A, and 0.55, 0.60 and 0.59 in model B for readers 1, 2 and 3, respectively. The AUC of Gerestein's model was 0.69, 0.61 and 0.69 for readers 1, 2 and 3, respectively. AUC values of 0.69 and 0.63 for reader 1 and 3 were found for subjective assessment.

CONCLUSIONS

Models to predict incomplete surgery in advanced ovarian cancer have limited predictive ability and their reproducibility is questionable. Subjective assessment seems as successful as applying predictive models. Present prediction models are not reliable enough to be used in clinical decision-making and should be interpreted with caution.

Development of a prediction model for residual disease in newly diagnosed advanced ovarian cancer

OBJECTIVES

To construct a tool, using computed tomography (CT) imaging and preoperative clinical variables, to estimate successful primary cytoreduction for advanced epithelial ovarian cancer (EOC).

METHODS

Women who underwent primary cytoreductive surgery for stage IIIC/IV EOC at Mayo Clinic between 1/2/2003 and 12/30/2011 and had preoperative CT images of the abdomen and pelvis within 90days prior to their surgery available for review were included. CT images were reviewed for large-volume ascites, diffuse peritoneal thickening (DPT), omental cake, lymphadenopathy (LP), and spleen or liver involvement. Preoperative factors included age, body mass index (BMI), Eastern Cooperative Oncology Group performance status (ECOG PS), American Society of Anesthesiologists (ASA) score, albumin, CA-125, and thrombocytosis. Two prediction models were developed to estimate the probability of (i) complete and (ii) suboptimal cytoreduction (residual disease (RD) >1cm) using multivariable logistic analysis with backward and stepwise variable selection methods. Internal validation was assessed using bootstrap resampling to derive an optimism-corrected estimate of the c-index.

RESULTS

279 patients met inclusion criteria: 143 had complete cytoreduction, 26 had suboptimal cytoreduction (RD>1cm), and 110 had measurable RD ≤1cm. On multivariable analysis, age, absence of ascites, omental cake, and DPT on CT imaging independently predicted complete cytoreduction (c-index=0.748). Conversely, predictors of suboptimal cytoreduction were ECOG PS, DPT, and LP on preoperative CT imaging (c-index=0.685).

CONCLUSIONS

The generated models serve as preoperative evaluation tools that may improve counseling and selection for primary surgery, but need to be externally validated.

Role of minimally invasive surgery in ovarian cancer

The standard treatment of ovarian cancer includes upfront surgery with intent to accurately diagnose and stage the disease and to perform maximal cytoreduction, followed by chemotherapy in most cases. Surgical staging of ovarian cancer traditionally has included exploratory laparotomy with peritoneal washings, hysterectomy, salpingo-oophorectomy, omentectomy, multiple peritoneal biopsies, and possible pelvic and para-aortic lymphadenectomy. In the early 1990s, pioneers in laparoscopic surgery used minimally invasive techniques to treat gynecologic cancers, including laparoscopic staging of early ovarian cancer and primary and secondary cytoreduction in advanced and recurrent disease in selected cases. Since then, the role of minimally invasive surgery in gynecologic oncology has been continually expanding, and today advanced laparoscopic and robotic-assisted laparoscopic techniques are used to evaluate and treat cervical and endometrial cancer. However, the important question about the place of the minimally invasive approach in surgical treatment of ovarian cancer remains to be evaluated and answered. Overall, the potential role of minimally invasive surgery in treatment of ovarian cancer is as follows: i) laparoscopic evaluation, diagnosis, and staging of apparent early ovarian cancer; ii) laparoscopic assessment of feasibility of upfront surgical cytoreduction to no visible disease; iii) laparoscopic debulking of advanced ovarian cancer; iv) laparoscopic reassessment in patients with complete remission after primary treatment; and v) laparoscopic assessment and cytoreduction of recurrent disease. The accurate diagnosis of suspect adnexal masses, the safety and feasibility of this surgical approach in early ovarian cancer, the promise of laparoscopy as the most accurate tool for triaging patients with advanced disease for surgery vs upfront chemotherapy or neoadjuvant chemotherapy, and its potential in treatment of advanced cancer have been documented and therefore should be incorporated in the surgical methods of every gynecologic oncology unit and in the training programs in gynecologic oncology.

