CT prediction of resectability and prognosis in patients with pancreatic ductal adenocarcinoma after neoadjuvant treatment using image findings and texture analysis

Radiomics analysis of dual-layer detector spectral CT-derived iodine maps for predicting Ki-67 PI in pancreatic ductal adenocarcinoma

OBJECTIVE

To evaluate the feasibility of radiomics analysis using dual-layer detector spectral CT (DLCT)-derived iodine maps for the preoperative prediction of the Ki-67 proliferation index (PI) in pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

A total of 168 PDAC patients who underwent DLCT examination were included and randomly allocated to the training (n = 118) and validation (n = 50) sets. A clinical model was constructed using independent clinicoradiological features identified through multivariate logistic regression analysis in the training set. The radiomics signature was generated based on the coefficients of selected features from iodine maps in the arterial and portal venous phases of DLCT. Finally, a radiomics-clinical model was developed by integrating the radiomics signature and significant clinicoradiological features. The predictive performance of three models was evaluated using the Receiver Operating Characteristic (ROC) curve and Decision Curve Analysis. The optimal model was then used to develop a nomogram, with goodness-of-fit evaluated through the calibration curve.

RESULTS

The radiomics-clinical model integrating radiomics signature, CA19-9, and CT-reported regional lymph node status demonstrated excellent performance in predicting Ki-67 PI in PDAC, which showed an area under the ROC curve of 0.979 and 0.956 in the training and validation sets, respectively. The radiomics-clinical nomogram demonstrated the improved net benefit and exhibited satisfactory consistency.

CONCLUSIONS

This exploratory study demonstrated the feasibility of using DLCT-derived iodine map-based radiomics to predict Ki-67 PI preoperatively in PDAC patients. While preliminary, our findings highlight the potential of functional imaging combined with radiomics for personalized treatment planning.

Enhanced preoperative prediction of pancreatic fistula using radiomics and clinical features with SHAP visualization

Background

Clinically relevant postoperative pancreatic fistula (CR-POPF) represents a significant complication after pancreaticoduodenectomy (PD). Therefore, the early prediction of CR-POPF is of paramount importance. Based on above, this study sought to develop a CR-POPF prediction model that amalgamates radiomics and clinical features to predict CR-POPF, utilizing Shapley Additive explanations (SHAP) for visualization.

Methods

Extensive radiomics features were extracted from preoperative enhanced Computed Tomography (CT) images of patients scheduled for PD. Subsequently, feature selection was performed using Least Absolute Shrinkage and Selection Operator (Lasso) regression and random forest (RF) algorithm to select pertinent radiomics and clinical features. Last, 15 CR-POPF prediction models were developed using five distinct machine learning (ML) predictors, based on selected radiomics features, selected clinical features, and a combination of both. Model performance was compared using DeLong's test for the area under the receiver operating characteristic curve (AUC) differences.

Results

The CR-POPF prediction model based on the XGBoost predictor with the combination of the radiomics and clinical features selected by Lasso regression and RF exhibited superior performance among these 15 CR-POPF prediction models, achieving an accuracy of 0.85, an AUC of 0.93. DeLong's test showed statistically significant differences (P < 0.05) when compared to the radiomics-only and clinical-only models, with recall of 0.63, precision of 0.65, and F1 score of 0.64.

Conclusion

The proposed CR-POPF prediction model based on the XGBoost predictor with the combination of the radiomics and clinical features selected by Lasso regression and RF can effectively predicting the CR-POPF and may provide strong support for early clinical management of CR-POPF.

CT Imaging of the Pancreas: A Review of Current Developments and Applications

Pancreatic cancer continues to pose daily challenges to clinicians, radiologists, and researchers. These challenges are encountered at each stage of pancreatic cancer management, including early detection, definite characterization, accurate assessment of tumour burden, preoperative planning when surgical resection is possible, prediction of tumour aggressiveness, response to treatment, and detection of recurrence. CT imaging of the pancreas has made major advances in recent years through innovations in research and clinical practice. Technical advances in CT imaging, often in combination with imaging biomarkers, hold considerable promise in addressing such challenges. Ongoing research in dual-energy and spectral photon-counting computed tomography, new applications of artificial intelligence and image rendering have led to innovative implementations that allow now a more precise diagnosis of pancreatic cancer and other diseases affecting this organ. This article aims to explore the major research initiatives and technological advances that are shaping the landscape of CT imaging of the pancreas. By highlighting key contributions in diagnostic imaging, artificial intelligence, and image rendering, this article provides a comprehensive overview of how these innovations are enhancing diagnostic precision and improving outcome in patients with pancreatic diseases.

AI-Driven insights in pancreatic cancer imaging: from pre-diagnostic detection to prognostication

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related deaths in the United States, largely due to its poor five-year survival rate and frequent late-stage diagnosis. A significant barrier to early detection even in high-risk cohorts is that the pancreas often appears morphologically normal during the pre-diagnostic phase. Yet, the disease can progress rapidly from subclinical stages to widespread metastasis, undermining the effectiveness of screening. Recently, artificial intelligence (AI) applied to cross-sectional imaging has shown significant potential in identifying subtle, early-stage changes in pancreatic tissue that are often imperceptible to the human eye. Moreover, AI-driven imaging also aids in the discovery of prognostic and predictive biomarkers, essential for personalized treatment planning. This article uniquely integrates a critical discussion on AI's role in detecting visually occult PDAC on pre-diagnostic imaging, addresses challenges of model generalizability, and emphasizes solutions like standardized datasets and clinical workflows. By focusing on both technical advancements and practical implementation, this article provides a forward-thinking conceptual framework that bridges current gaps in AI-driven PDAC research.

Development and validation of a model based on preoperative dual-layer detector spectral computed tomography 3D VOI-based quantitative parameters to predict high Ki-67 proliferation index in pancreatic ductal adenocarcinoma

OBJECTIVE

To develop and validate a model integrating dual-layer detector spectral computed tomography (DLCT) three-dimensional (3D) volume of interest (VOI)-based quantitative parameters and clinical features for predicting Ki-67 proliferation index (PI) in pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

A total of 162 patients with histopathologically confirmed PDAC who underwent DLCT examination were included and allocated to the training (114) and validation (48) sets. 3D VOI-iodine concentration (IC), 3D VOI-slope of the spectral attenuation curves, and 3D VOI-effective atomic number were obtained from the portal venous phase. The significant clinical features and DLCT quantitative parameters were identified through univariate analysis and multivariate logistic regression. The discrimination capability and clinical applicability of the clinical, DLCT, and DLCT-clinical models were quantified by the Receiver Operating Characteristic curve (ROC) and Decision Curve Analysis (DCA), respectively. The optimal model was then used to develop a nomogram, with the goodness-of-fit evaluated through the calibration curve.

RESULTS

The DLCT-clinical model demonstrated superior predictive capability and a satisfactory net benefit for Ki-67 PI in PDAC compared to the clinical and DLCT models. The DLCT-clinical model integrating 3D VOI-IC and CA125 showed area under the ROC curves of 0.939 (95% CI, 0.895-0.982) and 0.915 (95% CI, 0.834-0.996) in the training and validation sets, respectively. The nomogram derived from the DLCT-clinical model exhibited favorable calibration, as depicted by the calibration curve.

CONCLUSIONS

The proposed model based on DLCT 3D VOI-IC and CA125 is a non-invasive and effective preoperative prediction tool demonstrating favorable predictive performance for Ki-67 PI in PDAC.

CRITICAL RELEVANCE STATEMENT

The dual-layer detector spectral computed tomography-clinical model could help predict high Ki-67 PI in pancreatic ductal adenocarcinoma patients, which may help clinicians provide appropriate and individualized treatments.

KEY POINTS

Dual-layer detector spectral CT (DLCT) could predict Ki-67 in pancreatic ductal adenocarcinoma (PDAC). The DLCT-clinical model improved the differential diagnosis of Ki-67. The nomogram showed satisfactory calibration and net benefit for discriminating Ki-67.

Peritumoral MRI Radiomics Features Increase the Evaluation Efficiency for Response to Chemotherapy in Patients With Epithelial Ovarian Cancer

BACKGROUND

It remains unclear whether extracting peritumoral volume (PTV) radiomics features are useful tools for evaluating response to chemotherapy of epithelial ovarian cancer (EOC).

PURPOSE

To evaluate MRI radiomics signatures (RS) capturing subtle changes of PTV and their added evaluation performance to whole tumor volume (WTV) for response to chemotherapy in patients with EOC.

STUDY TYPE

Retrospective.

POPULATION

219 patients aged from 15 to 79 years were enrolled.

FIELD STRENGTH/SEQUENCE

3.0 or 1.5T, axial fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), and contrast enhanced T1-weighted imaging (CE-T1WI).

ASSESSMENT

MRI features were extracted from the four axial sequences and six different volumes of interest (VOIs) (WTV and WTV + PTV (WPTV)) with different peritumor sizes (PS) ranging from 1 to 5 mm. Those features underwent preprocessing, and the most informative features were selected using minimum redundancy maximum relevance and least absolute shrinkage and selection operator to construct the RS. The optimal RS, with the highest area under the curve (AUC) of receiver operating characteristic was then integrated with independent clinical characteristics through multivariable logistic regression to construct the radiomics-clinical model (RCM).

STATISTICAL TESTS

Mann-Whitney U test, chi-squared test, DeLong test, log-rank test. P < 0.05 indicated a significant difference.

RESULTS

All the RSs constructed on WPTV exhibited higher AUCs (0.720-0.756) than WTV (0.671). Of which, RS with PS = 2 mm displayed a significantly better performance (AUC = 0.756). International Federation of Gynecology and Obstetrics (FIGO) stage was identified as the exclusive independent clinical evaluation characteristic, and the RCM demonstrated higher AUC (0.790) than the RS, but without statistical significance (P = 0.261).

DATA CONCLUSION

The radiomics features extracted from PTV could increase the efficiency of WTV radiomics for evaluating the chemotherapy response of EOC. The cut-off of 2 mm PTV was a reasonable value to obtain effective evaluation efficiency.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 2.

