Estimating Recurrence after Upfront Surgery in Patients with Resectable Pancreatic Ductal Adenocarcinoma by Using Pancreatic CT: Development and Validation of a Risk Score

Case Study on Analysing the Early Disease Detection of Pancreatic Ductal Adenocarcinoma in Korean Association for Clinical Oncology

OBJECTIVES

Pancreatic ductal adenocarcinoma (PDAC) is the most pervasive sort of pancreatic malignant growth. Due to the lack of early symptoms and effective methods for early detection and screening, the majority of patients (80% to 85%) are diagnosed with advanced metastatic or locally advanced disease, resulting in a low 5-year survival rate of 12%. The case study represents a comprehensive investigation into the intricate landscape of pancreatic cancer diagnosis within the Korean population.

METHODS

Grounded in epidemiological bits of knowledge, the review plans to disentangle the particular examples, commonness, and segment attributes of PDAC in Korea. By scrutinizing current diagnostic modalities, including conventional imaging techniques, molecular markers, and emerging technologies, the research seeks to evaluate the strengths and limitations of existing approaches within the Korean clinical context. Central to the study is an exploration of the collaborative initiatives spearheaded by the Association of Clinical Oncology in Korea in the domain of PDAC early detection. Analysing research projects, clinical trials, and interdisciplinary collaborations, the case study sheds light on the association's pivotal role in driving innovation and progress in oncology.

RESULTS

The goal is to offer a detailed analysis of how the association helps in furthering knowledge and enhancing results in the management of PDAC. The case study delves into the implications of early PDAC detection for patient outcomes, emphasizing the significance of timely interventions and tailored treatment strategies. By outlining the potential benefits and challenges associated with early diagnosis, the study aims to inform health care policies, shape clinical guidelines, and guide future research priorities.

CONCLUSION

Through a holistic approach, the case study endeavours to offer important experiences into the multifaceted landscape of PDAC early detection within the Korean health care system, contributing to the broader discourse on effective oncological practices and patient care.

Prognostic implication of extra-pancreatic organ invasion in resectable pancreas ductal adenocarcinoma in the pancreas tail

OBJECTIVES

To assess the prognostic significance of extra-pancreatic organ invasion in patients with resectable pancreatic ductal adenocarcinoma (PDAC) in the pancreas tail.

MATERIALS & METHODS

This retrospective study included patients with resectable PDAC in the pancreas tail who received upfront surgery between 2014 and 2020 at a tertiary institution. Preoperative pancreas protocol computed tomography (CT) scans evaluated tumor size, peripancreatic tumor infiltration, suspicious metastatic lymph nodes, and extra-pancreatic organ invasion. The influence of extra-pancreatic organ invasion, detected by CT or postoperative pathology, on pathologic resection margin status was evaluated using logistic regression. The impact on recurrence-free survival (RFS) was analyzed using multivariable Cox proportional hazard models (clinical-CT and clinical-pathologic).

RESULTS

The study included 158 patients (mean age, 65 years ± 8.8 standard deviation; 93 men). Extra-pancreatic organ invasion identified by either CT (p = 0.92) or pathology (p = 0.99) was not associated with a positive resection margin. Neither CT (p = 0.42) nor pathological (p = 0.64) extra-pancreatic organ invasion independently correlated with RFS. Independent predictors for RFS included suspicious metastatic lymph node (hazard ratio [HR], 2.05; 95 % confidence interval [CI], 1.08-3.9; p = 0.03) on CT in the clinical-CT model, pathological T stage (HR, 2.97; 95 % confidence interval [CI], 1.39-6.35; p = 0.005 for T2 and HR, 3.78; 95 % CI, 1.64-8.76; p = 0.002 for T3) and adjuvant therapy (HR, 0.62; 95 % confidence interval [CI], 0.42-0.92; p = 0.02) in the clinical-pathologic model.

CONCLUSION

Extra-pancreatic organ invasion does not independently influence pathologic resection margin status and RFS in patients with resectable PDAC in the pancreas tail after curative-intent resection; therefore, it should not be considered a high-risk factor.

Predicting Recurrence-Free Survival After Upfront Surgery in Resectable Pancreatic Ductal Adenocarcinoma: A Preoperative Risk Score Based on CA 19-9, CT, and 18F-FDG PET/CT

OBJECTIVE

To develop and validate a preoperative risk score incorporating carbohydrate antigen (CA) 19-9, CT, and fluorine-18-fluorodeoxyglucose (18F-FDG) PET/CT variables to predict recurrence-free survival (RFS) after upfront surgery in patients with resectable pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

Patients with resectable PDAC who underwent upfront surgery between 2014 and 2017 (development set) or between 2018 and 2019 (test set) were retrospectively evaluated. In the development set, a risk-scoring system was developed using the multivariable Cox proportional hazards model, including variables associated with RFS. In the test set, the performance of the risk score was evaluated using the Harrell C-index and compared with that of the postoperative pathological tumor stage.

RESULTS

A total of 529 patients, including 335 (198 male; mean age ± standard deviation, 64 ± 9 years) and 194 (103 male; mean age, 66 ± 9 years) patients in the development and test sets, respectively, were evaluated. The risk score included five variables predicting RFS: tumor size (hazard ratio [HR], 1.29 per 1 cm increment; P < 0.001), maximal standardized uptake values of tumor ≥ 5.2 (HR, 1.29; P = 0.06), suspicious regional lymph nodes (HR, 1.43; P = 0.02), possible distant metastasis on 18F-FDG PET/CT (HR, 2.32; P = 0.03), and CA 19-9 (HR, 1.02 per 100 U/mL increment; P = 0.002). In the test set, the risk score showed good performance in predicting RFS (C-index, 0.61), similar to that of the pathologic tumor stage (C-index, 0.64; P = 0.17).

CONCLUSION

The proposed risk score based on preoperative CA 19-9, CT, and 18F-FDG PET/CT variables may have clinical utility in selecting high-risk patients with resectable PDAC.