A prospective comparison of integrated FDG-PET/contrast-enhanced CT and contrast-enhanced CT for pretreatment imaging of advanced epithelial ovarian cancer

OBJECTIVE

The use of tumor debulking surgery in the management of epithelial ovarian cancer (EOC), which is often disseminated in the peritoneal cavity at the time of diagnosis, has a significant impact on prognosis. We compared (18)F-fluorodeoxyglucose (FDG) positron emission tomography/contrast-enhanced computed tomography (PET/CT) to contrast-enhanced CT for the detection of dissemination into the abdominal cavity preventing successful primary debulking surgery.

METHODS

Forty-one women with EOC underwent preoperative whole-body low-dose FDG-PET/CT followed by diagnostic high dose contrast-enhanced CT scan, and the results were compared with systematically recorded surgical findings as a reference standard. Both site-based and patient-based analyses were conducted.

RESULTS

FDG-PET/CT was superior to conventional CT for the detection of carcinomatosis in subdiaphragmatic peritoneal surfaces (p=0.020) and in the bowel mesentery (p=0.001). Patient-based analysis of upper abdominal areas requiring extensive surgical procedures showed no significant differences between the two imaging methods. The sensitivity of PET/CT and CT was poor in certain areas of the peritoneal cavity (64% vs. 27% in the small bowel mesentery and 65% vs. 55% in the right upper abdomen). Extra-abdominal disease spread was detected by PET/CT in 32 patients and by CT in 25 patients.

CONCLUSIONS

PET/CT was not superior to CT for the detection of intra-abdominal disease spread. Patients with suspected EOC should be referred for upfront radical surgery regardless of the results of preoperative imaging studies. PET/CT is more effective for the detection of extra-abdominal disease than CT, but the clinical significance of this finding is unclear.

The use of CT findings to predict extent of tumor at primary surgery for ovarian cancer

BACKGROUND

High tumor dissemination (HTD) is a major risk factor for serious morbidity after primary ovarian cancer (OC) surgery, particularly in medically compromised patients. We performed a pilot study of whether CT findings could predict extent of disease and surgical complexity necessary in advanced OC.

METHODS

Preoperative CT images for patients with advanced OC from 1997-2003 were evaluated for rigorously defined disease-related findings and compared to both the findings at exploration and the required surgical procedures. Associations were assessed by the chi-square test.

RESULTS

Forty-six cases met inclusion criteria. Mean age was 66.4y, and 76% had residual disease (RD) 1cm or less. CT and surgical findings correlated (sensitivity/specificity) as follows: diaphragm disease (48%/100%); surface liver (100%/93%); omental cake (72%/65%); any sigmoid involvement (54%/100%); ascites (44%/100%); extra-pelvic large bowel involvement (29%/91%). When diaphragm disease and omental cake were present, HTD was found in all cases (positive predictive value and specificity=100%, sensitivity 48%). For CT findings of liver, large bowel and spleen involvement there was a strong trend toward resection (P=0.001, P=0.06 and P=0.06, respectively).

CONCLUSIONS

The findings of diaphragm disease and omental cake on CT scan are highly predictive for high tumor dissemination (HTD) and thus likelihood of extensive surgery required to achieve low residual disease. In addition, multiple CT findings correlate strongly with the need for higher surgical complexity which should facilitate preoperative planning and/or triage to specialized centers. These preliminary data suggest specific CT findings can be used to optimize treatment planning.

Usefulness of computed tomography in predicting cytoreductive surgical outcomes for ovarian cancer

PURPOSE

The objective of this study was to identify features of preoperative computed tomography (CT) scans that can best predict outcomes of primary cytoreductive surgery in ovarian cancer patients.