Computed tomography-based vascular burden index as a predictor of vascular resection and pathological vascular invasion in pancreatic cancer with neo-adjuvant chemotherapy

BACKGROUND

Determination of vessel resection in patients with pancreatectomy after neo-adjuvant chemotherapy remains controversial. The recently introduced computed tomography-based vascular burden index presents a potential solution to this challenge. This study aimed to evaluate the model performance for the prediction of vascular resection and pathological invasion.

METHODS

Patients who underwent surgery after neo-adjuvant chemotherapy were included. Two independent reviewers measured the vascular tumour burden index around the adjacent artery (AVBI), and vein (VVBI). The area under the curve was compared to assess the predictive capacity of vascular burden index values and their changes for vascular resection and pathological vascular invasion.

RESULTS

Among 252 patients, 179 and 73 had borderline resectable and locally advanced pancreatic cancer, respectively. Concurrent vessel resection and pathological vascular invasion were observed in 121 (48.0 %) and 42 (16.6 %) patients, respectively. In all patients, the VVBI (area under the curve: 0.872) and AVBI (0.911) after neo-adjuvant therapy significantly predicted vessel resection. In patients with vascular resection, the VVBI after neo-adjuvant chemotherapy (0.752) and delta value of the AVBI (0.706) demonstrated better performance for predicting pathological invasion of the resected vein. The regression of the AVBI and VVBI was an independent prognostic factor for survival (hazard ratio: 0.54, 95 % confidence interval: 0.34-0.85; P = 0.009) CONCLUSIONS: Regressed VVBI on serial computed tomography scans is useful for predicting vein resection and pathological venous invasion before surgery. The delta value of the AVBI may therefore be helpful for predicting pathological arterial invasion after neo-adjuvant chemotherapy.

Development and validation of radiomics nomogram for metastatic status of epithelial ovarian cancer

To develop and validate an enhanced CT-based radiomics nomogram for evaluating preoperative metastasis risk of epithelial ovarian cancer (EOC). One hundred and nine patients with histologically confirmed EOC were retrospectively enrolled. The volume of interest (VOI) was delineated in preoperative enhanced CT images, and 851 radiomics features were extracted. The radiomics features were selected by the least absolute shrinkage and selection operator (LASSO), and the rad-score was calculated using the formula of the radiomics label. A clinical model, radiomics model, and combined model were constructed using the logistic regression classification algorithm. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the diagnostic performance of the models. Seventy-five patients (68.8%) were histologically confirmed to have metastasis. Eleven optimal radiomics features were retained by the LASSO algorithm to develop the radiomic model. The combined model for evaluating metastasis of EOC achieved area under the curve (AUC) values of 0.929 (95% CI 0.8593-0.9996) in the training cohort and 0.909 (95% CI 0.7921-1.0000) in the test cohort. To facilitate clinical use, a radiomic nomogram was built by combining the clinical characteristics with rad-score. The DCA indicated that the nomogram had the most significant net benefit when the threshold probability exceeded 15%, surpassing the benefits of both the treat-all and treat-none strategies. Compared with clinical model and radiomics model, the radiomics nomogram has the best diagnostic performance in evaluating EOC metastasis. The nomogram is a useful and convenient tool for clinical doctors to develop personalized treatment plans for EOC patients.

Development of a CT radiomics nomogram for preoperative prediction of Ki-67 index in pancreatic ductal adenocarcinoma: a two-center retrospective study

OBJECTIVES

To develop and validate a contrast-enhanced computed tomography (CECT)-based radiomics nomogram for the preoperative evaluation of Ki-67 proliferation status in pancreatic ductal adenocarcinoma (PDAC).

METHODS

In this two-center retrospective study, a total of 181 patients (95 in the training cohort; 42 in the testing cohort, and 44 in the external validation cohort) with PDAC who underwent CECT examination were included. Radiomic features were extracted from portal venous phase images. The radiomics signatures were built by using two feature-selecting methods (relief and recursive feature elimination) and four classifiers (support vector machine, naive Bayes, linear discriminant analysis (LDA), and logistic regression (LR)). Multivariate LR was used to build a clinical model and radiomics-clinical nomogram. The predictive performances of the models were evaluated using area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA).

RESULTS

The relief selector and LDA classifier using twelve features built the optimal radiomics signature, with AUCs of 0.948, 0.927, and 0.824 in the training, testing, and external validation cohorts, respectively. The radiomics-clinical nomogram incorporating the optimal radiomics signature, CT-reported lymph node status, and CA19-9 showed better predictive performance with AUCs of 0.976, 0.955, and 0.882 in the training, testing, and external validation cohorts, respectively. The calibration curve and DCA demonstrated goodness-of-fit and improved benefits in clinical practice of the nomogram.

CONCLUSIONS

The radiomics-clinical nomogram is an effective and non-invasive computer-aided tool to predict the Ki-67 expression status in patients with PDAC.

CLINICAL RELEVANCE STATEMENT

The radiomics-clinical nomogram is an effective and non-invasive computer-aided tool to predict the Ki-67 expression status in patients with pancreatic ductal adenocarcinoma.

KEY POINTS

The radiomics analysis could be helpful to predict Ki-67 expression status in patients with pancreatic ductal adenocarcinoma (PDAC). The radiomics-clinical nomogram integrated with the radiomics signature, clinical data, and CT radiological features could significantly improve the differential diagnosis of Ki-67 expression status. The radiomics-clinical nomogram showed satisfactory calibration and net benefit for discriminating high and low Ki-67 expression status in PDAC.

Delta Radiomic Features Predict Resection Margin Status and Overall Survival in Neoadjuvant-Treated Pancreatic Cancer Patients

BACKGROUND

Neoadjuvant therapy (NAT) emerged as the standard of care for patients with pancreatic ductal adenocarcinoma (PDAC) who undergo surgery; however, surgery is morbid, and tools to predict resection margin status (RMS) and prognosis in the preoperative setting are needed. Radiomic models, specifically delta radiomic features (DRFs), may provide insight into treatment dynamics to improve preoperative predictions.

METHODS

We retrospectively collected clinical, pathological, and surgical data (patients with resectable, borderline, locally advanced, and metastatic disease), and pre/post-NAT contrast-enhanced computed tomography (CT) scans from PDAC patients at the University of Texas Southwestern Medical Center (UTSW; discovery) and Humanitas Hospital (validation cohort). Gross tumor volume was contoured from CT scans, and 257 radiomics features were extracted. DRFs were calculated by direct subtraction of pre/post-NAT radiomic features. Cox proportional models and binary prediction models, including/excluding clinical variables, were constructed to predict overall survival (OS), disease-free survival (DFS), and RMS.

RESULTS

The discovery and validation cohorts comprised 58 and 31 patients, respectively. Both cohorts had similar clinical characteristics, apart from differences in NAT (FOLFIRINOX vs. gemcitabine/nab-paclitaxel; p < 0.05) and type of surgery resections (pancreatoduodenectomy, distal or total pancreatectomy; p < 0.05). The model that combined clinical variables (pre-NAT carbohydrate antigen (CA) 19-9, the change in CA19-9 after NAT (∆CA19-9), and resectability status) and DRFs outperformed the clinical feature-based models and other radiomics feature-based models in predicting OS (UTSW: 0.73; Humanitas: 0.66), DFS (UTSW: 0.75; Humanitas: 0.64), and RMS (UTSW 0.73; Humanitas: 0.69).

CONCLUSIONS

Our externally validated, predictive/prognostic delta-radiomics models, which incorporate clinical variables, show promise in predicting the risk of predicting RMS in NAT-treated PDAC patients and their OS or DFS.

CT Imaging Assessment of Pancreatic Adenocarcinoma Resectability after Neoadjuvant Therapy: Current Status and Perspective on the Use of Radiomics

Pancreatic adenocarcinoma (PDAC) is the most common pancreatic cancer and is associated with poor prognosis, a high mortality rate, and a substantial number of healthy life years lost. Surgical resection is the primary treatment option for patients with resectable disease; however, only 10-20% of all patients with PDAC are eligible for resection at the time of diagnosis. In this context, neoadjuvant therapy has the potential to increase the number of patients who are eligible for resection, thereby improving the overall survival rate. For patients who undergo neoadjuvant therapy, computed tomography (CT) remains the primary imaging tool for assessing treatment response. Nevertheless, the interpretation of imaging findings in this context remains challenging, given the similarity between viable tumor and treatment-related changes following neoadjuvant therapy. In this review, following an overview of the various treatment options for PDAC according to its resectability status, we will describe the key challenges regarding CT-based evaluation of PDAC treatment response following neoadjuvant therapy, as well as summarize the literature on CT-based evaluation of PDAC treatment response, including the use of radiomics. Finally, we will outline key recommendations for the management of PDAC after neoadjuvant therapy, taking into consideration CT-based findings.

Computed Tomography-Based Radiomics Using Tumor and Vessel Features to Assess Resectability in Cancer of the Pancreatic Head

The preoperative prediction of resectability pancreatic ductal adenocarcinoma (PDAC) is challenging. This retrospective single-center study examined tumor and vessel radiomics to predict the resectability of PDAC in chemo-naïve patients. The tumor and adjacent arteries and veins were segmented in the portal-venous phase of contrast-enhanced CT scans, and radiomic features were extracted. Features were selected via stability and collinearity testing, and least absolute shrinkage and selection operator application (LASSO). Three models, using tumor features, vessel features, and a combination of both, were trained with the training set (N = 86) to predict resectability. The results were validated with the test set (N = 15) and compared to the multidisciplinary team's (MDT) performance. The vessel-features-only model performed best, with an AUC of 0.92 and sensitivity and specificity of 97% and 73%, respectively. Test set validation showed a sensitivity and specificity of 100% and 88%, respectively. The combined model was as good as the vessel model (AUC = 0.91), whereas the tumor model showed poor performance (AUC = 0.76). The MDT's prediction reached a sensitivity and specificity of 97% and 84% for the training set and 88% and 100% for the test set, respectively. Our clinician-independent vessel-based radiomics model can aid in predicting resectability and shows performance comparable to that of the MDT. With these encouraging results, improved, automated, and generalizable models can be developed that reduce workload and can be applied in non-expert hospitals.

