A nomogram of preoperative predictors for occult metastasis in patients with PDAC during laparoscopic exploration

Use of Diagnostic Laparoscopy and Peritoneal Washings for Pancreatic Cancer

Pancreatic adenocarcinoma is an aggressive malignancy that often presents with advanced disease. Accurate staging is essential for treatment planning and shared decision-making with patients. Staging laparoscopy is a minimally invasive procedure that can detect radiographically occult metastatic disease. Its routine use with the collection of peritoneal washings in patients with pancreatic cancer remains controversial. We, herein, review the current literature concerning staging laparoscopy and peritoneal washings in patients with pancreatic cancer.

Radiologic Occult Metastases in Pancreatic Cancer: Analysis of Risk Factors and Survival Outcomes in the Age of Contemporary Neoadjuvant Multi-agent Chemotherapy

BACKGROUND

Radiologic occult metastatic disease (ROMD) in patients with pancreatic ductal adenocarcinoma (PDAC) who undergo contemporary neoadjuvant chemotherapy (NAC) has not been well studied. This study sought to analyze the incidence, risk factors, and oncologic outcomes for patients who underwent the NAC approach for PDAC.

METHODS

A retrospective review analyzed a prospectively maintained database of patients who had potentially resectable PDAC treated with NAC and were offered pancreatectomy at our institution from 2011 to 2022. Multivariable regression analysis was performed to assess risk factors associated with ROMD. Kaplan-Meier curves with log-rank analyses were generated to estimate time-to-event end points.

RESULTS

The study enrolled 366 patients. Upfront and borderline resectable anatomic staging comprised 80% of the cohort, whereas 20% had locally advanced disease. The most common NAC regimen was FOLFIRINOX (n = 274, 75%). For 55 patients (15%) who harbored ROMD, the most common site was liver-only metastases (n = 33, 60%). The independent risk factors for ROMD were increasing CA19-9 levels during NAC (odds ratio [OR], 7.01; confidence interval [CI], 1.97-24.96; p = 0.008), indeterminate liver lesions (OR, 2.19; CI, 1.09-4.39; p = 0.028), and enlarged para-aortic lymph nodes (OR, 6.87; CI, 2.07-22.74; p = 0.002) on preoperative cross-sectional imaging. Receipt of palliative chemotherapy (p < 0.001) and eventual formal pancreatectomy (p = 0.04) were associated with survival benefit in the log-rank analysis. The median overall survival (OS) of the patients with ROMD was nearly 15 months from the initial diagnosis, with radiologic evidence of metastases occurring after a median of 2 months.

CONCLUSIONS

Radiologic occult metastatic disease remains a clinical challenge associated with poor outcomes for patients who have PDAC treated with multi-agent NAC.

Development and validation of a deep learning radiomics model with clinical-radiological characteristics for the identification of occult peritoneal metastases in patients with pancreatic ductal adenocarcinoma

BACKGROUND

Occult peritoneal metastases (OPM) in patients with pancreatic ductal adenocarcinoma (PDAC) are frequently overlooked during imaging. The authors aimed to develop and validate a computed tomography (CT)-based deep learning-based radiomics (DLR) model to identify OPM in PDAC before treatment.

METHODS

This retrospective, bicentric study included 302 patients with PDAC (training: n =167, OPM-positive, n =22; internal test: n =72, OPM-positive, n =9: external test, n =63, OPM-positive, n =9) who had undergone baseline CT examinations between January 2012 and October 2022. Handcrafted radiomics (HCR) and DLR features of the tumor and HCR features of peritoneum were extracted from CT images. Mutual information and least absolute shrinkage and selection operator algorithms were used for feature selection. A combined model, which incorporated the selected clinical-radiological, HCR, and DLR features, was developed using a logistic regression classifier using data from the training cohort and validated in the test cohorts.

RESULTS

Three clinical-radiological characteristics (carcinoembryonic antigen 19-9 and CT-based T and N stages), nine HCR features of the tumor, 14 DLR features of the tumor, and three HCR features of the peritoneum were retained after feature selection. The combined model yielded satisfactory predictive performance, with an area under the curve (AUC) of 0.853 (95% CI: 0.790-0.903), 0.845 (95% CI: 0.740-0.919), and 0.852 (95% CI: 0.740-0.929) in the training, internal test, and external test cohorts, respectively (all P <0.05). The combined model showed better discrimination than the clinical-radiological model in the training (AUC=0.853 vs. 0.612, P <0.001) and the total test (AUC=0.842 vs. 0.638, P <0.05) cohorts. The decision curves revealed that the combined model had greater clinical applicability than the clinical-radiological model.