METHODS

Preoperative CT scans of 98 patients were evaluated retrospectively. Multiple logistic regression analysis was used to develop two models.

RESULTS

Although optimal surgical reduction was attempted in 98 patients, 12 had suboptimal results. Having tumor implants on the small or large bowel mesenteries (any size) or at other sites (cutoff index: ≥ 1 cm) was found to be significant (p < 0.001) for predicting a suboptimal cytoreduction outcome. Two predictive models were created using multiple logistic regression analysis; both consider diffuse peritoneal thickening (DPT), infrarenal para-aortic or pelvic lymph node involvement, a bowel encasement tumor (≥ 2 cm), and any tumor implants in the cul-de-sac as significant. Model 1 adds consideration to any tumors in the pelvic or retroperitoneum and has an accuracy of 90.8% for predicting a suboptimal surgery. Model 2 (accuracy of 93.9%) adds to the core of predictors the presence of tumor implants on the bowel mesenteries (≥ 2 cm), omental caking (≥ 2 cm), and ascites fluid.

CONCLUSION

Using specific CT findings from patients with ovarian cancer, we have devised two predictive models that have an accuracy of greater than 90% for predicting whether cytoreductive surgery will completely remove all tumor tissue, which should greatly aid in the differential decision-making as to whether to attempt cytoreductive surgery first, or to advance directly to neoadjuvant chemotherapy.

[The role of interval surgery in the treatment's strategy of advanced ovarian cancer]

The extent of cytoreductive surgery and the amount of residual disease are among the most important factors impacting the survival of women with advanced ovarian cancer. Chronology of treatment is still debating. In the French standard treatment, primary surgery remains the cornerstone and interval surgery is an option. Neoadjuvant chemotherapy followed by surgery may reduce morbidity and mortality. Nevertheless the impact of this strategy on survival is still controversial.

Role of CT scan-based and clinical evaluation in the preoperative prediction of optimal cytoreduction in advanced ovarian cancer: a prospective trial

BACKGROUND

In advanced ovarian cancer, maximal efforts have to be attemptedto achieve optimal cytoreduction, as this represents the keystone in the therapeutic management. This large, prospective study aims at investigating the role of computed tomography (CT) scan in predicting the feasibility of optimal cytoreduction in ovarian cancer.

METHODS

A total of 195 consecutive patients with clinical/radiographic suspicion of advanced ovarian/peritoneal cancer were enrolled at the Gynecologic Oncology Unit, Catholic University of Rome and Campobasso, Italy. Preoperative CT scans were performed with a high-speed scanner (CT Hi Speed Nx/i Pro; 2-slice; GE Medical System). All patients underwent standard laparotomy, and maximal surgical effort was attempted. The following CT parameters were used: peritoneal thickening, peritoneal implants >2 cm, bowel mesentery involvement, omental cake, pelvic sidewall involvement and/or hydroureter, suprarenal aortic lymph nodes >1 cm, infrarenal aortic lymph nodes >2 cm, superficial liver metastases >2 cm and/or intraparenchimal liver metastases any size, large volume ascites (>500 ml). Clinical data included were age, Ca125 serum levels, and ECOG-PS. Radiographic and clinical features exhibiting a specificity >75%, a positive and negative predictive value >50%, an accuracy >60% in predicting surgical outcome were assigned a point value of 2. With this scoring system, a predictive index (PI) was calculated for each patient.

RESULTS

The PI scores ranged from 0 to 6, and from 0 to 8, in Model 1 (including only radiographic parameters) and in Model 2 (including radiographic and clinical data). The AUC was 0.78+0.035 in Model 1, and 0.81+0.031 in Model 2. Therefore, the addition of ECOG-PS data led to the improvement of the diagnostic performances (z=2.41, P-value <0.05).

CONCLUSIONS

Computed scan still represents a valid tool to predict ovarian cancer optimal cytoreduction; the predictive ability of a CT scan-based model is improved by integrating ECOG-PS data.