Contrast-Enhanced CT-Based Deep Learning Radiomics Nomogram for the Survival Prediction in Gallbladder Cancer

RATIONALE AND OBJECTIVES

An accurate prognostic model is essential for the development of treatment strategies for gallbladder cancer (GBC). This study proposes an integrated model using clinical features, radiomics, and deep learning based on contrast-enhanced computed tomography (CT) images for survival prediction in patients with GBC after surgical resection.

METHODS

A total of 167 patients with GBC who underwent surgical resection at two medical institutions were retrospectively enrolled. After obtaining the pre-treatment CT images, the tumor lesions were manually segmented, and handcrafted radiomics features were extracted. A clinical prognostic signature and radiomics signature were built using machine learning algorithms based on the optimal clinical features or handcrafted radiomics features, respectively. Subsequently, a DenseNet121 model was employed for transfer learning on the radiomics image data and as the basis for the deep learning signature. Finally, we used logistic regression on the three signatures to obtain the unified multimodal model for comprehensive interpretation and analysis.

RESULTS

The integrated model performed better than the other models, exhibiting the highest area under the curve (AUC) of 0.870 in the test set, and the highest concordance index (C-index) of 0.736 in predicting patient survival rates. A Kaplan-Meier analysis demonstrated that patients in high-risk group had a lower survival probability compared to those in low-risk group (log-rank p < 0.05).

CONCLUSION

The nomogram is useful for predicting the survival of patients with GBC after surgical resection, helping in the identification of high-risk patients with poor prognosis and ultimately facilitating individualized management of patients with GBC.

Extracellular volume-based scoring system for tracking tumor progression in pancreatic cancer patients receiving intraoperative radiotherapy

OBJECTIVES

To investigate the value of extracellular volume (ECV) derived from portal-venous phase (PVP) in predicting prognosis in locally advanced pancreatic cancer (LAPC) patients receiving intraoperative radiotherapy (IORT) with initial stable disease (SD) and to construct a risk-scoring system based on ECV and clinical-radiological features.

MATERIALS AND METHODS

One hundred and three patients with LAPC who received IORT demonstrating SD were enrolled and underwent multiphasic contrast-enhanced CT (CECT) before and after IORT. ECV maps were generated from unenhanced and PVP CT images. Clinical and CT imaging features were analyzed. The independent predictors of progression-free survival (PFS) determined by multivariate Cox regression model were used to construct the risk-scoring system. Time-dependent receiver operating characteristic (ROC) curve analysis and the Kaplan-Meier method were used to evaluate the predictive performance of the scoring system.

RESULTS

Multivariable analysis revealed that ECV, rim-enhancement, peripancreatic fat infiltration, and carbohydrate antigen 19-9 (CA19-9) response were significant predictors of PFS (all p < 0.05). Time-dependent ROC of the risk-scoring system showed a satisfactory predictive performance for disease progression with area under the curve (AUC) all above 0.70. High-risk patients (risk score ≥ 2) progress significantly faster than low-risk patients (risk score < 2) (p < 0.001).

CONCLUSION

ECV derived from PVP of conventional CECT was an independent predictor for progression in LAPC patients assessed as SD after IORT. The scoring system integrating ECV, radiological features, and CA19-9 response can be used as a practical tool for stratifying prognosis in these patients, assisting clinicians in developing an appropriate treatment approach.

CRITICAL RELEVANCE STATEMENT

The scoring system integrating ECV fraction, radiological features, and CA19-9 response can track tumor progression in patients with LAPC receiving IORT, aiding clinicians in choosing individual treatment strategies and improving their prognosis.

KEY POINTS

Predicting the progression of LAPC in patients receiving IORT is important. Our ECV-based scoring system can risk stratifying patients with initial SD. Appropriate prognostication can assist clinicians in developing appropriate treatment approaches.

Predicting postoperative prognosis of pancreatic cancer using a computed tomography-based radio-clinical model: exploring biologic functions

Computed tomography (CT) imaging has the potential to assist in predicting the prognosis and treatment strategies for pancreatic cancer (PC). This study aimed to develop and validate a radio-clinical model based on preoperative multiphase CT assessments to predict the overall survival (OS) of PC and identify differentially expressed genes associated with OS.

METHODS

Patients with PC who had undergone radical pancreatectomy (R0 resection) were divided into development and external validation sets. Independent predictors of OS were identified using Cox regression analyses and included in the nomogram, which was externally validated. The area under the curve was used to measure the model's accuracy in estimating OS probability. RNA sequencing data from The Cancer Genome Atlas were used for gene expression analysis.

RESULTS

In the development and external validation sets, survival was estimated respectively for 132 and 27 patients. Multivariate Cox regression analysis identified 5 independent OS predictors: age (P = .049), sex (P = .001), bilirubin level (P = .005), tumor size (P = .020), and venous invasion (P = .041). These variables were incorporated into the nomogram. Patients were divided into high- and low-risk groups for OS and survival curves showed that all patients in the low-risk group had better OS than that of those in the high-risk group (P < .001). Differentially expressed genes in patients with a poor prognosis were involved in neuroactive ligand-receptor interaction.

CONCLUSION

The radio-clinical model may be clinically useful for successfully predicting PC prognosis.

Pancreatic ductal adenocarcinoma: CEUS characteristics are correlated with pathological findings and help predict early recurrence after resection

OBJECTIVES

To identify characteristics of preoperative contrast-enhanced ultrasound (CEUS) that could predict early recurrence after curative resection of pancreatic ductal adenocarcinoma (PDAC).

METHODS

From January 2017 to September 2022, a total of 110 patients with PDAC (all confirmed by samples obtained via operation) who underwent CEUS within 1 month before surgery were enrolled. We proposed five CEUS enhancement patterns (Pattern I, homogeneous enhancement; Pattern II, heterogeneous enhancement without cystic components; pattern III, ring enhancement; Pattern IV, starry enhancement; Pattern V, heterogeneous enhancement with cystic components) of PDAC. Clinical-pathologic and CEUS characteristics for predicting early recurrence (recurrence within 1 year after curative resection) were analyzed. Important CEUS characteristics were compared with the pathological findings.