Radiological parameters to predict pancreatic texture: Current evidence and future perspectives

Preoperative prediction of the postoperative pancreatic fistula risk is critical in the current era of minimally invasive pancreatic surgeries to tailor perioperative management, thereby minimizing postoperative morbidity. Pancreatic duct diameter can be readily measured by any routine imaging used to diagnose pancreatic disease. However, radiological evaluation of pancreatic texture, an important determinant of pancreatic fistula, has not been widely used to predict the risk of postoperative pancreatic fistula. Qualitative and quantitative assessment of pancreatic fibrosis and fat fraction provides the basis for predicting pancreatic texture. Traditionally computed tomography has been utilized in identifying and characterizing pancreatic lesions and background parenchymal pathologies. With the increasing utilisation of endoscopic ultrasound and magnetic resonance imaging for evaluating pancreatic pathologies, elastography is emerging as a promising tool for predicting pancreatic texture. Also, recent studies have shown that early surgery for chronic pancreatitis is associated with better pain relief and preservation of pancreatic function. Pancreatic texture assessment can allow early diagnosis of chronic pancreatitis, facilitating early intervention. The present review outlines the current evidence in utilizing various imaging modalities for determining the pancreatic texture based on different parameters and image sequences. However, multidisciplinary investigations using strong radiologic-pathologic correlation are needed to standardize and establish the role of these non-invasive diagnostic tools in predicting pancreatic texture.

Computer-Aided Detection for Pancreatic Cancer Diagnosis: Radiological Challenges and Future Directions

Radiological imaging plays a crucial role in the detection and treatment of pancreatic ductal adenocarcinoma (PDAC). However, there are several challenges associated with the use of these techniques in daily clinical practice. Determination of the presence or absence of cancer using radiological imaging is difficult and requires specific expertise, especially after neoadjuvant therapy. Early detection and characterization of tumors would potentially increase the number of patients who are eligible for curative treatment. Over the last decades, artificial intelligence (AI)-based computer-aided detection (CAD) has rapidly evolved as a means for improving the radiological detection of cancer and the assessment of the extent of disease. Although the results of AI applications seem promising, widespread adoption in clinical practice has not taken place. This narrative review provides an overview of current radiological CAD systems in pancreatic cancer, highlights challenges that are pertinent to clinical practice, and discusses potential solutions for these challenges.

Pancreatic Cancer Biomarkers: Oncogenic Mutations, Tissue and Liquid Biopsies, and Radiomics-A Review

Pancreatic cancer is one of the most fatal malignancies, as approximately 80% of patients are at advanced stages by the time of diagnosis. The main reason for the poor overall survival is late diagnosis that is partially due to the lack of tools for early-stage detection. In addition, there are several challenges in evaluating response to treatment and predicting prognosis. In this article, we do a review of the most common pancreatic cancer biomarkers with emphasis in new and promising approaches. Liquid biopsies seem to have important clinical applications in early detection, screening, prognosis, and longitudinal monitoring of on-treatment patients. Together with biomarkers in imaging, can represent valuable alternative non-invasive tools in order to achieve a more effective management of pancreatic cancer patients.

Cancer-cell-derived sialylated IgG as a novel biomarker for predicting poor pathological response to neoadjuvant therapy and prognosis in pancreatic cancer

BACKGROUND

Neoadjuvant therapy (NAT) is increasingly applied in pancreatic ductal adenocarcinoma (PDAC); however, accurate prediction of therapeutic response to NAT remains a pressing clinical challenge. Cancer-cell-derived sialylated immunoglobulin G (SIA-IgG) was previously identified as a prognostic biomarker in PDAC. This study aims to explore whether SIA-IgG expression in treatment-naïve fine needle aspirate (FNA) biopsy specimens could predict the pathological response (PR) to NAT for PDAC.

METHODS

Endoscopic ultrasonography-guided FNA biopsy specimens prior to NAT were prospectively obtained from 72 patients with PDAC at the Johns Hopkins Hospital. SIA-IgG expression of PDAC specimens was assessed by immunohistochemistry. Associations between SIA-IgG expression and PR, as well as patient prognosis, were analyzed. A second cohort enrolling surgically resected primary tumor specimens from 79 patients with PDAC was used to validate the prognostic value of SIA-IgG expression.

RESULTS

SIA-IgG was expressed in 58.3% of treatment-naïve FNA biopsies. Positive SIA-IgG expression at diagnosis was associated with unfavorable PR and can serve as an independent predictor of PR. The sensitivity and specificity of SIA-IgG expression in FNA specimens in predicting an unfavorable PR were 63.9% and 80.6%, respectively. Both positive SIA-IgG expression in treatment-naïve FNA specimens and high SIA-IgG expression in surgically resected primary tumor specimens were significantly associated with shorter survival.

CONCLUSIONS

Assessment of SIA-IgG on FNA specimens prior to NAT may help predict PR for PDAC. Additionally, SIA-IgG expression in treatment-naïve FNA specimens and surgically resected primary tumor specimens were predictive of the prognosis for PDAC.

A radiomics nomogram model for predicting prognosis of pancreatic ductal adenocarcinoma after high-intensity focused ultrasound surgery

OBJECTIVE

To develop and validate a radiomics nomogram for predicting the survival of patients with pancreatic ductal adenocarcinoma (PDAC) after receiving high-intensity focused ultrasound (HIFU) treatment.

METHODS

A total of 52 patients with PDAC were enrolled. To select features, the least absolute shrinkage and selection operator algorithm were applied, and the radiomics score (Rad-Score) was obtained. Radiomics model, clinics model, and radiomics nomogram model were constructed by multivariate regression analysis. The identification, calibration, and clinical application of nomogram were evaluated. Survival analysis was performed using Kaplan-Meier (K-M) method.

RESULTS

According to conclusions made from the multivariate Cox model, Rad-Score, and tumor size were independent risk factors for OS. Compared with the clinical model and radiomics model, the combination of Rad-Score and clinicopathological factors could better predict the survival of patients. Patients were divided into high-risk and low-risk groups according to Rad-Score. K-M analysis showed that the difference between the two groups was statistically significant (p < 0.05). In addition, the radiomics nomogram model indicated better discrimination, calibration, and clinical practicability in training and validation cohorts.

CONCLUSION

The radiomics nomogram effectively evaluates the prognosis of patients with advanced pancreatic cancer after HIFU surgery, which could potentially improve treatment strategies and promote individualized treatment of advanced pancreatic cancer.

Development and external validation of a radiomics combined with clinical nomogram for preoperative prediction prognosis of resectable pancreatic ductal adenocarcinoma patients

PURPOSE

To develop and externally validate a prognosis nomogram based on contrast-enhanced computed tomography (CECT) combined clinical for preoperative prognosis prediction of patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

184 patients from Center A with histopathologically confirmed PDAC who underwent CECT were included and allocated to training cohort (n=111) and internal validation cohort (n=28). The radiomic score (Rad - score) for predicting overall survival (OS) was constructed by using the least absolute shrinkage and selection operator (LASSO). Univariate and multivariable Cox regression analysis was used to construct clinic-pathologic features. Finally, a radiomics nomogram incorporating the Rad - score and clinical features was established. External validation was performed using Center B dataset (n = 45). The validation of nomogram was evaluated by calibration curve, Harrell's concordance index (C-index) and decision curve analysis (DCA). The Kaplan-Meier (K-M) method was used for OS analysis.

RESULTS

Univariate and multivariate analysis indicated that Rad - score, preoperative CA 19-9 and postoperative American Joint Committee on Cancer (AJCC) TNM stage were significant prognostic factors. The nomogram based on Rad - score and preoperative CA19-9 was found to exhibit excellent prediction ability: in the training cohort, C-index was superior to that of the preoperative CA19-9 (0.713 vs 0.616, P< 0.001) and AJCC TNM stage (0.713 vs 0.614, P< 0.001); the C-index was also had good performance in the validation cohort compared with CA19-9 (internal validation cohort: 0.694 vs 0.555, P< 0.001; external validation cohort: 0.684 vs 0.607, P< 0.001) and AJCC TNM stage (internal validation cohort: 0.694 vs 0.563, P< 0.001; external validation cohort: 0.684 vs 0.596, P< 0.001). The calibration plot and DCA showed excellent predictive accuracy in the validation cohort.

CONCLUSION

We established a well-designed nomogram to accurately predict OS of PDAC preoperatively. The nomogram showed a satisfactory prediction effect and was worthy of further evaluation in the future.

Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis

BACKGROUND & AIMS

Our purpose was to detect pancreatic ductal adenocarcinoma (PDAC) at the prediagnostic stage (3-36 months before clinical diagnosis) using radiomics-based machine-learning (ML) models, and to compare performance against radiologists in a case-control study.

METHODS

Volumetric pancreas segmentation was performed on prediagnostic computed tomography scans (CTs) (median interval between CT and PDAC diagnosis: 398 days) of 155 patients and an age-matched cohort of 265 subjects with normal pancreas. A total of 88 first-order and gray-level radiomic features were extracted and 34 features were selected through the least absolute shrinkage and selection operator-based feature selection method. The dataset was randomly divided into training (292 CTs: 110 prediagnostic and 182 controls) and test subsets (128 CTs: 45 prediagnostic and 83 controls). Four ML classifiers, k-nearest neighbor (KNN), support vector machine (SVM), random forest (RM), and extreme gradient boosting (XGBoost), were evaluated. Specificity of model with highest accuracy was further validated on an independent internal dataset (n = 176) and the public National Institutes of Health dataset (n = 80). Two radiologists (R4 and R5) independently evaluated the pancreas on a 5-point diagnostic scale.

