Preoperative PET/CT score can predict complete resection in advanced epithelial ovarian cancer: a prospective study

A modified diffusion-weighted magnetic resonance imaging-based model from the radiologist's perspective: improved performance in determining the surgical resectability of advanced high-grade serous ovarian cancer

BACKGROUND

Complete resection of all visible lesions during primary debulking surgery is associated with the most favorable prognosis in patients with advanced high-grade serous ovarian cancer. An accurate preoperative assessment of resectability is pivotal for tailored management.

OBJECTIVE

This study aimed to assess the potential value of a modified model that integrates the original 8 radiologic criteria of the Memorial Sloan Kettering Cancer Center model with imaging features of the subcapsular or diaphragm and mesenteric lesions depicted on diffusion-weighted magnetic resonance imaging and growth patterns of all lesions for predicting the resectability of advanced high-grade serous ovarian cancer.

STUDY DESIGN

This study included 184 patients with high-grade serous ovarian cancer who underwent preoperative diffusion-weighted magnetic resonance imaging between December 2018 and May 2023 at 2 medical centers. The patient cohort was divided into 3 subsets, namely a study cohort (n=100), an internal validation cohort (n=46), and an external validation cohort (n=38). Preoperative radiologic evaluations were independently conducted by 2 radiologists using both the Memorial Sloan Kettering Cancer Center model and the modified diffusion-weighted magnetic resonance imaging-based model. The morphologic characteristics of the ovarian tumors depicted on magnetic resonance imaging were assessed as either mass-like or infiltrative, and transcriptomic analysis of the primary tumor samples was performed. Univariate and multivariate statistical analyses were performed.

RESULTS

In the study cohort, both the scores derived using the Memorial Sloan Kettering Cancer Center (intraclass correlation coefficients of 0.980 and 0.959, respectively; both P<.001) and modified diffusion-weighted magnetic resonance imaging-based models (intraclass correlation coefficients of 0.962 and 0.940, respectively; both P<.001) demonstrated excellent intra- and interobserver agreement. The Memorial Sloan Kettering Cancer Center model (odds ratio, 1.825; 95% confidence interval, 1.390-2.395; P<.001) and the modified diffusion-weighted magnetic resonance imaging-based model (odds ratio, 1.776; 95% confidence interval, 1.410-2.238; P<.001) independently predicted surgical resectability. The modified diffusion-weighted magnetic resonance imaging-based model demonstrated improved predictive performance with an area under the curve of 0.867 in the study cohort and 0.806 and 0.913 in the internal and external validation cohorts, respectively. Using the modified diffusion-weighted magnetic resonance imaging-based model, patients with scores of 0 to 2, 3 to 4, 5 to 6, 7 to 10, and ≥11 achieved complete tumor debulking rates of 90.3%, 66.7%, 53.3%, 11.8%, and 0%, respectively. Most patients with incomplete tumor debulking had infiltrative tumors, and both the Memorial Sloan Kettering Cancer Center and the modified diffusion-weighted magnetic resonance imaging-based models yielded higher scores. The molecular differences between the 2 morphologic subtypes were identified.

CONCLUSION

When compared with the Memorial Sloan Kettering Cancer Center model, the modified diffusion-weighted magnetic resonance imaging-based model demonstrated enhanced accuracy in the preoperative prediction of resectability for advanced high-grade serous ovarian cancer. Patients with scores of 0 to 6 were eligible for primary debulking surgery.

Assessment of Intraoperative Scoring Systems for Predicting Cytoreduction Outcome in Peritoneal Metastatic Disease: A Systematic Review and Meta-analysis

BACKGROUND

Cytoreductive surgery (CRS) is a widely acknowledged treatment approach for peritoneal metastasis, showing favorable prognosis and long-term survival. Intraoperative scoring systems quantify tumoral burden before CRS and may predict complete cytoreduction (CC). This study reviews the intraoperative scoring systems for predicting CC and optimal cytoreduction (OC) and evaluates the predictive performance of the Peritoneal Cancer Index (PCI) and Predictive Index Value (PIV).

METHODS

Systematic searches were conducted in Embase, MEDLINE, and Web of Science. Meta-analyses of extracted data were performed to compare the absolute predictive performances of PCI and PIV.