RESULTS

Tumor size and TNM stage were closely associated with early recurrence. Incomplete-enhancement and enhancement pattern III, IV and V at CEUS imaging were more prone to early recurrence. Incomplete-enhancement lesions had higher histological tumor grades, less frequent remaining acini, and more frequent necrosis within the tumor. PDACs with pattern I and II had lower histological tumor grades, and pattern III, IV and V had higher histological tumor grades. PDACs with pattern I, II and IV had less frequent intratumoral necrosis than PDACs with pattern III and V, and PDACs with pattern IV had lower MVD values.

CONCLUSIONS

PDACs with incomplete enhancement and enhancement pattern III, IV and V were more prone to early recurrence after attempted curative resection, and these important CEUS characteristics were closely related to the pathological findings of PDAC.

Inadequacy of the eighth edition of the American Joint Committee on Cancer pancreatic cancer staging system for invasive carcinoma associated with premalignant lesions in the pancreas: an analysis using the National Cancer Database

BACKGROUND

Invasive carcinomas arising from premalignant lesions are currently staged by the same criteria as conventional pancreatic ductal adenocarcinoma.

METHODS

Clinicopathologic information and survival data were extracted through a thorough search of histology codes from National Cancer Database (2006-2016). A total of 723 patients with invasive intraductal papillary mucinous neoplasm and mucinous cystic neoplasm were analyzed.

RESULTS

The median age was 67 years, and 351 patients (48.5%) were male. There were 212 (29.3%), 232 (32.1%), 272 (37.6%), and 7 (1.0%) patients with T1, T2, T3, and T4 classification. Extrapancreatic extension (EPE) was present in 284 (39.3%). Age (HR = 1.504, 95% CI 1.196-1.891), R1 or R2 resection (HR = 1.585, 95% CI 1.175-2.140), and EPE (HR = 1.598, 95% CI 1.209-2.113) were independent prognostic factors for overall survival. Size criteria did not significantly affect survival. The median survival was 115.9 months for patients without EPE, compared to 34.2 months for those with EPE. EPE discriminated survival better than tumor size.

DISCUSSION

The T classification of the eighth edition AJCC staging system is not adequate for invasive carcinomas associated with premalignant lesions of the pancreas. They merit a separate, dedicated staging system that uses appropriate prognostic factors.

Preoperative evaluating early recurrence in resectable pancreatic ductal adenocarcinoma by using CT radiomics

OBJECTIVE

To investigate the feasibility of a radiomics model based on contrast-enhanced CT for preoperatively predicting early recurrence after curative resection in patients with resectable pancreatic ductal adenocarcinoma (PDAC).

METHODS

One hundred and eighty-six patients with resectable PDAC who underwent curative resection were included and allocated to training set (131 patients) and validation set (55 patients). Radiomics features were extracted from arterial phase and portal venous phase images. The Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) regression were used for feature selection and radiomics signature construction. The radiomics model based on radiomics signature and clinical features was developed by the multivariate logistic regression analysis. Performance of the radiomics model was investigated by the area under the receiver operating characteristic (ROC) curve.

RESULTS

The radiomics signature, consisting of three arterial phase and three venous phase features, showed optimal prediction performance for early recurrence in both training (AUC = 0.73) and validation sets (AUC = 0.66). Multivariate logistic analysis identified the radiomics signature (OR, 2.58; 95% CI 2.36-3.17; p = 0.002) and clinical stage (OR, 1.60; 95% CI 1.15-2.30; p = 0.007) as independent predictors. The AUC values for risk evaluation of early recurrence using the radiomics model incorporating clinical stage were 0.80 (training set) and 0.75 (validation set).

CONCLUSION

The radiomics-based model integrating with clinical stage can predict early recurrence after upfront surgery in patients with resectable PDAC.

Preoperative prediction of early recurrence in resectable pancreatic cancer integrating clinical, radiologic, and CT radiomics features

OBJECTIVES

To use clinical, radiographic, and CT radiomics features to develop and validate a preoperative prediction model for the early recurrence of pancreatic cancer.

METHODS

We retrospectively analyzed 190 patients (150 and 40 in the development and test cohort from different centers) with pancreatic cancer who underwent pancreatectomy between January 2018 and June 2021. Radiomics, clinical-radiologic (CR), and clinical-radiologic-radiomics (CRR) models were developed for the prediction of recurrence within 12 months after surgery. Performance was evaluated using the area under the curve (AUC), Brier score, sensitivity, and specificity.

RESULTS

Early recurrence occurred in 36.7% and 42.5% of the development and test cohorts, respectively (P = 0.62). The features for the CR model included carbohydrate antigen 19-9 > 500 U/mL (odds ratio [OR], 3.60; P = 0.01), abutment to the portal and/or superior mesenteric vein (OR, 2.54; P = 0.054), and adjacent organ invasion (OR, 2.91; P = 0.03). The CRR model demonstrated significantly higher AUCs than the radiomics model in the internal (0.77 vs. 0.73; P = 0.048) and external (0.83 vs. 0.69; P = 0.038) validations. Although we found no significant difference between AUCs of the CR and CRR models (0.83 vs. 0.76; P = 0.17), CRR models showed more balanced sensitivity and specificity (0.65 and 0.87) than CR model (0.41 and 0.91) in the test cohort.

CONCLUSIONS

The CRR model outperformed the radiomics and CR models in predicting the early recurrence of pancreatic cancer, providing valuable information for risk stratification and treatment guidance.

Preoperative prediction of disease-free survival in pancreatic ductal adenocarcinoma patients after R0 resection using contrast-enhanced CT and CA19-9

OBJECTIVES

To investigate the efficiency of a combination of preoperative contrast-enhanced computed tomography (CECT) and carbohydrate antigen 19-9 (CA19-9) in predicting disease-free survival (DFS) after R0 resection of pancreatic ductal adenocarcinoma (PDAC).

METHODS

A total of 138 PDAC patients who underwent curative R0 resection were retrospectively enrolled and allocated chronologically to training (n = 91, January 2014-July 2019) and validation cohorts (n = 47, August 2019-December 2020). Using univariable and multivariable Cox regression analyses, we constructed a preoperative clinicoradiographic model based on the combination of CECT features and serum CA19-9 concentrations, and validated it in the validation cohort. The prognostic performance was evaluated and compared with that of postoperative clinicopathological and tumor-node-metastasis (TNM) models. Kaplan-Meier analysis was conducted to verify the preoperative prognostic stratification performance of the proposed model.

