Pancreatic adenocarcinoma: quantitative CT features are correlated with fibrous stromal fraction and help predict outcome after resection

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.

Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma

OBJECTIVE

This study aims to compare the feasibility and effectiveness of automatic deep learning network and radiomics models in differentiating low tumor stroma ratio (TSR) from high TSR in pancreatic ductal adenocarcinoma (PDAC).

METHODS

A retrospective analysis was conducted on a total of 207 PDAC patients from three centers (training cohort: n = 160; test cohort: n = 47). TSR was assessed on hematoxylin and eosin-stained specimens by experienced pathologists and divided as low TSR and high TSR. Deep learning and radiomics models were developed including ShuffulNetV2, Xception, MobileNetV3, ResNet18, support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and logistic regression (LR). Additionally, the clinical models were constructed through univariate and multivariate logistic regression. Kaplan-Meier survival analysis and log-rank tests were conducted to compare the overall survival time between different TSR groups.

RESULTS

To differentiate low TSR from high TSR, the deep learning models based on ShuffulNetV2, Xception, MobileNetV3, and ResNet18 achieved AUCs of 0.846, 0.924, 0.930, and 0.941, respectively, outperforming the radiomics models based on SVM, KNN, RF, and LR with AUCs of 0.739, 0.717, 0.763, and 0.756, respectively. Resnet 18 achieved the best predictive performance. The clinical model based on T stage alone performed worse than deep learning models and radiomics models. The survival analysis based on 142 of the 207 patients demonstrated that patients with low TSR had longer overall survival.

CONCLUSIONS

Deep learning models demonstrate feasibility and superiority over radiomics in differentiating TSR in PDAC. The tumor stroma ratio in the PDAC microenvironment plays a significant role in determining prognosis.

CRITICAL RELEVANCE STATEMENT

The objective was to compare the feasibility and effectiveness of automatic deep learning networks and radiomics models in identifying the tumor-stroma ratio in pancreatic ductal adenocarcinoma. Our findings demonstrate deep learning models exhibited superior performance compared to traditional radiomics models.

KEY POINTS

• Deep learning demonstrates better performance than radiomics in differentiating tumor-stroma ratio in pancreatic ductal adenocarcinoma. • The tumor-stroma ratio in the pancreatic ductal adenocarcinoma microenvironment plays a protective role in prognosis. • Preoperative prediction of tumor-stroma ratio contributes to clinical decision-making and guiding precise medicine.

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.

Deep Learning for Fully Automated Prediction of Overall Survival in Patients Undergoing Resection for Pancreatic Cancer: A Retrospective Multicenter Study

OBJECTIVE

To develop an imaging-derived biomarker for prediction of overall survival (OS) of pancreatic cancer by analyzing preoperative multiphase contrast-enhanced computed topography (CECT) using deep learning.

BACKGROUND

Exploiting prognostic biomarkers for guiding neoadjuvant and adjuvant treatment decisions may potentially improve outcomes in patients with resectable pancreatic cancer.

METHODS

This multicenter, retrospective study included 1516 patients with resected pancreatic ductal adenocarcinoma (PDAC) from 5 centers located in China. The discovery cohort (n=763), which included preoperative multiphase CECT scans and OS data from 2 centers, was used to construct a fully automated imaging-derived prognostic biomarker-DeepCT-PDAC-by training scalable deep segmentation and prognostic models (via self-learning) to comprehensively model the tumor-anatomy spatial relations and their appearance dynamics in multiphase CECT for OS prediction. The marker was independently tested using internal (n=574) and external validation cohorts (n=179, 3 centers) to evaluate its performance, robustness, and clinical usefulness.

RESULTS

Preoperatively, DeepCT-PDAC was the strongest predictor of OS in both internal and external validation cohorts [hazard ratio (HR) for high versus low risk 2.03, 95% confidence interval (CI): 1.50-2.75; HR: 2.47, CI: 1.35-4.53] in a multivariable analysis. Postoperatively, DeepCT-PDAC remained significant in both cohorts (HR: 2.49, CI: 1.89-3.28; HR: 2.15, CI: 1.14-4.05) after adjustment for potential confounders. For margin-negative patients, adjuvant chemoradiotherapy was associated with improved OS in the subgroup with DeepCT-PDAC low risk (HR: 0.35, CI: 0.19-0.64), but did not affect OS in the subgroup with high risk.

CONCLUSIONS

Deep learning-based CT imaging-derived biomarker enabled the objective and unbiased OS prediction for patients with resectable PDAC. This marker is applicable across hospitals, imaging protocols, and treatments, and has the potential to tailor neoadjuvant and adjuvant treatments at the individual level.

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.

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.

Fractal analysis improves tumour size measurement on computed tomography in pancreatic ductal adenocarcinoma: comparison with gross pathology and multi-parametric MRI

Objectives

Tumour size measurement is pivotal for staging and stratifying patients with pancreatic ductal adenocarcinoma (PDA). However, computed tomography (CT) frequently underestimates tumour size due to insufficient depiction of the tumour rim. CT-derived fractal dimension (FD) maps might help to visualise perfusion chaos, thus allowing more realistic size measurement.

Methods

In 46 patients with histology-proven PDA, we compared tumour size measurements in routine multiphasic CT scans, CT-derived FD maps, multi-parametric magnetic resonance imaging (mpMRI), and, where available, gross pathology of resected specimens. Gross pathology was available as reference for diameter measurement in a discovery cohort of 10 patients. The remaining 36 patients constituted a separate validation cohort with mpMRI as reference for diameter and volume.