RESULTS

Median (range) time between prediagnostic CTs of the test subset and PDAC diagnosis was 386 (97-1092) days. SVM had the highest sensitivity (mean; 95% confidence interval) (95.5; 85.5-100.0), specificity (90.3; 84.3-91.5), F1-score (89.5; 82.3-91.7), area under the curve (AUC) (0.98; 0.94-0.98), and accuracy (92.2%; 86.7-93.7) for classification of CTs into prediagnostic versus normal. All 3 other ML models, KNN, RF, and XGBoost, had comparable AUCs (0.95, 0.95, and 0.96, respectively). The high specificity of SVM was generalizable to both the independent internal (92.6%) and the National Institutes of Health dataset (96.2%). In contrast, interreader radiologist agreement was only fair (Cohen's kappa 0.3) and their mean AUC (0.66; 0.46-0.86) was lower than each of the 4 ML models (AUCs: 0.95-0.98) (P < .001). Radiologists also recorded false positive indirect findings of PDAC in control subjects (n = 83) (7% R4, 18% R5).

CONCLUSIONS

Radiomics-based ML models can detect PDAC from normal pancreas when it is beyond human interrogation capability at a substantial lead time before clinical diagnosis. Prospective validation and integration of such models with complementary fluid-based biomarkers has the potential for PDAC detection at a stage when surgical cure is a possibility.

Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances

Pancreatic ductal carcinoma (PDAC) is one of the leading causes of cancer-related death worldwide. Computed tomography (CT) remains the primary imaging modality for diagnosis of PDAC. However, CT has limitations for early pancreatic tumor detection and tumor characterization so that it is currently challenged by magnetic resonance imaging. More recently, a particular attention has been given to radiomics for the characterization of pancreatic lesions using extraction and analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence (AI) with the aim of better characterizing pancreatic lesions and providing a more precise assessment of tumor burden. This review article sums up recent advances in imaging of PDAC in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning. In addition, current applications of radiomics and AI in the field of PDAC are discussed.

A systematic review of prognosis predictive role of radiomics in pancreatic cancer: heterogeneity markers or statistical tricks?

OBJECTIVES

We aimed to systematically evaluate the prognostic prediction accuracy of radiomics features extracted from pre-treatment imaging in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

Radiomics literature on overall survival (OS) prediction of PDAC were all included in this systematic review. A further meta-analysis was performed on the effect size of first-order entropy. Methodological quality and risk of bias of the included studies were assessed by the radiomics quality score (RQS) and prediction model risk of bias assessment tool (PROBAST).

RESULTS

Twenty-three studies were finally identified in this review. Two (8.7%) studies compared prognosis prediction ability between radiomics model and TNM staging model by C-index, and both showed a better performance of the radiomics. Twenty-one (91.3%) studies reported significant predictive values of radiomics features. Nine (39.1%) studies were included in the meta-analysis, and it showed a significant correlation between first-order entropy and OS (HR 1.66, 95%CI 1.18-2.34). RQS assessment revealed validation was only performed in 5 (21.7%) studies on internal datasets and 2 (8.7%) studies on external datasets. PROBAST showed that 22 (95.7%) studies have a high risk of bias in participants because of the retrospective study design.

CONCLUSION

First-order entropy was significantly associated with OS and might improve the accuracy of PDAC prognosis prediction. Existing studies were poorly validated, and it should be noted in future studies. Modification of PROBAST for radiomics studies is necessary since the strict requirements of prospective study design may not be applicable to the demand for a large sample size in the model construction stage.

KEY POINTS

• Radiomics based on the primary lesion holds great potential for prognosis prediction. First-order entropy was significantly associated with the overall survival of PDAC and might improve the accuracy of current PDAC prognosis prediction. • We strongly recommend that at least an internal validation should be conducted in any radiomics study. Attention should be paid to the complex relationships between radiomics features. • Due to the close relationship between radiomics and big data, the strict requirement of prospective study design in PROABST may not be appropriate for radiomics studies. A balance between study types and sample sizes for radiomics studies needs to be found in the model construction stage.

Computed Tomography-based Radiomics Evaluation of Postoperative Local Recurrence of Pancreatic Ductal Adenocarcinoma

OBJECTIVE

To develop and validate an effective model for identifying patients with postoperative local disease recurrence of pancreatic ductal adenocarcinoma (PDAC).

METHODS

A total of 153 patients who had undergone surgical resection of PDAC with regular postoperative follow-up were consecutively enrolled and randomly divided into training (n = 108) and validation (n = 45) cohorts. The postoperative soft-tissue biopsy results or clinical follow-up results served as the reference diagnostic criteria. Radiomics analysis of the postoperative soft-tissue was performed on a commercially available prototype software using portal vein phase image. Three models were built to characterize postoperative soft tissue: computed tomography (CT)-based radiomics, clinicoradiological, and their combination. The area under the receiver operating characteristic curves (AUC) was used to evaluate the differential diagnostic performance. A nomogram was used to select the final model with best performance. One radiologist's diagnostic choices that were made with and without the nomogram's assistance were evaluated.

RESULTS

A seven-feature-combined radiomics signature was constructed as a predictor of postoperative local recurrence. The nomogram model combining the radiomics signature with postoperative CA 19-9 elevation showed the best performance (training cohort, AUC = 0.791 [95%CI: 0.707, 0.876]; validation cohort, AUC = 0.742 [95%CI: 0.590, 0.894]). In the validation cohort, the AUC for differential diagnosis was significantly improved for the combined model relative to that for postoperative CA 19-9 elevation (AUC = 0.742 vs. 0.533, p < 0.001). The calibration curve and decision curve analysis demonstrated the clinical usefulness of the proposed nomogram. The diagnostic performance of the radiologist was not significantly improve by using the proposed nomogram (AUC = 0.742 vs. 0.670, p = 0.17).

CONCLUSION

The combined model using CT radiomic features and CA 19-9 elevation effectively characterized postoperative soft tissue and potentially may improve treatment strategies and facilitate personalized treatment for PDAC after surgical resection.

Identifying Outcomes of Patients With Advanced Pancreatic Adenocarcinoma and RECIST Stable Disease Using Radiomics Analysis

Few studies have explored the biomarkers for predicting the heterogeneous outcomes of patients with advanced pancreatic adenocarcinoma showing stable disease (SD) on the initial postchemotherapy computed tomography. We aimed to devise a radiomics signature (RS) to predict these outcomes for further risk stratification.

CT classification model of pancreatic serous cystic neoplasms and mucinous cystic neoplasms based on a deep neural network

BACKGROUND

At present, numerous challenges exist in the diagnosis of pancreatic SCNs and MCNs. After the emergence of artificial intelligence (AI), many radiomics research methods have been applied to the identification of pancreatic SCNs and MCNs.

PURPOSE

A deep neural network (DNN) model termed Multi-channel-Multiclassifier-Random Forest-ResNet (MMRF-ResNet) was constructed to provide an objective CT imaging basis for differential diagnosis between pancreatic serous cystic neoplasms (SCNs) and mucinous cystic neoplasms (MCNs).

MATERIALS AND METHODS

This study is a retrospective analysis of pancreatic unenhanced and enhanced CT images in 63 patients with pancreatic SCNs and 47 patients with MCNs (3 of which were mucinous cystadenocarcinoma) confirmed by pathology from December 2010 to August 2016. Different image segmented methods (single-channel manual outline ROI image and multi-channel image), feature extraction methods (wavelet, LBP, HOG, GLCM, Gabor, ResNet, and AlexNet) and classifiers (KNN, Softmax, Bayes, random forest classifier, and Majority Voting rule method) are used to classify the nature of the lesion in each CT image (SCNs/MCNs). Then, the comparisons of classification results were made based on sensitivity, specificity, precision, accuracy, F1 score, and area under the receiver operating characteristic curve (AUC), with pathological results serving as the gold standard.

RESULTS

Multi-channel-ResNet (AUC 0.98) was superior to Manual-ResNet (AUC 0.91).CT image characteristics of lesions extracted by ResNet are more representative than wavelet, LBP, HOG, GLCM, Gabor, and AlexNet. Compared to the use of three classifiers alone and Majority Voting rule method, the use of the MMRF-ResNet model exhibits a better evaluation effect (AUC 0.96) for the classification of the pancreatic SCNs and MCNs.

CONCLUSION

The CT image classification model MMRF-ResNet is an effective method to distinguish between pancreatic SCNs and MCNs.

Application of artificial intelligence to pancreatic adenocarcinoma

BACKGROUND AND OBJECTIVES

Pancreatic cancer (PC) is one of the deadliest cancers worldwide although substantial advancement has been made in its comprehensive treatment. The development of artificial intelligence (AI) technology has allowed its clinical applications to expand remarkably in recent years. Diverse methods and algorithms are employed by AI to extrapolate new data from clinical records to aid in the treatment of PC. In this review, we will summarize AI's use in several aspects of PC diagnosis and therapy, as well as its limits and potential future research avenues.

METHODS

We examine the most recent research on the use of AI in PC. The articles are categorized and examined according to the medical task of their algorithm. Two search engines, PubMed and Google Scholar, were used to screen the articles.

RESULTS

Overall, 66 papers published in 2001 and after were selected. Of the four medical tasks (risk assessment, diagnosis, treatment, and prognosis prediction), diagnosis was the most frequently researched, and retrospective single-center studies were the most prevalent. We found that the different medical tasks and algorithms included in the reviewed studies caused the performance of their models to vary greatly. Deep learning algorithms, on the other hand, produced excellent results in all of the subdivisions studied.

CONCLUSIONS

AI is a promising tool for helping PC patients and may contribute to improved patient outcomes. The integration of humans and AI in clinical medicine is still in its infancy and requires the in-depth cooperation of multidisciplinary personnel.

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.

Systematic review and meta-analysis of diagnostic performance of CT imaging for assessing resectability of pancreatic ductal adenocarcinoma after neoadjuvant therapy: importance of CT criteria

PURPOSE

To assess the CT diagnostic performance for evaluating resectability of pancreatic ductal adenocarcinoma (PDAC) after neoadjuvant therapy and identify the factor(s) that affect(s) diagnostic performance.