RESULTS

Thirty-eight studies (5834 patients) focusing on gynecological (n = 34; 89.5%), gastrointestinal (n = 2; 5.3%) malignancies, and on tumors of various origins (n = 2; 5.3%) were identified. Seventy-seven models assessing the predictive performance of scoring systems (54 for CC and 23 for OC) were identified with PCI (n = 39/77) and PIV (n = 16/77) being the most common. Twenty models (26.0%) reinterpreted previous scoring systems of which ten (13%) used a modified version of PIV (reclassification). Meta-analyses of models predicting CC based on PCI (n = 21) and PIV (n = 8) provided an AUC estimate of 0.83 (95% confidence interval [CI] 0.79-0.86; Q = 119.6, p = 0.0001; I2 = 74.1%) and 0.74 (95% CI 0.68-0.81; Q = 7.2, p = 0.41; I2 = 11.0%), respectively.

CONCLUSIONS

Peritoneal Cancer Index models demonstrate an excellent estimate of CC, while PIV shows an acceptable performance. There is a need for high-quality studies to address management differences, establish standardized cutoff values, and focus on non-gynecological malignancies.

Role of Positron Emission Tomography/Computed Tomography in Epithelial Ovarian Cancer

Ovarian cancer (OC) is the most lethal gynecological malignancy with majority of cases diagnosed in advanced stages and associated with high morbidity and mortality. Positron emission tomography/computed tomography (PET/CT) has emerged as an integral part of the management of several nongynecological cancers. We used PubMed search engine using MeSH words "ovarian cancer" and "PET/CT" and reviewed the current status of PET/CT in epithelial OC. Its application related to ovarian tumor including adnexal mass evaluation, baseline staging, as a triaging tool for upfront surgery or neoadjuvant chemotherapy, for response assessment and prognostication, and for relapse detection and treatment planning has been highlighted. we highlight the current guidelines and newer upcoming PET modalities and radiotracers.

Molecular and phenotypic characteristics influencing the degree of cytoreduction in high-grade serous ovarian carcinomas

BACKGROUND

High-grade serous ovarian carcinoma (HGSOC) is the deadliest ovarian cancer subtype, and survival relates to initial cytoreductive surgical treatment. The existing tools for surgical outcome prediction remain inadequate for anticipating the outcomes of the complex relationship between tumour biology, clinical phenotypes, co-morbidity and surgical skills. In this genotype-phenotype association study, we combine phenotypic markers with targeted DNA sequencing to discover novel biomarkers to guide the surgical management of primary HGSOC.

METHODS

Primary tumour tissue samples (n = 97) and matched blood from a phenotypically well-characterised treatment-naïve HGSOC patient cohort were analysed by targeted massive parallel DNA sequencing (next generation sequencing [NGS]) of a panel of 360 cancer-related genes. Association analyses were performed on phenotypic traits related to complete cytoreductive surgery, while logistic regression analysis was applied for the predictive model.

RESULTS

The positive influence of complete cytoreductive surgery (R0) on overall survival was confirmed (p = 0.003). Before surgery, low volumes of ascitic fluid, lower CA125 levels, higher platelet counts and relatively lower clinical stage at diagnosis were all indicators, alone and combined, for complete cytoreduction (R0). Mutations in either the chromatin remodelling SWI_SNF (p = 0.036) pathway or the histone H3K4 methylation pathway (p = 0.034) correlated with R0. The R0 group also demonstrated higher tumour mutational burden levels (p = 0.028). A predictive model was developed by combining two phenotypes and the mutational status of five genes and one genetic pathway, enabling the prediction of surgical outcomes in 87.6% of the cases in this cohort.

CONCLUSION

Inclusion of molecular biomarkers adds value to the pre-operative stratification of HGSOC patients. A potential preoperative risk stratification model combining phenotypic traits and single-gene mutational status is suggested, but the set-up needs to be validated in larger cohorts.

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.

Prediction of Surgical Outcome in Advanced Ovarian Cancer by Imaging and Laparoscopy: A Narrative Review

Maximal-effort upfront or interval debulking surgery is the recommended approach for advanced-stage ovarian cancer. The role of diagnostic imaging is to provide a systematic and structured report on tumour dissemination with emphasis on key sites for resectability. Imaging methods, such as pelvic and abdominal ultrasound, contrast-enhanced computed tomography, whole-body diffusion-weighted magnetic resonance imaging and positron emission tomography, yield high diagnostic performance for diagnosing bulky disease, but they are less accurate for depicting small-volume carcinomatosis, which may lead to unnecessary explorative laparotomies. Diagnostic laparoscopy, on the other hand, may directly visualize intraperitoneal involvement but has limitations in detecting tumours beyond the gastrosplenic ligament, in the lesser sac, mesenteric root or in the retroperitoneum. Laparoscopy has its place in combination with imaging in cases where ima-ging results regarding resectability are unclear. Different imaging models predicting tumour resectability have been developed as an adjunctional objective tool. Incorporating results from tumour quantitative analyses (e.g., radiomics), preoperative biopsies and biomarkers into predictive models may allow for more precise selection of patients eligible for extensive surgery. This review will discuss the ability of imaging and laparoscopy to predict non-resectable disease in patients with advanced ovarian cancer.