RESULTS

The preoperative clinicoradiographic model included five independent prognostic factors (tumor diameter on CECT > 4 cm, extrapancreatic organ infiltration, CECT-reported lymph node metastasis, peripheral enhancement, and preoperative CA19-9 levels > 180 U/mL). It better predicted DFS than did the postoperative clinicopathological (C-index, 0.802 vs. 0.787; p < 0.05) and TNM (C-index, 0.802 vs. 0.711; p < 0.001) models in the validation cohort. Low-risk patients had significantly better DFS than patients at the high-risk, defined by the model preoperatively (p < 0.001, training cohort; p < 0.01, validation cohort).

CONCLUSIONS

The clinicoradiographic model, integrating preoperative CECT features and serum CA19-9 levels, helped preoperatively predict postsurgical DFS for PDAC and could facilitate clinical decision-making.

CLINICAL RELEVANCE STATEMENT

We constructed a simple model integrating clinical and radiological features for the prediction of disease-free survival after curative R0 resection in patients with pancreatic ductal adenocarcinoma; this novel model may facilitate preoperative identification of patients at high risk of recurrence and metastasis that may benefit from neoadjuvant treatments.

KEY POINTS

• Existing clinicopathological predictors for prognosis in pancreatic ductal adenocarcinoma (PDAC) patients who underwent R0 resection can only be ascertained postoperatively and do not allow preoperative prediction. • We constructed a clinicoradiographic model, using preoperative contrast-enhanced computed tomography (CECT) features and preoperative carbohydrate antigen 19-9 (CA19-9) levels, and presented it as a nomogram. • The presented model can predict disease-free survival (DFS) in patients with PDAC better than can postoperative clinicopathological or tumor-node-metastasis (TNM) models.

Prediction of postoperative recurrence in resectable pancreatic body/tail adenocarcinoma: a novel risk stratification approach using a CT-based nomogram

OBJECTIVES

To identify prognostic CT features that predict recurrence in patients with resectable pancreatic body/tail adenocarcinoma (PBTA) and construct a CT-based nomogram for preoperative risk stratification.

METHODS

A total of 258 patients with resectable PBTA who underwent upfront surgery were retrospectively enrolled (development cohort, n = 172; validation cohort, n = 86), and their clinical and CT features were analyzed. Stepwise Cox proportional hazard analysis was performed to identify prognostic features and construct a predictive nomogram for recurrence-free survival (RFS). The prognostic performance of the CT-based nomogram was validated and compared to the 8th American Joint Committee on Cancer (AJCC) pathological staging system.

RESULTS

In the development cohort, the following five CT features for predicting recurrence were identified to construct the nomogram: tumor density in the venous phase, tumor necrosis, adjacent organ invasion, splenic vein invasion, and superior mesenteric vein/portal vein abutment. In the validation cohort, the CT-based nomogram showed a concordance index of 0.65 (95% confidence interval: 0.58-0.73), which was higher than the 8th AJCC staging system. The area under the curves of the nomogram for predicting recurrence at 0.5, 1, and 2 years were 0.66, 0.71, and 0.72, respectively. Patients were categorized into high- and low-risk groups with 1-year recurrence probabilities of 0.73 and 0.43, respectively.

CONCLUSIONS

The proposed nomogram provided accurate recurrence risk stratification for patients with resectable PBTA in a preoperative setting and may be used to facilitate clinical decision-making.

CLINICAL RELEVANCE STATEMENT

The proposed CT-based nomogram, based on easily available CT features, may serve as an effective and convenient tool for stratifying further the recurrence risk of patients with pancreatic body/tail adenocarcinoma.

KEY POINTS

• The CT-based nomogram, incorporating five commonly used CT features, successfully preoperatively stratified patients with resectable PBTA into distinct prognosis groups. • Tumor density in the venous phase, tumor necrosis, splenic vein invasion, adjacent organ invasion, and superior mesenteric vein/portal vein abutment were associated with RFS in patients with resectable PBTA. • The CT-based nomogram exhibited better predictive performance for recurrence than the 8th AJCC staging system.

Features of T1 pancreatic cancer and validation of the eighth edition AJCC staging system definition using a Korean-Japanese joint cohort and the SEER database

BACKGROUND/PURPOSE

Little is known about the features of T1 pancreatic ductal adenocarcinoma (PDAC) and its definition in the eighth edition of the American Joint Committee on Cancer (AJCC) staging system needs validation. The aims were to analyze the clinicopathologic features of T1 PDAC and investigate the validity of its definition.

METHOD

Data from 1506 patients with confirmed T1 PDAC between 2000 and 2019 were collected and analyzed. The results were validated using 3092 T1 PDAC patients from the Surveillance, Epidemiology, and End Results (SEER) database.

RESULTS

The median survival duration of patients was 50 months, and the 5-year survival rate was 45.1%. R0 resection was unachievable in 10.0% of patients, the nodal metastasis rate was 40.0%, and recurrence occurred in 55.2%. The current T1 subcategorization was not feasible for PDAC, tumors with extrapancreatic extension (72.8%) had worse outcomes than those without extrapancreatic extension (median survival 107 vs. 39 months, p < .001). Extrapancreatic extension was an independent prognostic factor whereas the current T1 subcategorization was not. The results of this study were reproducible with data from the SEER database.

CONCLUSION

Despite its small size, T1 PDAC displayed aggressive behavior warranting active local and systemic treatment. The subcategorization by the eighth edition of the AJCC staging system was not adequate for PDAC, and better subcategorization methods need to be explored. In addition, the role of extrapancreatic extension in the staging system should be reconsidered.