METHODS

Databases were searched to identify studies published from January 1, 2000, to November 5, 2019 that evaluated the CT diagnostic performance for assessing resectability of post-neoadjuvant PDAC. Two reviewers independently extracted data and assessed the study quality. A meta-analysis was performed to obtain summary sensitivity and specificity values using a bivariate random-effects model, and heterogeneity across studies was assessed. Univariable meta-regression analysis was performed with eight variables, including the different CT criteria for resectability, conventional National Comprehensive Cancer Network (NCCN) criteria for upfront surgery, and modified criteria for post-neoadjuvant surgery.

RESULTS

Ten studies were included and analyzed. The summary sensitivity and specificity for resectability were 78% (95% CI 68-86%) and 60% (95% CI 44-74%), respectively. No significant heterogeneity was identified (bivariate correlation coefficient ρ = - 1, p-value for hierarchical summary receiver operating characteristics model β = 0.667). The two different CT criteria showed different diagnostic performance (p < 0.01), with higher sensitivity (81% [95% CI 73-90%] vs. 28% [95% CI 15-42%], p < 0.01) and lower specificity (57% [95% CI 41-73%] vs. 90% [95% CI 80-100%], p < 0.01) for the modified criteria. No other variables affected the diagnostic performance.

CONCLUSION

CT criteria were the factors that affected the diagnostic performance. Modification of the conventional criteria improved sensitivity but lowered specificity. Further modifications are required to improve specificity and uniformity.

Role of imaging in evaluating the response after neoadjuvant treatment for pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy. Despite the development of multimodality treatments, including surgical resection, radiotherapy, and chemotherapy, the long-term prognosis of patients with PDAC remains poor. Recently, the introduction of neoadjuvant treatment (NAT) has made more patients amenable to surgery, increasing the possibility of R0 resection, treatment of occult micro-metastasis, and prolongation of overall survival. Imaging plays a vital role in tumor response evaluation after NAT. However, conventional imaging modalities such as multidetector computed tomography have limited roles in the assessment of tumor resectability after NAT for PDAC because of the similar appearance of tissue fibrosis and tumor infiltration. Perfusion computed tomography, using blood perfusion as a biomarker, provides added value in predicting the histopathologic response of PDAC to NAT by reflecting the changes in tumor matrix and fibrosis content. Other imaging technologies, including diffusion-weighted imaging of magnetic resonance imaging and positron emission tomography, can reveal the tumor response by monitoring the structural changes in tumor cells and functional metabolic changes in tumors after NAT. In addition, with the renewed interest in data acquisition and analysis, texture analysis and radiomics have shown potential for the early evaluation of the response to NAT, thus improving patient stratification to achieve accurate and intensive treatment. In this review, we briefly introduce the application and value of NAT in resectable and unresectable PDAC. We also summarize the role of imaging in evaluating the response to NAT for PDAC, as well as the advantages, limitations, and future development directions of current imaging techniques.

The utility of Multi-Detector Computed Tomography criteria after neoadjuvant therapy in Borderline Resectable Pancreatic cancer: Prospective, bi-institutional study

OBJECTIVES

To evaluate the utility of MDCT criteria for the determination of resectability and tumor response in borderline resectable pancreatic cancer (BRPC) following neoadjuvant therapy (NAT).

METHODS

This prospective study includes 90 consecutive BRPC patients who underwent surgery following NAT. Two radiologists assessed baseline and pre-surgical CTs for (largest tumor axis, size, attenuation, and vascular criteria). Logistic regression was used to determine which CT criteria independently associated with R0 resection and pathologic major response (pMR). Median survival and overall survival (OS) were calculated.

RESULTS

Seventy-three/90 (81.1 %) patients had R0 resection, and 11/90 (12.2 %) had pMR. After NAT, there were significant interval changes in the largest tumor axis, size, attenuation, and venous burden index (VBI) (P < 0.02). On the multivariable analysis, regression of the VBI and low VBI at the pre-surgical CT were independently associated with an increased likelihood of R0 resection (OR 1.82; 95 % CI 1.44-5.33) (OR 1.91; 95 % CI 1.83-6.14). The assessment of VBI at the pre-surgical CT showed moderate reproducibility (k-value, 0.56 - 0.60). On the multivariable analysis, partial response (PR) was found to be independently associated with an increased likelihood of pMR (OR 1.71; 95 % CI 1.31-3.45). The median survival was longer in patients who had R0 (P = 0.01). The overall survival was longer in patients who had pMR compared to those who did not (P = 0.02).

CONCLUSION

Surgical exploration could be indicated in patients who had regression of the VBI and low VBI at the pre-surgical CT. PR response is associated with pMR.

Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI

Simple Summary

Part I is an overview aimed to investigate some technical principles and the main fields of radiomic application in gastrointestinal oncologic imaging (CT and MRI) with a focus on diagnosis, prediction prognosis, and assessment of response to therapy in gastrointestinal cancers, describing mostly the results for each pre-eminent tumor. In particular, this paper provides a general description of the main radiomic drawbacks and future challenges, which limit radiomic application in clinical setting as routine. Further investigations need to standardize and validate the Radiomics as a helpful tool in management of oncologic patients. In that context, Radiomics has been playing a relevant role and could be considered as a future imaging landscape.

Abstract

Radiomics has been playing a pivotal role in oncological translational imaging, particularly in cancer diagnosis, prediction prognosis, and therapy response assessment. Recently, promising results were achieved in management of cancer patients by extracting mineable high-dimensional data from medical images, supporting clinicians in decision-making process in the new era of target therapy and personalized medicine. Radiomics could provide quantitative data, extracted from medical images, that could reflect microenvironmental tumor heterogeneity, which might be a useful information for treatment tailoring. Thus, it could be helpful to overcome the main limitations of traditional tumor biopsy, often affected by bias in tumor sampling, lack of repeatability and possible procedure complications. This quantitative approach has been widely investigated as a non-invasive and an objective imaging biomarker in cancer patients; however, it is not applied as a clinical routine due to several limitations related to lack of standardization and validation of images acquisition protocols, features segmentation, extraction, processing, and data analysis. This field is in continuous evolution in each type of cancer, and results support the idea that in the future Radiomics might be a reliable application in oncologic imaging. The first part of this review aimed to describe some radiomic technical principles and clinical applications to gastrointestinal oncologic imaging (CT and MRI) with a focus on diagnosis, prediction prognosis, and assessment of response to therapy.

Noninvasive prediction of residual disease for advanced high-grade serous ovarian carcinoma by MRI-based radiomic-clinical nomogram

OBJECTIVES

To develop a preoperative MRI-based radiomic-clinical nomogram for prediction of residual disease (RD) in patients with advanced high-grade serous ovarian carcinoma (HGSOC).

METHODS

In total, 217 patients with advanced HGSOC were enrolled from January 2014 to June 2019 and randomly divided into a training set (n = 160) and a validation set (n = 57). Finally, 841 radiomic features were extracted from each tumor on T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequence, respectively. We used two fusion methods, the maximal volume of interest (MV) and the maximal feature value (MF), to fuse the radiomic features of bilateral tumors, so that patients with bilateral tumors have the same kind of radiomic features as patients with unilateral tumors. The radiomic signatures were constructed by using mRMR method and LASSO classifier. Multivariable logistic regression analysis was used to develop a radiomic-clinical nomogram incorporating radiomic signature and conventional clinico-radiological features. The performance of the nomogram was evaluated on the validation set.

RESULTS

In total, 342 tumors from 217 patients were analyzed in this study. The MF-based radiomic signature showed significantly better prediction performance than the MV-based radiomic signature (AUC = 0.744 vs. 0.650, p = 0.047). By incorporating clinico-radiological features and MF-based radiomic signature, radiomic-clinical nomogram showed favorable prediction ability with an AUC of 0.803 in the validation set, which was significantly higher than that of clinico-radiological signature and MF-based radiomic signature (AUC = 0.623, 0.744, respectively).

CONCLUSIONS

The proposed MRI-based radiomic-clinical nomogram provides a promising way to noninvasively predict the RD status.

KEY POINTS

• MRI-based radiomic-clinical nomogram is feasible to noninvasively predict residual disease in patients with advanced HGSOC. • The radiomic signature based on MF showed significantly better prediction performance than that based on MV. • The radiomic-clinical nomogram showed a favorable prediction ability with an AUC of 0.803.

[Radiologic Evaluation for Resectability of Pancreatic Adenocarcinoma]

Imaging studies play an important role in the detection, diagnosis, assessment of resectability, staging, and determination of patient-tailored treatment options for pancreatic adenocarcinoma. Recently, for patients diagnosed with borderline resectable or locally advanced pancreatic cancers, it is recommended to consider curative-intent surgery following neoadjuvant or palliative therapy, if possible. This review covers how to interpret imaging tests and what to consider when assessing resectability, diagnosing distant metastasis, and re-assessing the resectability of pancreatic cancer after neoadjuvant or palliative therapy.

CT texture analysis for the presurgical prediction of superior mesenteric-portal vein invasion in pancreatic ductal adenocarcinoma: comparison with CT imaging features

AIM

To investigate the value of computed tomography (CT) texture analysis (TA) and imaging features for evaluating suspected surgical superior mesenteric-portal vein (SMPV) invasion in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

Fifty-four patients with PDAC in the pancreatic head or uncinate process with suspected SMPV involvement were analysed retrospectively. SMPV invasion status was identified by surgical exploration. For each patient, 396 texture features were extracted on pretreatment CT. Non-parametric tests and minimum redundancy maximum relevance were used for feature selection. A CTTA model was constructed using multivariate logistic regression, and the area under the receiver operating characteristic (AUROC) of the model was calculated. Two reviewers evaluated qualitative imaging features independently for SMPV invasion and interobserver agreement was investigated. The diagnostic performance of the imaging features and the CTTA model for SMPV invasion was compared using the McNemar test.