18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) predictive score for complete resection in primary cytoreductive surgery

OBJECTIVE

To assess the value of preoperative 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) scan, combined with clinical variables, in predicting complete cytoreduction in selected patients with advanced ovarian cancer.

METHODS

We carried out a multicenter, observational, retrospective study evaluating patients who underwent primary cytoreductive surgery for advanced ovarian cancer in two Spanish centers between January 2017 and January 2022. Inclusion criteria were histological confirmation of invasive epithelial ovarian carcinoma; preoperative International Federation of Gynecology and Obstetrics (FIGO) stage III or IV; upfront cytoreductive surgery; and 18F-FDG PET/CT performed 1 month prior to surgery. A modified 18F-FDG PET/CT peritoneal cancer index score was calculated for all patients. Clinical variables and preoperative 18F-FDG PET/CT findings were analyzed and a multivariate model was constructed. A predictive score based on the odds ratio of the variables was calculated to determine patient selection.

RESULTS

A total of 45 patients underwent primary cytoreductive surgery. Complete resection was achieved in 36 (80%) patients. On multivariate analysis, two clinical variables (age ≥58 years and American Society of Anesthesiology score ≥3) and two preoperative 18F-FDG PET/CT scan findings (presence of extra-abdominal lymph node involvement and modified peritoneal cancer index value of 6 or more) were associated with gross residual disease. For this multivariate model predictive of non-complete cytoreduction, the area under the curve was 0.881. A predictive value of ≥5 was the most predictive cut-off for gross residual disease. Complete resection rate was 91.7% in patients with a score of ≤4 and 33.3% in patients with a score of ≥5 points on the predictive score.

CONCLUSIONS

In selected patients, a predictive score value ≥5 may be consider as a cut-off point for triaging patients to diagnostic laparoscopy before the primary surgery or neoadjuvant chemotherapy.

Preoperative PET/CT score can predict incomplete resection after debulking surgery for advanced serous ovarian cancer better than CT score, MTV, tumor markers and hematological markers

INTRODUCTION

Complete resection after debulking surgery is strongly associated with prolonged survival for advanced serous ovarian cancer (ASOC). Though positron emission tomography/computed tomography (PET/CT) is more advantageous than computed tomorgraphy (CT) for detecting metastases, studies on the PET/CT prediction model for incomplete resection for ovarian cancer are insufficient. We analyzed and compared the predictive value of preoperative PET/CT score, CT score, metabolic parameters, tumor markers and hematological markers for incomplete resection after debulking surgery for ASOC.

MATERIAL AND METHODS

A total of 62 ASOC patients who underwent preoperative [¹⁸ F]FDG PET/CT and debulking surgery were retrospectively analyzed. PET/CT and CT scores were based on the Suidan model. The predictive value of PET/CT score, CT score, the maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), human epididymis protein 4 (HE4), cancer antigen 125 (CA125), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) for incomplete resection were analyzed and compared.

RESULTS

Preoperative PET/CT score had the highest predictive value for incomplete resection in primary debulking surgery group (sensitivity: 65.0%, specificity: 88.9%, area under the ROC curve (AUC): 0.847, p < 0.001), however, in secondary debulking surgery group, preoperative PET/CT score and CT score had the same and highest predictive value for incomplete resection (sensitivity: 80.0%, specificity: 94.7%, AUC: 0.853, p = 0.017), compared with preoperative metabolic parameters SUVmax and MTV, tumor markers HE4 and CA125, and hematological markers LMR, PLR and NLR. Preoperative PET/CT score ≥ 3 (Suidan model) and preoperative PET/CT score ≥ 2 predicted a high risk of incomplete resection after primary and secondary debulking surgeries, respectively. There was no statistical difference between primary and secondary debulking surgery groups in predictive value of PET/CT score for incomplete resection (p = 0.971). There were significant differences between PET/CT scores and CT scores in primary debulking surgery group and no significant differences in secondary debulking surgery group.

CONCLUSIONS

A high PET/CT score predicted a high risk of incomplete resection. The preoperative PET/CT score had an identical predictive value in primary and secondary debulking surgery groups. PET/CT score was more accurate in the detection of metastases than CT score was.