Development and Validation of a Nomogram for Preoperative Prediction of Early Recurrence after Upfront Surgery in Pancreatic Ductal Adenocarcinoma by Integrating Deep Learning and Radiological Variables

Around 80% of pancreatic ductal adenocarcinoma (PDAC) patients experience recurrence after curative resection. We aimed to develop a deep-learning model based on preoperative CT images to predict early recurrence (recurrence within 12 months) in PDAC patients. The retrospective study included 435 patients with PDAC from two independent centers. A modified 3D-ResNet18 network was used for a deep learning model construction. A nomogram was constructed by incorporating deep learning model outputs and independent preoperative radiological predictors. The deep learning model provided the area under the receiver operating curve (AUC) values of 0.836, 0.736, and 0.720 in the development, internal, and external validation datasets for early recurrence prediction, respectively. Multivariate logistic analysis revealed that higher deep learning model outputs (odds ratio [OR]: 1.675; 95% CI: 1.467, 1.950; p < 0.001), cN1/2 stage (OR: 1.964; 95% CI: 1.036, 3.774; p = 0.040), and arterial involvement (OR: 2.207; 95% CI: 1.043, 4.873; p = 0.043) were independent risk factors associated with early recurrence and were used to build an integrated nomogram. The nomogram yielded AUC values of 0.855, 0.752, and 0.741 in the development, internal, and external validation datasets. In conclusion, the proposed nomogram may help predict early recurrence in PDAC patients.

MRI-Based Tumor Necrosis Depiction in Pancreatic Ductal Adenocarcinoma: Can It Predict Tumor Aggressiveness?

The purpose of this study was to investigate whether tumor necrosis depicted on contrast-enhanced abdominal MRI can predict tumor aggressiveness in pancreatic ductal adenocarcinoma (PDAC). In this retrospective analysis, we included 71 patients with pathology-proven PDAC who underwent contrast-enhanced MRI from 2006 to 2020. Assessment for the presence/absence of imaging detected necrosis was performed on T2-weighted and contrast-enhanced T1-weighted images. Primary tumor characteristics, regional lymphadenopathy, metastases, stage, and overall survival were analyzed. Fisher's exact and Mann-Whitney U tests were used for statistical analysis. Of the 72 primary tumors, necrosis was identified on MRI in 58.3% (42/72). Necrotic PDACs were larger (44.6 vs. 34.5 mm, p = 0.0016), had higher rates of regional lymphadenopathy (69.0% vs. 26.7%, p = 0.0007), and more frequent metastases (78.6% vs. 40.0%, p = 0.0010) than those without MRI-evident necrosis. A non-statistically significant reduction in median overall survival was observed in patients with versus without MRI-evident necrosis (15.8 vs. 38.0 months, p = 0.23). PDAC tumor necrosis depicted on MRI was associated with larger tumors and higher frequency of regional lymphadenopathy and metastases.

Combined CT and serum CA19-9 for stratifying risk for progression in patients with locally advanced pancreatic cancer receiving intraoperative radiotherapy

Background and purpose

The aim of this study was to evaluate the significance of baseline computed tomography (CT) imaging features and carbohydrate antigen 19-9 (CA19-9) in predicting prognosis of locally advanced pancreatic cancer (LAPC) receiving intraoperative radiotherapy (IORT) and to establish a progression risk nomogram that helps to identify the potential beneficiary of IORT.

Methods

A total of 88 LAPC patients with IORT as their initial treatment were enrolled retrospectively. Clinical data and CT imaging features were analyzed. Cox regression analyses were performed to identify the independent risk factors for progression-free survival (PFS) and to establish a nomogram. A risk-score was calculated by the coefficients of the regression model to stratify the risk of progression.

Results

Multivariate analyses revealed that relative enhanced value in portal-venous phase (REV-PVP), peripancreatic fat infiltration, necrosis, and CA19-9 were significantly associated with PFS (all p < 0.05). The nomogram was constructed according to the above variables and showed a good performance in predicting the risk of progression with a concordance index (C-index) of 0.779. Our nomogram stratified patients with LAPC into low- and high-risk groups with distinct differences in progression after IORT (p < 0.001).

Conclusion

The integrated nomogram would help clinicians to identify appropriate patients who might benefit from IORT before treatment and to adapt an individualized treatment strategy.

Methods for Clinical Evaluation of Artificial Intelligence Algorithms for Medical Diagnosis

Adequate clinical evaluation of artificial intelligence (AI) algorithms before adoption in practice is critical. Clinical evaluation aims to confirm acceptable AI performance through adequate external testing and confirm the benefits of AI-assisted care compared with conventional care through appropriately designed and conducted studies, for which prospective studies are desirable. This article explains some of the fundamental methodological points that should be considered when designing and appraising the clinical evaluation of AI algorithms for medical diagnosis. The specific topics addressed include the following: (a) the importance of external testing of AI algorithms and strategies for conducting the external testing effectively, (b) the various metrics and graphical methods for evaluating the AI performance as well as essential methodological points to note in using and interpreting them, (c) paired study designs primarily for comparative performance evaluation of conventional and AI-assisted diagnoses, (d) parallel study designs primarily for evaluating the effect of AI intervention with an emphasis on randomized clinical trials, and (e) up-to-date guidelines for reporting clinical studies on AI, with an emphasis on guidelines registered in the EQUATOR Network library. Sound methodological knowledge of these topics will aid the design, execution, reporting, and appraisal of clinical evaluation of AI.

Prediction of the characteristics of aggressiveness of pancreatic neuroendocrine neoplasms (PanNENs) based on CT radiomic features

OBJECTIVES

To predict tumor grade (G1 vs. G2/3), presence of distant metastasis (M+), metastatic lymph nodes (N+), and microvascular invasion (VI) of pancreatic neuroendocrine neoplasms (PanNEN) based on preoperative CT radiomic features (RFs), by applying a machine learning approach aimed to limit overfit.

METHODS

This retrospective study included 101 patients who underwent surgery for PanNEN; the entire population was split into training (n = 70) and validation cohort (n = 31). Based on a previously validated methodology, after tumor segmentation on contrast-enhanced CT, RFs were extracted from unenhanced CT images. In addition, conventional radiological and clinical features were combined with RFs into multivariate logistic regression models using minimum redundancy and a bootstrap-based machine learning approach. For each endpoint, models were trained and validated including only RFs (RF_model), and both (radiomic and clinicoradiological) features (COMB_model).