RESULTS

Of the 54 patients with PDAC, SMPV invasion was detected in 23 (42.6%). The CTTA model yielded an AUROC of 0.88 (95% confidence interval, 0.76-0.97) and achieved significantly higher specificity (0.90) than the two reviewers (0.61 and 0.65; p=0.027 and 0.043). Interobserver agreement was moderate between the two reviewers (κ = 0.517). Of the 13 cases with disagreement between the two reviewers, 11 cases were predicted accurately by the CTTA model.

CONCLUSION

CTTA can predict suspected SMPV invasion in PDAC and may be a beneficial addition for qualitative imaging evaluation.

Preoperative CT staging of borderline pancreatic cancer patients after neoadjuvant treatment: accuracy in the prediction of vascular invasion and resectability

OBJECTIVES

To assess the utility of MDCT tumor-vascular interface criteria for predicting vascular invasion and resectability in borderline pancreatic cancer (BRPC) patients after neoadjuvant therapy (NAT).

METHODS

This prospective study included 90 patients with BRPC who finished NAT, showed no progression in preoperative CTs and underwent surgery. Two radiologists independently assessed preoperative vessel-tumor interface criteria. The area under the ROC curve (AUC) was used to evaluate the diagnostic performance for predicting vascular invasions and resectability using surgical and pathological results as the gold standard. Inter-reader agreement was assessed using the κ coefficient.

RESULTS

Pathologic vascular invasion was confirmed in 47 (54.7%) veins and 14 (16.3%) arteries. R0 resection was achieved in (82.6%71/86) pancreatic resection. Using criteria of circumferential interface ≥ 180 degrees with contour deformity ≥ grade 3 and/or length of tumor contact > 2 cm to predict vascular invasion, the AUCs for the two readers were 0.85-0.88 for arterial invasion and 0.92-0.87 for venous invasion. Using criteria of circumferential interface ≤ 180° with contour deformity ≤ grade 2 and/or length of tumor contact < 2 cm to predict R0 resection, the AUCs was 0.85-0.86 for the two readers. The overall inter-reader agreement was good (κ = 0.75-0.80). The κ values for venous invasion, arterial invasion and R0 resection were 0.76, 0.78, and 0.80.

CONCLUSION

Tumor-vessel criteria demonstrated good diagnostic performance and reproducibility in the prediction of vascular invasion after NAT in BRPC. These criteria could be helpful in the prediction of R0 resection in cases with only venous involvement.

CT and MRI of pancreatic tumors: an update in the era of radiomics

Radiomics is a relatively new approach for image analysis. As a part of radiomics, texture analysis, which consists in extracting a great amount of quantitative data from original images, can be used to identify specific features that can help determining the actual nature of a pancreatic lesion and providing other information such as resectability, tumor grade, tumor response to neoadjuvant therapy or survival after surgery. In this review, the basic of radiomics, recent developments and the results of texture analysis using computed tomography and magnetic resonance imaging in the field of pancreatic tumors are presented. Future applications of radiomics, such as artificial intelligence, are discussed.

Radiomics-Assisted Presurgical Prediction for Surgical Portal Vein-Superior Mesenteric Vein Invasion in Pancreatic Ductal Adenocarcinoma

Objectives

To develop a radiomics signature for predicting surgical portal vein-superior mesenteric vein (PV-SMV) in patients with pancreatic ductal adenocarcinoma (PDAC) and measure the effect of providing the predictions of radiomics signature to radiologists with different diagnostic experiences during imaging interpretation.

Methods

Between February 2008 and June 2020, 146 patients with PDAC in pancreatic head or uncinate process from two institutions were retrospectively included and randomly split into a training (n = 88) and a validation (n =58) cohort. Intraoperative vascular exploration findings were used to identify surgical PV-SMV invasion. Radiomics features were extracted from the portal venous phase CT images. Radiomics signature was built with a linear elastic-net regression model. Area under receiver operating characteristic curve (AUC) of the radiomics signature was calculated. A senior and a junior radiologist independently review CT scans and made the diagnosis for PV-SMV invasion both with and without radiomics score (Radscore) assistance. A 2-sided Pearson's chi-squared test was conducted to evaluate whether there was a difference in sensitivity, specificity, and accuracy between the radiomics signature and the unassisted radiologists. To assess the incremental value of providing Radscore predictions to the radiologists, we compared the performance between unassisted evaluation and Radscore-assisted evaluation by using the McNemar test.

Results

Numbers of patients identified as presence of surgical PV-SMV invasion were 33 (37.5%) and 19 (32.8%) in the training and validation cohort, respectively. The radiomics signature achieved an AUC of 0.848 (95% confidence interval, 0.724-0.971) in the validation cohort and had a comparable sensitivity, specificity, and accuracy as the senior radiologist in predicting PV-SMV invasion (all p-values > 0.05). Providing predictions of radiomics signature increased both radiologists' sensitivity in identifying PV-SMV invasion, while only the increase of the junior radiologist was significant (63.2 vs 89.5%, p-value = 0.025) instead of the senior radiologist (73.7 vs 89.5%, p-value = 0.08). Both radiologists' accuracy had no significant increase when provided radiomics signature assistance (both p-values > 0.05).

Conclusions

The radiomics signature can predict surgical PV-SMV invasion in patients with PDAC and may have incremental value to the diagnostic performance of radiologists during imaging interpretation.

Validation of Prognostic Radiomic Features From Resectable Pancreatic Ductal Adenocarcinoma in Patients With Advanced Disease Undergoing Chemotherapy

BACKGROUND

Radiomic features in pancreatic ductal adenocarcinoma (PDAC) often lack validation in independent test sets or are limited to early or late stage disease. Given the lethal nature of PDAC it is possible that there are similarities in radiomic features of both early and advanced disease reflective of aggressive biology.

PURPOSE

To assess the performance of prognostic radiomic features previously published in patients with resectable PDAC in a test set of patients with unresectable PDAC undergoing chemotherapy.

METHODS

The pre-treatment CT of 108 patients enrolled in a prospective chemotherapy trial were used as a test cohort for 2 previously published prognostic radiomic features in resectable PDAC (Sum Entropy and Cluster Tendency with square-root filter[Sqrt]). We assessed the performance of these 2 radiomic features for the prediction of overall survival (OS) and time to progression (TTP) using Cox proportional-hazard models.

RESULTS

Sqrt Cluster Tendency was significantly associated with outcome with a hazard ratio (HR) of 1.27(for primary pancreatic tumor plus local nodes), (Confidence Interval(CI):1.01 -1.6, P-value = 0.039) for OS and a HR of 1.25(CI:1.00 -1.55, P-value = 0.047) for TTP. Sum entropy was not associated with outcomes. Sqrt Cluster Tendency remained significant in multivariate analysis.

CONCLUSION

The CT radiomic feature Sqrt Cluster Tendency, previously demonstrated to be prognostic in resectable PDAC, remained a significant prognostic factor for OS and TTP in a test set of unresectable PDAC patients. This radiomic feature warrants further investigation to understand its biologic correlates and CT applicability in PDAC patients.

Assessment of malignant potential in intraductal papillary mucinous neoplasms of the pancreas using MR findings and texture analysis

OBJECTIVES

To investigate the utility of MR findings and texture analysis for predicting the malignant potential of pancreatic intraductal papillary mucinous neoplasms (IPMNs).

METHODS

Two hundred forty-eight patients with surgically confirmed IPMNs (106 malignant [invasive carcinoma/high-grade dysplasia] and 142 benign [low/intermediate-grade dysplasia]) and who underwent magnetic resonance imaging (MRI) with MR cholangiopancreatography (MRCP) were included. Two reviewers independently analyzed MR findings as proposed by the 2017 international consensus guidelines. Texture analysis of MRCP was also performed. A multivariate logistic regression analysis was used to identify predictors for malignant IPMNs. Diagnostic performance was also analyzed using receiver operating curve analysis.

RESULTS

Among MR findings, enhancing mural nodule size ≥ 5 mm, main pancreatic duct (MPD) ≥ 10 mm or MPD of 5 to 9 mm, and abrupt change of MPD were significant predictors for malignant IPMNs (p < 0.05). Among texture variables, significant predictors were effective diameter, surface area, sphericity, compactness, entropy, and gray-level co-occurrence matrix entropy (p < 0.05). At multivariate analysis, enhancing mural nodule ≥ 5 mm (odds ratios (ORs), 6.697 and 6.968, for reviewers 1 and 2, respectively), MPD ≥ 10 mm or MPD of 5 to 9 mm (ORs, 4.098 and 4.215, and 2.517 and 3.055, respectively), larger entropy (ORs, 1.485 and 1.515), and smaller compactness (ORs, 0.981 and 0.977) were significant predictors for malignant IPMNs (p < 0.05). When adding texture variable to MR findings, diagnostic performance for predicting malignant IPMNs improved from 0.80 and 0.78 to 0.85 and 0.85 in both reviewers (p < 0.05), respectively.

CONCLUSIONS

MRCP-derived texture features are useful for predicting malignant IPMNs, and the addition of texture analysis to MR features may improve diagnostic performance for predicting malignant IPMNs.

KEY POINTS

• Among the MR imaging findings, an enhancing mural nodule size ≥ 5 mm and dilated main pancreatic ducts are independent predictors for malignant IPMNs. • Greater entropy and smaller compactness on MR texture analysis are independent predictors for malignant IPMNs. • The addition of MR texture analysis improved the diagnostic performance for predicting malignant IPMNs from 0.80 and 0.78 to 0.85 and 0.85, respectively.

CT in the prediction of margin-negative resection in pancreatic cancer following neoadjuvant treatment: a systematic review and meta-analysis

OBJECTIVES

We aimed to systematically evaluate the diagnostic accuracy of CT-determined resectability following neoadjuvant treatment for predicting margin-negative resection (R0 resection) in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

Original studies with sufficient details to obtain the sensitivity and specificity of CT-determined resectability following neoadjuvant treatment, with a reference on the pathological margin status, were identified in PubMed, EMBASE, and Cochrane databases until February 24, 2020. The identified studies were divided into two groups based on the criteria of R0 resectable tumor (ordinary criterion: resectable PDAC alone; extended criterion: resectable and borderline resectable PDAC). The meta-analytic summary of the sensitivity and specificity for each criterion was estimated separately using a bivariate random-effect model. Summary results of the two criteria were compared using a joint-model bivariate meta-regression.