CONCLUSIONS

The model combining CT-based DLR and clinical-radiological features showed satisfactory performance for predicting OPM in patients with PDAC.

Rim Enhancement and Peripancreatic Fat Stranding in Preoperative MDCT as Predictors for Occult Metastasis in PDAC Patients

RATIONALE AND OBJECTIVE

To identify the radiological features and clinical biomarkers that could predict the occult metastasis (OM) of pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

This retrospective study included PDAC patients who were radiologically diagnosed resectable (R) or borderline resectable (BR) and underwent surgical exploration from January 2018 to December 2021. Depending on whether distant metastases were found during the exploration, patients were divided into OM and non-OM groups. Univariate and multivariable logistic regression analyses were performed to determine the radiological and clinical predictive factors for occult metastasis. Model performance was determined by discrimination and calibration.

RESULTS

A total of 502 patients (median age, 64 years; interquartile range, 57-70 years; 294 men) were enrolled, among which 68 (13.5%) patients were found with distant metastases, with 45 liver-only, 19 peritoneal-only, four patients had both liver and peritoneal metastases. Rim enhancement and peripancreatic fat stranding were more frequent in the OM group than in the non-OM group. Tumor size (p = 0.028), tumor resectability (p = 0.031), rim enhancement (p < 0.001), peripancreatic fat stranding (p < 0.001) and level of CA125 (p = 0.021) were independent predictors of occult metastasis according to the multivariable analyses, and the areas under the curve (AUCs) of these characteristics were 0.703, 0.594, 0.638, 0.655, 0.631, respectively. The combined model showed the highest AUC of 0.823.

CONCLUSIONS

Rim enhancement, peripancreatic fat stranding, tumor size, tumor resectability and level of CA125 are risk factors for OM of PDAC. The combined model of radiological and clinical features may help the preoperative prediction of OM in PDAC.

Prediction of synchronous distant metastasis of primary pancreatic ductal adenocarcinoma using the radiomics features derived from 18F-FDG PET and MRI

AIM

To explore the potential of the joint radiomics analysis of positron-emission tomography (PET) and magnetic resonance imaging (MRI) of primary tumours for predicting the risk of synchronous distant metastasis (SDM) in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

18F-FDG PET and MRI images of PDAC patients from January 2011 to December 2020 were collected retrospectively. Patients (n=66) who received 18F-FDG PET/CT and MRI were included in a development group. Patients (n=25) scanned with hybrid PET/MRI were incorporated in an external test group. A radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm to select PET-MRI radiomics features of primary PDAC tumours. A radiomics nomogram was developed by combining the radiomics signature and important clinical indicators using univariate and multivariate analysis to assess patients' metastasis risk. The nomogram was verified with the employment of an external test group.

RESULTS

Regarding the development cohort, the radiomics nomogram was found to be better for predicting the risk of distant metastasis (area under the curve [AUC]: 0.93, sensitivity: 87%, specificity: 85%) than the clinical model (AUC: 0.70, p<0.001; sensitivity:70%, specificity: 65%) and the radiomics signature (AUC: 0.89, p>0.05; sensitivity: 65%, specificity:100%). Concerning the external test cohort, the radiomics nomogram yielded an AUC of 0.85.

CONCLUSION

PET-MRI based radiomics analysis exhibited effective prediction of the risk of SDM for preoperative PDAC patients and may offer complementary information and provide hints for cancer staging.

Prognosis of Idiopathic Sudden Sensorineural Hearing Loss: The Nomogram Perspective

OBJECTIVE

The aim of this study is to create a nomogram for accurately predicting the prognosis of idiopathic sudden sensorineural hearing loss (ISSNHL) and provide a reference for clinical treatment.

METHODS

Three hundred and twenty-three patients with ISSNHL were admitted from September 2014 to November 2020. The clinical data were retrospectively reviewed. Prognostic factors for ISSNHL were assessed based on univariate and multivariate logistic regression analysis and used to create a nomogram. Nomogram performance in terms of predictive and discriminatory ability was evaluated by calculating the concordance index (C-index) and generating calibration plots.

RESULTS

The overall hearing improvement rate was 41.4%, comprising complete recovery (13.3%), marked recovery (17.0%), and slight recovery (11.1%). Multivariate logistic regression analysis showed that age, symptoms of vertigo, interval between onset and treatment, low-density lipoprotein, and type of hearing loss were independent predictors of ISSNHL. A nomogram based on these 5 factors had a C index of 0.798 (95% confidence interval 0.750-0.845).

CONCLUSIONS

Age, vertigo, interval between onset and treatment, low-density lipoprotein level, and type of hearing loss are closely associated with hearing recovery. The nomogram may enable prediction of the prognosis of ISSNHL and facilitate clinical decision-making.