RESULTS

Twenty-five patients had G2/G3 tumor, 37 N+, and 14 M+ and 38 were shown to have VI. From a total of 182 RFs initially extracted, few independent radiomic and clinicoradiological features were identified. For M+ and G, the resulting models showed moderate to high performances: areas under the curve (AUC) for training/validation cohorts were 0.85/0.77 (RF_model) and 0.81/0.81 (COMB_model) for M+ and 0.67/0.72 and 0.68/0.70 for G. Concerning N+ and VI, only the COMB_model could be built, with poorer performance for N+ (AUC = 0.72/0.61) compared to VI (0.82/0.75). For all endpoints, the negative predictive value was good (≥ 0.75).

CONCLUSIONS

Combining few radiomic and clinicoradiological features resulted in presurgical prediction of histological characteristics of PanNENs. Despite the limited risk of overfit, external validations are warranted.

KEY POINTS

• Histology is the only tool currently available allowing characterization of PanNEN biological characteristics important for prognostic assessment; significant limitations to this approach exist. • Based upon preoperative contrast-enhanced CT images, a machine learning approach optimized to favor models' generalizability was successfully applied to train predictive models for tumor grading (G1 vs. G2/3), microvascular invasion, metastatic lymph nodes, and distant metastatic spread. • Moderate to high discriminative models (AUC: 0.67-0.85) based on few parameters (≤ 3) showing high negative predictive value (0.75-0.98) were generated and then successfully validated.

Survival Prediction after Curative Resection of Pancreatic Ductal Adenocarcinoma by Imaging-Based Intratumoral Necrosis

We aimed to determine the histopathological characteristics and prognosis of curatively resected pancreatic ductal adenocarcinoma (PDAC) showing intratumoral necrosis on preoperative CT or MRI. This study consecutively included 102 patients who underwent upfront surgery with margin-negative resection from 2012 to 2020. All patients underwent both pancreatic CT and MRI within 1 month before surgery. Two radiologists independently assessed CT/MRI findings, including the presence of CT- and MRI-detected necrosis. Histopathological characteristics of PDACs according to CT or MRI detection of necrosis were evaluated. Disease-free survival (DFS) and overall survival (OS) were assessed by the Kaplan−Meier method and the Cox proportional hazards model. Among the 102 PDAC patients, 14 patients (13.7%) had CT-detected necrosis, and 16 patients (15.7%) had MRI-detected necrosis, of which 9 showed both CT- and MRI-detected necrosis. PDACs with CT- or MRI-detected necrosis demonstrated a significantly higher degree of histopathological necrosis than those without (p < 0.001). Multivariable analysis revealed that tumor size (hazard ratio [HR], 1.19; p = 0.040), tumor location (HR, 0.46; p = 0.009), and MRI-detected necrosis (HR, 2.64; p = 0.002) had independent associations with DFS. Only MRI-detected necrosis was significantly associated with OS (HR, 2.59; p = 0.004). Therefore, MRI-detected necrosis might be a potential imaging predictor of poor survival after curative resection of PDAC.

Prognostic Value of Sarcopenia and Myosteatosis in Patients with Resectable Pancreatic Ductal Adenocarcinoma

OBJECTIVE

The clinical relevance of myosteatosis has not been well evaluated in patients with pancreatic ductal adenocarcinoma (PDAC), although sarcopenia has been extensively researched. Therefore, we evaluated the prognostic value of muscle quality, including myosteatosis, in patients with resectable PDAC treated surgically.

MATERIALS AND METHODS

We retrospectively evaluated 347 patients with resectable PDAC who underwent curative surgery (mean age ± standard deviation, 63.6 ± 9.6 years; 202 male). Automatic muscle segmentation was performed on preoperative computed tomography (CT) images using an artificial intelligence program. A single axial image of the portal phase at the inferior endplate level of the L3 vertebra was used for analysis in each patient. Sarcopenia was evaluated using the skeletal muscle index, calculated as the skeletal muscle area (SMA) divided by the height squared. The mean SMA attenuation was used to evaluate myosteatosis. Diagnostic cutoff values for sarcopenia and myosteatosis were devised using the Contal and O'Quigley methods, and patients were classified according to normal (nMT), sarcopenic (sMT), myosteatotic (mMT), or combined (cMT) muscle quality types. Multivariable Cox regression analyses were conducted to assess the effects of muscle type on the overall survival (OS) and recurrence-free survival (RFS) after surgery.

RESULTS

Eighty-four (24.2%), 73 (21.0%), 75 (21.6%), and 115 (33.1%) patients were classified as having nMT, sMT, mMT, and cMT, respectively. Compared to nMT, mMT and cMT were significantly associated with poorer OS, with hazard ratios (HRs) of 1.49 (95% confidence interval, 1.00-2.22) and 1.68 (1.16-2.43), respectively, while sMT was not (HR of 1.40 [0.94-2.10]). Only mMT was significantly associated with poorer RFS, with an HR of 1.59 (1.07-2.35), while sMT and cMT were not.

CONCLUSION

Myosteatosis was associated with poor OS and RFS in patients with resectable PDAC who underwent curative surgery.

Tumor fibrosis correlates with the survival of patients with pancreatic adenocarcinoma and is predictable using clinicoradiological features

OBJECTIVES

To evaluate the prognostic value of fibrosis for patients with pancreatic adenocarcinoma (PDAC) and preoperatively predict fibrosis using clinicoradiological features. Tumor fibrosis plays an important role in the chemoresistance of PDAC. However, the prognostic value of tumor fibrosis remains contradiction and accurate prediction of tumor fibrosis is required.

METHODS

The study included 131 patients with PDAC who underwent first-line surgery. The prognostic value of fibrosis and rounded cutoff fibrosis points for median overall survival (OS) and disease-free survival (DFS) were determined using Cox regression and receiver operating characteristic (ROC) analyses. Then the whole cohort was randomly divided into training (n = 88) and validation (n = 43) sets. Binary logistic regression analysis was performed to select independent risk factors for fibrosis in the training set, and a nomogram was constructed. Nomogram performance was assessed using a calibration curve and decision curve analysis (DCA).