RESULTS

Of 739 studies initially searched, 6 studies (6 with ordinary criterion and 5 with extended criterion) were included for analysis. The meta-analytic summary of sensitivity and specificity was 45% (95% confidence interval [CI], 19-73%; I² = 88.3%) and 85% (95% CI, 65-94%; I² = 60.5%) for the ordinary criterion, and 81% (95% CI, 71-87%; I² = 0.0%) and 42% (95% CI, 28-57%; I² = 6.2%) for the extended criterion, respectively. The diagnostic accuracy significantly differed between the two criteria (p = 0.02).

CONCLUSIONS

For determining resectability on CT, the ordinary criterion might be highly specific but insensitive for predicting R0 resection, whereas the extended criterion increased sensitivity but would decrease specificity. Further investigations using quantitative parameters may improve the identification of R0 resection.

KEY POINTS

• CT-determined resectability of PDAC after neoadjuvant treatment using the ordinary criterion shows low sensitivity and high specificity in predicting R0 resection. • With the extended criterion, CT-determined resectability shows higher sensitivity but lower specificity than with the ordinary criterion. • CT-determined resectability with both criteria achieved suboptimal diagnostic performances, suggesting that care should be taken while selecting surgical candidates and when determining the surgical extent after neoadjuvant treatment in patients with PDAC.

Borderline Resectable and Locally Advanced Pancreatic Cancer: FDG PET/MRI and CT Tumor Metrics for Assessment of Pathologic Response to Neoadjuvant Therapy and Prediction of Survival

BACKGROUND. Imaging biomarkers of response to neoadjuvant therapy (NAT) for pancreatic ductal adenocarcinoma (PDA) are needed to optimize treatment decisions and long-term outcomes. OBJECTIVE. The purpose of this study was to investigate metrics from PET/MRI and CT to assess pathologic response of PDA to NAT and to predict overall survival (OS). METHODS. This retrospective study included 44 patients with ¹⁸F-FDG-avid borderline resectable or locally advanced PDA on pretreatment PET/MRI who also underwent post-NAT PET/MRI before surgery between August 2016 and February 2019. Carbohydrate antigen 19-9 (CA 19-9) level, metabolic metrics from PET/MRI, and morphologic metrics from CT (n = 34) were compared between pathologic responders (College of American Pathologists scores 0 and 1) and nonresponders (scores 2 and 3). AUCs were measured for metrics significantly associated with pathologic response. Relation to OS was evaluated with Cox proportional hazards models. RESULTS. Among 44 patients (22 men, 22 women; mean age, 62 ± 11.6 years), 19 (43%) were responders, and 25 (57%) were nonresponders. Median OS was 24 months (range, 6-42 months). Before treatment, responders and nonresponders did not differ in CA 19-9 level, metabolic metrics, or CT metrics (p > .05). After treatment, responders and nonresponders differed in complete metabolic response (CMR) (responders, 89% [17/19]; nonresponders, 40% [10/25]; p = .04], mean change in SUV_max (ΔSUV_max; responders, -70% ± 13%; nonresponders, -37% ± 42%; p < .001), mean change in SUV_max corrected to serum glucose level (ΔSUV_gluc) (responders, -74% ± 12%; nonresponders, -30% ± 58%; p < .001), RECIST response on CT (responders, 93% [13/14]; nonresponders, 50% [10/20]; p = .02)], and mean change in tumor volume on CT (ΔTvol) (responders, -85% ± 21%; nonresponders, 57% ± 400%; p < .001). The AUC of CMR for pathologic response was 0.75; ΔSUV_max, 0.83; ΔSUV_gluc, 0.87; RECIST, 0.71; and ΔTvol 0.86. The AUCs of bivariable PET/MRI and CT models were 0.83 (CMR and ΔSUV_max), 0.87 (CMR and ΔSUV_gluc), and 0.87 (RECIST and ΔTvol). OS was associated with CMR (p = .03), ΔSUV_max (p = .003), ΔSUV_gluc (p = .003), and RECIST (p = .046). CONCLUSION. Unlike CA 19-9 level, changes in metabolic metrics from PET/MRI and morphologic metrics from CT after NAT were associated with pathologic response and OS in patients with PDA, warranting prospective validation. CLINICAL IMPACT. Imaging metrics associated with pathologic response and OS in PDA could help guide clinical management and outcomes for patients with PDA who undergo emergency therapeutic interventions.

Pancreatic Cancer Imaging: A New Look at an Old Problem

Computed tomography is the most commonly used imaging modality to detect and stage pancreatic cancer. Previous advances in pancreatic cancer imaging have focused on optimizing image acquisition parameters and reporting standards. However, current state-of-the-art imaging approaches still misdiagnose some potentially curable pancreatic cancers and do not provide prognostic information or inform optimal management strategies beyond stage. Several recent developments in pancreatic cancer imaging, including artificial intelligence and advanced visualization techniques, are rapidly changing the field. The purpose of this article is to review how these recent advances have the potential to revolutionize pancreatic cancer imaging.

CT-determined resectability of borderline resectable and unresectable pancreatic adenocarcinoma following FOLFIRINOX therapy

OBJECTIVES

We aimed to assess the ability of CT-determined resectability, as defined by a recent version of NCCN criteria, and associated CT findings to predict margin-negative (R0) resection in patients with PDAC after neoadjuvant FOLFIRINOX chemotherapy.

METHODS

Sixty-four patients (36 men and 28 women; mean age, 58.8 years) with borderline resectable or unresectable PDAC who received neoadjuvant FOLFIRINOX were evaluated retrospectively. CT findings were independently assessed by two abdominal radiologists according to NCCN criteria (version 3. 2019). Tumor resectability was classified as resectable, borderline resectable, or unresectable, and change in resectability was classified as regression, stability, or progression. The associations of R0 resection rate with CT-determined resectability and change in resectability categories were evaluated, as were the sensitivity and specificity of NCCN criteria for R0 resection. Factors associated with R0 resection were identified by logistic regression analysis.

RESULTS

R0 resection rate did not differ significantly among the resectable, borderline resectable, or unresectable PDAC (67-73%, p = 0.95) or among PDAC with regression, stability, or progression (56-77%, p = 0.39). The sensitivity and specificity for R0 resection were 67% and 37%, respectively, for resectability (resectable/borderline vs. unresectable) and 80% and 21%, respectively, for changes in resectability (regression/stable vs. progression). Low-contrast enhancement of soft tissue contacting artery (≤ 46.4 HU) was independently associated with R0 resection (p = 0.01).

CONCLUSION

CT-determined resectability after neoadjuvant FOLFIRINOX chemotherapy was relatively insensitive and non-specific for predicting R0 resection. Low-contrast enhancement of soft tissue contacting artery may increase the ability of CT to predict R0 resection.

KEY POINTS

• Margin-negative resection rate of pancreatic cancer following FOLFIRINOX therapy did not differ among each resectability (67-73%, p = 0.95) based on NCCN criteria or changes in resectability categories (56-77%, p = 0.39). • The sensitivity and specificity for margin-negative resection were 67% and 37% for resectability (resectable/borderline vs. unresectable) and 80% and 21% for changes in resectability (regression/stable vs. progression). • Low-contrast enhancement of soft tissue contacting artery (≤ 46.4 HU) was independently associated with margin-negative resection (p = 0.01).

Preoperative prediction of perineural invasion and KRAS mutation in colon cancer using machine learning

PURPOSE

Preoperative prediction of perineural invasion (PNI) and Kirsten RAS (KRAS) mutation in colon cancer is critical for treatment planning and patient management. We developed machine learning models for diagnosis of PNI and KRAS mutation in colon cancer patients by interpreting preoperative CT.

METHODS

This retrospective study included 207 patients who received surgical resection in our institution. The underlying tumor characteristics were described by analyzing CT image texture quantitatively. The key radiomics features were determined with similarity analysis followed by RELIEFF method among 306 CT imaging features. Eight kernel-based support vector machines classifiers were constructed using individual (II, III, or IV) or multi-stage (II + III + IV) patient cohorts for predicting PNI and KRAS mutation. The model performances were evaluated using accuracy, receiver operating curve, and decision curve analyses.

RESULTS

Multi-stage classifiers obtained AUC of 0.793 and 0.862 for detecting PNI and KRAS mutation for test cohort. Moreover, individual-stage classifiers demonstrated significantly improved diagnostic performance at all stages (II_AUC: [0.86; 0.99], III_AUC: [0.99; 0.99], and IV_AUC: [1.00; 1.00], respectively, for PNI and KRAS mutation in test cohort). Besides, stage II tumor is better described with coarse texture features while more detailed features are required for better characterization of advanced-stage tumors (III and IV) for diagnoses of PNI or KRAS mutation.

CONCLUSION

Machine learning models developed using preoperative CT data can predict PNI and KRAS mutation in colon cancer patients with satisfactory performance. Individual-stage models better-characterized the relationship between CT features and PNI or KRAS mutation than multi-stage models and demonstrated good prediction scores.

Predictive Value of the Texture Analysis of Enhanced Computed Tomographic Images for Preoperative Pancreatic Carcinoma Differentiation

Purpose

To assess the utility of texture analysis for predicting the pathological degree of differentiation of pancreatic carcinoma (PC).

Methods

Eighty-three patients with PC who went through postoperative pathology diagnose and CT examination were selected at Anhui Provincial Hospital. Among them, 34 cases were moderately differentiated, 13 cases were poorly differentiated, and 36 cases were moderately poorly differentiated. The images in the arterial and venous phase (VP) with the lesions at their largest cross section were selected to manually outline the region of interest (ROI) to delineate lesions using open-source software. A total of 396 features were extracted from the ROI using AK software. Spearman correlation analysis and random forest selection by filter (rfSBF) in the caret package of R studio were used to select the discriminating features. The receiver operating characteristic ROC analysis was used to evaluate their discriminative performance.