Results

Median RECIST diameter of all included tumours was 40 mm (range: 18–82 mm). In the discovery cohort, we found significant (p = 0.03) underestimation of tumour diameter on CT compared with gross pathology (Δdiameter_3D = −5.7 mm), while realistic diameter measurements were obtained from FD maps (Δdiameter_3D = 0.6 mm) and mpMRI (Δdiameter_3D = −0.9 mm), with excellent correlation between the two (R² = 0.88). In the validation cohort, CT also systematically underestimated tumour size in comparison to mpMRI (Δdiameter_3D = −10.6 mm, Δvolume = −10.2 mL), especially in larger tumours. In contrast, FD map measurements agreed excellently with mpMRI (Δdiameter_3D = +1.5 mm, Δvolume = −0.6 mL). Quantitative perfusion chaos was significantly (p = 0.001) higher in the tumour rim (FD_rim = 4.43) compared to the core (FD_core = 4.37) and remote pancreas (FD_pancreas = 4.28).

Conclusions

In PDA, fractal analysis visualises perfusion chaos in the tumour rim and improves size measurement on CT in comparison to gross pathology and mpMRI, thus compensating for size underestimation from routine CT.

Key Points

• CT-based measurement of tumour size in pancreatic adenocarcinoma systematically underestimates both tumour diameter (Δdiameter = −10.6 mm) and volume (Δvolume = −10.2 mL), especially in larger tumours.

• Fractal analysis provides maps of the fractal dimension (FD), which enable a more reliable and size-independent measurement using gross pathology or multi-parametric MRI as reference standards.

• FD quantifies perfusion chaos—the underlying pathophysiological principle—and can separate the more chaotic tumour rim from the tumour core and adjacent non-tumourous pancreas tissue.

DeepPrognosis: Preoperative prediction of pancreatic cancer survival and surgical margin via comprehensive understanding of dynamic contrast-enhanced CT imaging and tumor-vascular contact parsing

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers and carries a dismal prognosis of ∼10% in five year survival rate. Surgery remains the best option of a potential cure for patients who are evaluated to be eligible for initial resection of PDAC. However, outcomes vary significantly even among the resected patients who were the same cancer stage and received similar treatments. Accurate quantitative preoperative prediction of primary resectable PDACs for personalized cancer treatment is thus highly desired. Nevertheless, there are a very few automated methods yet to fully exploit the contrast-enhanced computed tomography (CE-CT) imaging for PDAC prognosis assessment. CE-CT plays a critical role in PDAC staging and resectability evaluation. In this work, we propose a novel deep neural network model for the survival prediction of primary resectable PDAC patients, named as 3D Contrast-Enhanced Convolutional Long Short-Term Memory network (CE-ConvLSTM), which can derive the tumor attenuation signatures or patterns from patient CE-CT imaging studies. Tumor-vascular relationships, which might indicate the resection margin status, have also been proven to hold strong relationships with the overall survival of PDAC patients. To capture such relationships, we propose a self-learning approach for automated pancreas and peripancreatic anatomy segmentation without requiring any annotations on our PDAC datasets. We then employ a multi-task convolutional neural network (CNN) to accomplish both tasks of survival outcome and margin prediction where the network benefits from learning the resection margin related image features to improve the survival prediction. Our presented framework can improve overall survival prediction performances compared with existing state-of-the-art survival analysis approaches. The new staging biomarker integrating both the proposed risk signature and margin prediction has evidently added values to be combined with the current clinical staging system.

Important CT and histopathological findings for recurrence and overall survival in patients with pancreatic ductal adenocarcinoma who underwent surgery after neoadjuvant FOLFIRINOX

OBJECTIVES

To investigate important factors for recurrence-free survival (RFS) and overall survival (OS) in patients with pancreatic ductal adenocarcinoma (PDA) who underwent surgery after neoadjuvant FOLFIRINOX using CT and histopathological findings.

MATERIALS AND METHODS

Sixty-nine patients with PDA who underwent surgery after neoadjuvant FOLFIRINOX were retrospectively included. All patients underwent baseline and first follow-up CT. Two reviewers assessed the CT findings and resectability based on the NCCN guideline. They graded extrapancreatic perineural invasion (EPNI) using a 3-point scale focused on 5 routes. Clinical and histopathological results, such as T- and N-stage, tumor regression grade (TRG) using the College of American Pathology (CAP) grading system, and resection status, were also investigated. Kaplan-Meier methods were used for RFS and OS. The Cox proportional hazard model and logistic regression model were used to identify significant predictive factors.

RESULTS

There were 57 patients (82.6%) without residual tumors (R0) and 12 patients (17.4%) with residual tumors (R1 or R2). The median RFS was 13 months (range 0~22 months). For RFS, EPNI on baseline CT (hazard ratio (HR) 2.53, 95% confidence interval (CI) 1.116-5.733, p = 0.026) and TRG (HR 1.76, 95% CI 1.000-3.076, p = 0.046) were important predictors of early recurrence. The mean OS was 48 months (range 11~35 months). For OS, TRG (HR 1.05, 95% CI 1.251-6.559, p = 0.013) was a significant factor. However, there were no independent predictors for residual tumors according to the CT findings.

CONCLUSION

EPNI on baseline CT and TRG were important prognostic factors for tumor recurrence. In addition, TRG was also an important prognostic factor for OS.

KEY POINTS

• CT and histopathological findings are helpful for predicting early recurrence and poor survival. • EPNI on baseline CT (HR 2.53, p = 0.026) is an important predictor of early recurrence. • The TRG is an important prognostic factor for early recurrence (HR 1.76, p = 0.046) and poor survival (HR 1.05, p = 0.013).