Diagnostic accuracy of 18F-FDG PET/CT scan for peritoneal metastases in advanced ovarian cancer

Background

To assess the diagnostic accuracy of ¹⁸F-FDG PET/CT to determine the Eisenkop score and peritoneal cancer index (PCI) in correlation with surgical findings.

Methods

Forty-three patients underwent preoperative ¹⁸F-FDG PET/CT scan, followed by primary cytoreductive surgery for advanced ovarian cancer between September 2015 and February 2018. Clinical data were prospectively collected, including intraoperative assessment (with Eisenkop and PCI scores) and surgical results. The sensitivity, specificity, and accuracy were calculated at each anatomical site. The Eisenkop score, PCI score, and tumor volume of PET/CT scans were compared with surgical findings.

Results

A total of 32 (74.4%) patients were diagnosed with stage III, and 11 (25.6%) patients were stage IV. Among these individuals, 19 (44.2%) patients had no residual disease after primary surgery. The median [range] Eisenkop score on PET/CT scans and surgical findings were 5 [1-13] and 6 [2-13], respectively. PET/CT scans correctly predicted the Eisenkop score with high sensitivity (84.2%), specificity (87.0%), and accuracy (85.1%). The diagnostic accuracy of PET/CT scans for PCI scores was lower (78.5%), with 72.7% sensitivity and 84.9% specificity. Preoperative PET/CT scans might underestimate tumor volume compared with surgical findings.

Conclusions

¹⁸F-FDG PET/CT scans accurately predicted peritoneal metastases in advanced ovarian cancer before surgery using Eisenkop score.

An improved patch-based regularization method for PET image reconstruction

Background

Statistical reconstruction methods based on penalized maximum likelihood (PML) are being increasingly used in positron emission tomography (PET) imaging to reduce noise and improve image quality. Wang and Qi proposed a patch-based edge-preserving penalties algorithm that can be implemented in three simple steps: a maximum-likelihood expectation-maximization (MLEM) image update, an image smoothing step, and a pixel-by-pixel image fusion step. The pixel-by-pixel image fusion step, which fuses the MLEM updated image and the smoothed image, involves a trade-off between preserving the fine structural features of an image and suppressing noise. Particularly when reconstructing images from low-count data, this step cannot preserve fine structural features in detail. To better preserve these features and accelerate the algorithm convergence, we proposed to improve the patch-based regularization reconstruction method.

Methods

Our improved method involved adding a total variation (TV) regularization step following the MLEM image update in the patch-based algorithm. A feature refinement (FR) step was then used to extract the lost fine structural features from the residual image between the TV regularized image and the fused image based on patch regularization. These structural features would then be added back to the fused image. With the addition of these steps, each iteration of the image should gain more structural information. A brain phantom simulation experiment and a mouse study were conducted to evaluate our proposed improved method. Brain phantom simulation with added noise were used to determine the feasibility of the proposed algorithm and its acceleration of convergence. Data obtained from the mouse study were divided into event count sets to validate the performance of the proposed algorithm when reconstructing images from low-count data. Five criteria were used for quantitative evaluation: signal-to-noise ratio (SNR), covariance (COV), contrast recovery coefficient (CRC), regional relative bias, and relative variance.

Results

The bias and variance of the phantom brain image reconstructed using the patch-based method were 0.421 and 5.035, respectively, and this process took 83.637 seconds. The bias and variance of the image reconstructed by the proposed improved method, however, were 0.396 and 4.568, respectively, and this process took 41.851 seconds. This demonstrates that the proposed algorithm accelerated the reconstruction convergence. The CRC of the phantom brain image reconstructed using the patch-based method was iterated 20 times and reached 0.284, compared with the proposed method, which reached 0.446. When using a count of 5,000 K data obtained from the mouse study, both the patch-based method and the proposed method reconstructed images similar to the ground truth image. The intensity of the ground truth image was 88.3, and it was located in the 102^nd row and the 116^th column. However, when the count was reduced to below 40 K, and the patch-based method was used, image quality was significantly reduced. This effect was not observed when the proposed method was used. When a count of 40 K was used, the image intensity was 58.79 when iterated 100 times by the patch-based method, and it was located in the 102^nd row and the 116^th column, while the intensity when iterated 50 times by the proposed method was 63.83. This suggests that the proposed method improves image reconstruction from low-count data.

Conclusions

This improved method of PET image reconstruction could potentially improve the quality of PET images faster than other methods and also produce better reconstructions from low-count data.