Results

Twelve and six features were selected in the arterial and VPs, respectively. The areas under the ROC curve (AUC) in the arterial phase (AP) for diagnosing poorly differentiated, moderately differentiated and moderate-poorly differentiated cases were 0.80, 1, and 0.80 in the training group and 0.77, 1, and 0.77 in the test group; in the VP, the values were 0.81, 1, and 0.82 in the training group and 0.74, 1, and 0.74 in the test group.

Conclusion

Texture analysis based on contrast-enhanced CT images can be used as an adjunct for the preoperative assessment of the pathological degrees of differentiation of PC.

MRI Texture Analysis for Differentiating Nonfunctional Pancreatic Neuroendocrine Neoplasms From Solid Pseudopapillary Neoplasms of the Pancreas

RATIONALE AND OBJECTIVES

To evaluate the value of texture analysis on preoperative magnetic resonance imaging (MRI) for identifying nonfunctional pancreatic neuroendocrine neoplasms (NF-PNENs) and solid pseudopapillary neoplasms (SPNs).

MATERIALS AND METHODS

This retrospective study included 119 patients who underwent MRI, including T2-weighted imaging with fat-suppression, diffusion-weighted imaging (DWI), apparent diffusion coefficient, precontrast T1-weighted imaging with fat-suppression (T₁WI+fs), and dynamic contrast-enhanced (DCE)-T₁WI+fs. Raw data analysis, principal component analysis, linear discriminant analysis, and nonlinear discriminant analysis (NDA) were used to classify NF-PNENs and SPNs. The results are reported as misclassification rates. The images were simultaneously evaluated by an experienced senior radiologist without knowledge of the pathological results. The misclassification rate of the radiologist was compared to the MaZda (texture analysis software) results. Neural network classifier testing was used for validation. In addition, 30 textures for each MRI sequence were investigated.

RESULTS

The misclassification rate of NDA was lower than that of other analyses. In NDA, DWI obtained the lowest value of 7.92%, but there was no significant difference among the sequences. The misclassification rate of the radiologist (34.65%) was significantly higher than that of NDA for all sequences. The validation results were good in the arterial phase and delayed phase. In the training set, entropy and sum entropy were optimal texture features on DWI and precontrast T₁WI+fs, while the mean and percentile seemed to be the more discriminative features on DCE-T₁WI+fs.

CONCLUSION

Texture analysis can sensitively distinguish between NF-PNENs and SPNs on MRI, and percentile and mean of DCE-T₁WI+fs images were informative for differentiation of neoplasms.

Performance of CT-based radiomics in diagnosis of superior mesenteric vein resection margin in patients with pancreatic head cancer

OBJECTIVES

To accurately identify the relationship between a portal radiomics score (rad-score) and pathologic superior mesenteric vein (SMV) resection margin and to evaluate the diagnostic performance in patients with pancreatic head cancer.

MATERIALS AND METHODS

A total of 181 patients with postoperatively and pathologically confirmed pancreatic head cancer who underwent multislice computed tomography within one month of resection between January 2016 and December 2018 were retrospectively investigated. For each patient, 1029 radiomics features of the portal phase were extracted, which were reduced using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. Multivariate logistic regression models were used to analyze the association between the portal rad-score and SMV resection margin.

RESULTS

Patients with negative (R0) and positive (R1) margins accounted for 70.17% (127) and 29.83% (54) of the cohort, respectively. The rad-score was significantly associated with the SMV resection margin status (p < 0.05). Multivariate analyses confirmed a significant and independent association between the portal rad-score and SMV resection margin (OR 4.62; 95% CI 2.19-9.76; p < 0.0001). The portal rad-score had high accuracy (area under the curve = 0.750). The best cut point based on maximizing the sum of sensitivity and specificity was - 0.741 (sensitivity = 64.8%; specificity = 74.0%; accuracy = 71.3%). Decision curve analysis indicated the clinical usefulness of radiomics score.

CONCLUSIONS

The portal rad-score is significantly associated with the pathologic SMV resection margin, and it can accurately and noninvasively predict the SMV resection margin in patients with pancreatic cancer.

MRI radiomics for early prediction of response to vaccine therapy in a transgenic mouse model of pancreatic ductal adenocarcinoma

BACKGROUND

There is a lack of well-established clinical tools for predicting dendritic cell (DC) vaccination response of pancreatic ductal adenocarcinoma (PDAC). DC vaccine treatment efficiency was demonstrated using histological analysis in pre-clinical studies; however, its usage was limited due to invasiveness. In this study, we aimed to investigate the potential of MRI texture features for detection of early immunotherapeutic response as well as overall survival (OS) of PDAC subjects following dendritic cell (DC) vaccine treatment in LSL-KrasG12D;LSL-Trp53R172H;Pdx-1-Cre (KPC) transgenic mouse model of pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

KPC mice were treated with DC vaccines, and tumor growth was dynamically monitored. A total of a hundred and fifty-two image features of T2-weighted MRI images were analyzed using a kernel-based support vector machine model to detect treatment effects following the first and third weeks of the treatment. Moreover, univariate analysis was performed to describe the association between MRI texture and survival of KPC mice as well as histological tumor biomarkers.

RESULTS

OS for mice in the treatment group was 54.8 ± 22.54 days while the control group had 35.39 ± 17.17 days. A subset of three MRI features distinguished treatment effects starting from the first week with increasing accuracy throughout the treatment (75% to 94%). Besides, we observed that short-run emphasis of approximate wavelet coefficients had a positive correlation with the survival of the KPC mice (r = 0.78, p < 0.001). Additionally, tissue-specific MRI texture features showed positive association with fibrosis percentage (r = 0.84, p < 0.002), CK19 positive percentage (r = - 0.97, p < 0.001), and Ki67 positive cells (r = 0.81, p < 0.02) as histological disease biomarkers.

CONCLUSION

Our results demonstrate that MRI texture features can be used as imaging biomarkers for early detection of therapeutic response following DC vaccination in the KPC mouse model of PDAC. Besides, MRI texture can be utilized to characterize tumor microenvironment reflected with histology analysis.

Pancreatic ductal adenocarcinoma: a radiomics nomogram outperforms clinical model and TNM staging for survival estimation after curative resection

OBJECTIVES

To identify a CT-based radiomics nomogram for survival prediction in patients with resected pancreatic ductal adenocarcinoma (PDAC).

METHODS

A total of 220 patients (training cohort n = 147; validation cohort n = 73) with PDAC were enrolled. A total of 300 radiomics features were extracted from CT images. And the least absolute shrinkage and selection operator algorithm were applied to select features and develop a radiomics score (Rad-score). The radiomics nomogram was constructed by multivariate regression analysis. Nomogram discrimination, calibration, and clinical usefulness were evaluated. The association of the Rad-score and recurrence pattern in PDAC was evaluated.

RESULTS

The Rad-score was significantly associated with PDAC patient's disease-free survival (DFS) and overall survival (OS) (both p < 0.001 in two cohorts). Incorporating the Rad-score into the radiomics nomogram resulted in better performance of the survival prediction than that of the clinical model and TNM staging system. In addition, the radiomics nomogram exhibited good discrimination, calibration, and clinical usefulness in both the training and validation cohorts. There was no association between the Rad-score and recurrence pattern.

CONCLUSIONS

The radiomics nomogram integrating the Rad-score and clinical data provided better prognostic prediction in resected PDAC patients, which may hold great potential for guiding personalized care for these patients. The Rad-score was not a predictor of the recurrence pattern in resected PDAC patients.

KEY POINTS

• The Rad-score developed by CT radiomics features was significantly associated with PDAC patients' prognosis. • The radiomics nomogram integrating the Rad-score and clinical data has value to permit non-invasive, low-cost, and personalized evaluation of prognosis in PDAC patients. • The radiomics nomogram outperformed clinical model and the TNM staging system in terms of survival estimation.

Heterogeneity of metastatic gastrointestinal stromal tumor on texture analysis: DWI texture as potential biomarker of overall survival

PURPOSE

To determine if texture features of diffusion weighted imaging (DWI) on MRI of metastatic gastrointestinal stromal tumor (mGIST) have correlation with overall survival (OS).

METHOD

Fifty-one GIST patients with metastatic lesions who received imatinib targeted therapy were included. Texture features of the largest metastatic lesion were analyzed using inhouse software. Three types of texture features were assessed: fractal features, gray-level co-occurrence matrix (GLCM) features, and gray-level run-length matrix (GLRLM) features. The features were extracted from the regions of interest (ROIs) on T2-weighted imaging (T2WI), DWI and apparent diffusion coefficient (ADC) maps. Histogram analysis was performed on ADC maps. Patients were followed up until death. Kaplan-Meier analysis was performed to determine the correlation of texture features with OS. The curves of the high- and low-risk groups were compared using log-rank test. The prognostic efficacy of the predictors was assessed by calculating the concordance probability.

RESULTS

The median survival time was 43.5 months (range, 3.97-120.90 m). Four DWI and three ADC texture features showed significant correlation with OS on univariate analysis (p < 0.05). DWI_L_GLCM_maximum_probability [hazard ratio (HR): 2.062 (1.357-3.131)], ADC_H_GLRLM_mean [HR: 2.174 (1.457-3.244)], and ADC_O_GLCM_cluster_shade [HR: 1.882 (1.324-2.674)] were identified as representative prognostic indicators. The optimum threshold levels for these three features were 1.19×100, 1.71×10 and 2.19×0.1, respectively. Neither histogram analysis values nor fractal features revealed significant correlation with survival status (p > 0.05).

CONCLUSIONS

Texture features of the mGIST on DWI exhibited correlation with overall survival. High-grade heterogeneity was associated with poor prognosis.