RESULTS

Hazard ratios of fibrosis for OS and DFS were 1.121 (95% confidence interval [CI]: 1.082-1.161) and 1.110 (95% CI: 1.067-1.155). ROC analysis identified 40% as the rounded cutoff fibrosis point for median OS and DFS. Tumor diameter, carbohydrate antigen 19-9 level, and peripancreatic tumor infiltration were independent risk factors; areas under the nomogram curve were 0.810 and 0.804 in the training and validation sets, respectively. The calibration curve indicated good agreement of the nomogram, and DCA demonstrated good clinical usefulness.

CONCLUSIONS

Tumor fibrosis was associated with poor OS and DFS in patients with PDAC. The nomogram incorporating clinicoradiological features was useful for preoperatively predicting tumor fibrosis.

KEY POINTS

• Tumor fibrosis is correlated with poor prognosis in patients with pancreatic adenocarcinoma. • Tumor fibrosis can be categorized according to its association with overall survival and disease-free survival. • A nomogram incorporating carbohydrate antigen 19-9 level, tumor diameter, and peripancreatic tumor infiltration is useful for preoperatively predicting tumor fibrosis.

Prediction of overall survival in patients with pancreatic ductal adenocarcinoma: histogram analysis of ADC value and correlation with pathological intratumoral necrosis

BACKGROUND

To evaluate the utility of histogram analysis (HA) of apparent diffusion coefficient (ADC) values to predict the overall survival (OS) in patients with pancreatic ductal adenocarcinoma (PDAC) and to correlate with pathologically evaluated massive intratumoral necrosis (MITN).

MATERIALS AND METHODS

Thirty-nine patients were included in this retrospective study with surgically resected PDAC who underwent preoperative magnetic resonance imaging. Twelve patients received neoadjuvant chemotherapy. HA on the ADC maps were performed to obtain the tumor HA parameters. Using Cox proportional regression analysis adjusted for age, time-dependent receiver-operating-characteristic (ROC) curve analysis, and Kaplan-Meier estimation, we evaluated the association between HA parameters and OS. The association between prognostic factors and pathologically confirmed MITN was assessed by logistic regression analysis.

RESULTS

The median OS was 19.9 months. The kurtosis (P < 0.001), entropy (P = 0.013), and energy (P = 0.04) were significantly associated with OS. The kurtosis had the highest area under the ROC curve (AUC) for predicting 3-year survival (AUC 0.824) among these three parameters. Between the kurtosis and MITN, the logistic regression model revealed a positive correlation (P = 0.045). Lower survival rates occurred in patients with high kurtosis (cutoff value > 2.45) than those with low kurtosis (≤ 2.45) (P < 0.001: 1-year survival rate, 75.2% versus 100%: 3-year survival rate, 14.7% versus 100%).

CONCLUSIONS

HA derived kurtosis obtained from tumor ADC maps might be a potential imaging biomarker for predicting the presence of MITN and OS in patients with PDAC.

Resectable and Borderline Resectable Pancreatic Ductal Adenocarcinoma: Role of the Radiologist and Oncologist in the Era of Precision Medicine

The incidence and mortality of pancreatic ductal adenocarcinoma are growing over time. The management of patients with pancreatic ductal adenocarcinoma involves a multidisciplinary team, ideally involving experts from surgery, diagnostic imaging, interventional endoscopy, medical oncology, radiation oncology, pathology, geriatric medicine, and palliative care. An adequate staging of pancreatic ductal adenocarcinoma and re-assessment of the tumor after neoadjuvant therapy allows the multidisciplinary team to choose the most appropriate treatment for the patient. This review article discusses advancement in the molecular basis of pancreatic ductal adenocarcinoma, diagnostic tools available for staging and tumor response assessment, and management of resectable or borderline resectable pancreatic cancer.

Diagnostic value of chest computed tomography imaging for COVID-19 based on reverse transcription-polymerase chain reaction: a meta-analysis

Background

The computed tomography (CT) diagnostic value of COVID-19 is controversial. We summarized the value of chest CT in the diagnosis of COVID-19 through a meta-analysis based on the reference standard.

Methods

All Chinese and English studies related to the diagnostic value of CT for COVID-19 across multiple publication platforms, was searched for and collected. Studies quality evaluation and plotting the risk of bias were estimated. A heterogeneity test and meta-analysis, including plotting sensitivity (Sen), specificity (Spe) forest plots, pooled positive likelihood ratio (+LR), negative likelihood ratio (-LR), dignostic odds ratio (DOR) values and 95% confidence interval (CI), were estimated. If there was a threshold effect, summary receiver operating characteristic curves (SROC) was further plotted. Pooled area under the receiver operating characteristic curve (AUROC) and 95% CI were also calculated.

Results

Twenty diagnostic studies that represented a total of 9004 patients were included from 20 pieces of literatures after assessing all the aggregated studies. The reason for heterogeneity was caused by the threshold effect, so the AUROC = 0.91 (95% CI: 0.89–0.94) for chest CT of COVID-19. Pooled sensitivity, specificity, +LR, -LR from 20 studies were 0.91 (95% CI: 0.88–0.94), 0.71 (95% CI: 0.59–0.80), 3.1(95% CI: 2.2–4.4), 0.12 (95% CI: 0.09–0.17), separately. The I² was 85.6% (P = 0.001) by Q-test.

Conclusions

The results of this study showed that CT diagnosis of COVID-19 was close to the reference standard. The diagnostic value of chest CT may be further enhanced if there is a unified COVID-19 diagnostic standard. However, please pay attention to rational use of CT.

Graphic Abstract

Identification of intratumoral fluid-containing area by magnetic resonance imaging to predict prognosis in patients with pancreatic ductal adenocarcinoma after curative resection

OBJECTIVES

To compare the prognosis of pancreatic ductal adenocarcinoma (PDAC) after curative resection according to the type of intratumoral fluid-containing area identified on MRI.

METHODS

This retrospective study included 112 consecutive patients who underwent upfront surgery with margin-negative resection between 2012 and 2019. All patients underwent MRI within 1 month before surgery. Three radiologists independently assessed the MRI findings, determined whether intratumoral fluid-containing areas were present, and classified all intratumoral fluid-containing areas by type (i.e., imaging necrosis or neoplastic mucin cysts). Recurrence-free survival (RFS) and overall survival (OS) were evaluated by the Kaplan-Meier method and the Cox proportional hazards model. Histopathological differences according to the type of intratumoral fluid-containing area were assessed.

