Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features

Differentiation between renal cell carcinoma metastases to the pancreas and pancreatic neuroendocrine tumors in patients with renal cell carcinoma on CT or MRI

PURPOSE

To determine whether renal cell carcinoma metastases (RCC-Mets) to the pancreas can be differentiated from pancreatic neuroendocrine tumors (PNETs) in patients with RCC on CT or MRI at presentation.

METHODS

This retrospective study included patients with biopsy-proven RCC-Mets (n = 102) or PNETs (n = 32) at diagnosis or after nephrectomy for RCC. Inter-observer agreement (Cohen kappa) was assessed in 95 patients with independent reads by two radiologists, with discrepancies resolved by consensus for final analysis. The remaining 39 cases underwent consensus reads by two different radiologists for final analysis. The CT/MRI images were reviewed for number, size, regional distribution, parenchymal location (exophytic or intrapancreatic), contrast-enhancement, and enhancement pattern of pancreatic lesions in the available phases. Statistical tests were conducted using two sample t-tests and Pearson's chi-squared test for numeric and categorical variables respectively.

RESULTS

The study group comprised of 134 patients (90 males) with 265 lesions (229 RCC-Mets and 36 PNETs). Patients with PNETs were significantly younger (62 ± 12 years vs. 67 ± 9 years, p = 0.013). Inter-observer agreement for CT/MRI features was excellent across multiple imaging variables (k = 0.86-1.00). Most PNETs were single lesions (88 vs. 63%, p = 0.008), smaller in size (14 mm vs. 23 mm, p = 0.042), more common in the body and tail (81 vs. 57%, p = 0.01), showed homogeneous contrast enhancement (64-79% vs. 39-49%, p < 0.01-0.03), less T1-hypointense (80 vs. 99%, p = 0.002) and more DWI hyperintense (71 vs. 58%, p < 0.001) compared to RCC-Mets.

CONCLUSION

PNETs are typically single, occur in distal pancreas, and enhance homogeneously compared to RCC-Mets which are often multiple, occur in the proximal pancreas, and enhance heterogeneously.

Surgery for an Uncommon Pathology: Pancreatic Metastases from Renal Cell Carcinoma-Indications, Type of Pancreatectomy, and Outcomes in a Single-Center Experience

Background and Objectives: The role of surgery in pancreatic metastases of renal cell carcinoma (PM_RCC) is highly controversial, particularly in the context of modern systemic therapies and the conflicting results of studies published so far. This study aims to explore a single surgical center experience (including mainly pancreatic resections) regarding the indications, the type of pancreatectomies, and early and long-term outcomes for PM_RCC. Materials and Methods: The data of all patients with surgery for PM_RCC (from 1 January 2002 to 31 December 2023) were retrospectively assessed, and potential predictors of survival were explored. Results: 20 patients underwent surgery for PM_RCC (pancreatectomies-95%). Metachronous PM_RCC was 90%, with a median interval between the initial nephrectomy and PM_RCC occurrence of 104 months. For elective pancreatectomies, the overall and severe morbidity and mortality rates were 24%, 12%, and 0%, respectively; 32% of patients underwent non-standardized pancreatic resections. The median survival of patients with negative resection margins was 128 months after pancreatectomies, with an 82% 5-year survival rate. Left kidney RCC and the body/tail PM_RCC were favorable prognostic factors for the overall survival after pancreatectomies for PM_RCC. Body/tail, asymptomatic PM_RCC, and an interval after initial nephrectomy > 2 were favorable prognostic factors for the overall survival after initial nephrectomy for RCC. Conclusions: Pancreatectomies for PM_RCC can achieve long-term survival whenever complete resection is feasible, with acceptable complication rates. Patients with left kidney RCC, body/tail, and asymptomatic PM_RCC and an interval of more than 2 years after nephrectomy exhibit the best survival rates.

A primer on artificial intelligence in pancreatic imaging

Artificial Intelligence (AI) is set to transform medical imaging by leveraging the vast data contained in medical images. Deep learning and radiomics are the two main AI methods currently being applied within radiology. Deep learning uses a layered set of self-correcting algorithms to develop a mathematical model that best fits the data. Radiomics converts imaging data into mineable features such as signal intensity, shape, texture, and higher-order features. Both methods have the potential to improve disease detection, characterization, and prognostication. This article reviews the current status of artificial intelligence in pancreatic imaging and critically appraises the quality of existing evidence using the radiomics quality score.