RESULTS

Of the 112 PDAC patients, intratumoral fluid-containing areas were identified on MRI in 33 (29.5%), among which 18 were classified as imaging necrosis and 15 as neoplastic mucin cysts. PDAC patients with imaging necrosis demonstrated significantly shorter RFS (mean 6.1 months versus 47.3 months; p < .001) and OS (18.4 months versus 55.0 months, p = .001) than those with neoplastic mucin cysts. Multivariable analysis showed that only the type of intratumoral fluid-containing area was significantly associated with RFS (hazard ratio, 2.25 and 0.38; p = .009 and p = .046 for imaging necrosis and neoplastic mucin cysts, respectively). PDAC with imaging necrosis had more frequent histological necrosis, more aggressive tumor differentiation, and higher tumor cellularity than PDAC with neoplastic mucin cysts (p ≤ .02).

CONCLUSION

The detection and discrimination of intratumoral fluid-containing areas on preoperative MRI may be useful in predicting the prognosis of PDAC patients after curative resection.

KEY POINTS

• Pancreatic ductal adenocarcinoma (PDAC) patients with imaging necrosis demonstrated significantly shorter survival than those with neoplastic mucin cysts after curative resection. • Multivariable analysis showed that only the type of intratumoral fluid-containing area identified on MRI was significantly associated with recurrence-free survival. • PDAC with imaging necrosis had more frequent histological necrosis, more aggressive tumor differentiation, and higher tumor cellularity than PDAC with neoplastic mucin cysts.

Prediction of Early Distant Recurrence in Upfront Resectable Pancreatic Adenocarcinoma: A Multidisciplinary, Machine Learning-Based Approach

Simple Summary

If pancreatic adenocarcinoma is assessed to be technically resectable, curative surgery is still suggested as the primary treatment option; however, the recurrence rate can be very high even in this selected population. The aim of our retrospective study was to develop a preoperative model to accurately stratify upfront resectable patients according to the risk of early distant disease relapse after surgery (<12 months from index procedure). Through a machine learning-based approach, we identified one biochemical marker (serum level of CA19.9), one radiological finding (necrosis) and one radiomic feature (SurfAreaToVolumeRatio), all significantly associated with the early resurge of distant recurrence. A model composed of these three variables only allowed identification of those patients at high risk for early distant disease relapse (50% chance of developing metastases within 12 months after surgery), who would benefit from neoadjuvant chemotherapy instead of upfront surgery.

Abstract

Despite careful selection, the recurrence rate after upfront surgery for pancreatic adenocarcinoma can be very high. We aimed to construct and validate a model for the prediction of early distant recurrence (<12 months from index surgery) after upfront pancreaticoduodenectomy. After exclusions, 147 patients were retrospectively enrolled. Preoperative clinical and radiological (CT-based) data were systematically evaluated; moreover, 182 radiomics features (RFs) were extracted. Most significant RFs were selected using minimum redundancy, robustness against delineation uncertainty and an original machine learning bootstrap-based method. Patients were split into training (n = 94) and validation cohort (n = 53). Multivariable Cox regression analysis was first applied on the training cohort; the resulting prognostic index was then tested in the validation cohort. Clinical (serum level of CA19.9), radiological (necrosis), and radiomic (SurfAreaToVolumeRatio) features were significantly associated with the early resurge of distant recurrence. The model combining these three variables performed well in the training cohort (p = 0.0015, HR = 3.58, 95%CI = 1.98–6.71) and was then confirmed in the validation cohort (p = 0.0178, HR = 5.06, 95%CI = 1.75–14.58). The comparison of survival curves between low and high-risk patients showed a p-value <0.0001. Our model may help to better define resectability status, thus providing an actual aid for pancreatic adenocarcinoma patients’ management (upfront surgery vs. neoadjuvant chemotherapy). Independent validations are warranted.

Review of Statistical Methods for Evaluating the Performance of Survival or Other Time-to-Event Prediction Models (from Conventional to Deep Learning Approaches)

The recent introduction of various high-dimensional modeling methods, such as radiomics and deep learning, has created a much greater diversity in modeling approaches for survival prediction (or, more generally, time-to-event prediction). The newness of the recent modeling approaches and unfamiliarity with the model outputs may confuse some researchers and practitioners about the evaluation of the performance of such models. Methodological literacy to critically appraise the performance evaluation of the models and, ideally, the ability to conduct such an evaluation would be needed for those who want to develop models or apply them in practice. This article intends to provide intuitive, conceptual, and practical explanations of the statistical methods for evaluating the performance of survival prediction models with minimal usage of mathematical descriptions. It covers from conventional to deep learning methods, and emphasis has been placed on recent modeling approaches. This review article includes straightforward explanations of C indices (Harrell's C index, etc.), time-dependent receiver operating characteristic curve analysis, calibration plot, other methods for evaluating the calibration performance, and Brier score.

A Novel DNA Replication-Related Signature Predicting Recurrence After R0 Resection of Pancreatic Ductal Adenocarcinoma: Prognostic Value and Clinical Implications

The aim of any surgical resection for pancreatic ductal adenocarcinoma (PDAC) is to achieve tumor-free margins (R0). R0 margins give rise to better outcomes than do positive margins (R1). Nevertheless, postoperative morbidity after R0 resection remains high and prognostic gene signature predicting recurrence risk of patients in this subgroup is blank. Our study aimed to develop a DNA replication-related gene signature to stratify the R0-treated PDAC patients with various recurrence risks. We conducted Cox regression analysis and the LASSO algorithm on 273 DNA replication-related genes and eventually constructed a 7-gene signature. The predictive capability and clinical feasibility of this risk model were assessed in both training and external validation sets. Pathway enrichment analysis showed that the signature was closely related to cell cycle, DNA replication, and DNA repair. These findings may shed light on the identification of novel biomarkers and therapeutic targets for PDAC.