Metastasis from Renal Cell Carcinoma to Ectopic Pancreas Diagnosed after Resection

We herein report a 68-year-old man who underwent nephrectomy for right renal cell carcinoma 10 years prior. He remained under regular medical observation, and abdominal computed tomography showed tumors in the head and tail of the pancreas. He was diagnosed with pancreatic metastasis from renal cell carcinoma. He underwent surgical excision. The pathologic diagnosis proved that the pancreatic tumors were metastases from renal cell carcinoma and clarified that an ectopic pancreas in the duodenum had metastases as well. To our knowledge, this is the first case of metastasis to an ectopic pancreas.

Development and validation of A CT-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma

Background

Early identification of synchronous distant metastasis (SDM) in patients with clear cell Renal cell carcinoma (ccRCC) can certify the reasonable diagnostic examinations.

Methods

This retrospective study recruited 463 ccRCC patients who were divided into two cohorts (training and internal validation) at a 7:3 ratio. Besides, 115 patients from other hospital were assigned external validation cohort. A radiomics signature was developed based on features by means of the least absolute shrinkage and selection operator method. Demographics, laboratory variables and CT findings were combined to develop clinical factors model. Integrating radiomics signature and clinical factors model, a radiomics nomogram was developed.

Results

Ten features were used to build radiomics signature, which yielded an area under the curve (AUC) 0.882 in the external validation cohort. By incorporating the clinical independent predictors, the clinical model was developed with AUC of 0.920 in the external validation cohort. Radiomics nomogram (external validation, 0.925) had better performance than clinical factors model or radiomics signature. Decision curve analysis demonstrated the superiority of the radiomics nomogram in terms of clinical usefulness.

Conclusions

The CT-based nomogram could help in predicting SDM status in patients with ccRCC, which might provide assistance for clinicians in making diagnostic examinations.

Synchronous or metachronous presentation of pancreatic neuroendocrine tumor versus secondary lesion to pancreas in patients affected by renal cell carcinoma. Systematic review

The simultaneous or metachronous occurrence of pancreatic neuroendocrine tumor (panNET) and renal cell carcinoma (RCC) may represent a rare coincidence or a manifestation of von Hippel-Lindau disease (VHL). These two malignancies share both radiological and cytopathological features, making the differential diagnosis very challenging. In this review, we collected all cases of concurrent diagnosis of localized panNET and RCC, with or without VHL, as reported in the literature to date. We aimed to provide an insight into the differential diagnosis between panNET and RCC pancreatic metastasis with a focus on the optimal therapeutic algorithm depending on the diagnosis. We performed literature research in PubMed library databases for articles about coexisting panNET and RCC published from 2001 to 2018. We selected nine articles with a total of 13 patients, including one treated at our institution. Patients' median age was 49 years and eight out of 13 patients were women. VHL was diagnosed in nine cases. Most patients underwent radical nephrectomy for RCC (9/13) and a clear cell renal carcinoma variant was identified in six cases. The diagnosis of panNET was synchronous with RCC detection in nine cases and metachronous in four cases. The diameter of the pancreatic lesion was >2 cm in six cases. In two cases the panNET was misdiagnosed as metastatic RCC by radiological tests. Somatostatin receptor scanning was performed only in our patient (Octreoscan) showing intense uptake in the pancreatic mass. Endoscopic ultrasound fine needle aspiration of the pancreatic lesion was performed in four patients: in two cases the panNET was confused with metastatic RCC by cytological analysis. Most patients underwent pancreatic surgery (10/13) without histological confirmation. Clear cell panNET was recognized in six cases, while mixed neuroendocrine non-neuroendocrine neoplasm was diagnosed in one patient. Immunohistochemistry (IHC) staining showed positivity to typical neuroendocrine markers (chromogranin A and synaptophysin) in all reported tested cases (8/8). Three patients underwent systemic treatment: two patients received sunitinib and one patient interleukin-2 (IL-2). Other neoplasms were observed in seven patients, of whom six were affected by VHL syndrome. When neoplastic lesions are recognized in both the kidney and pancreas, panNET and RCC pancreatic metastasis are often misdiagnosed due to similar radiological and cytopathological features. An accurate differential diagnosis is crucial and IHC plays a central role in distinguishing the two entities. The therapeutic algorithm may change depending on the diagnosis: while pancreatic RCC metastases benefit from resection, in panNETs and VHL the indication for surgery must be carefully evaluated.

Modern Radiation Diagnostics and Intelligent Personalized Technologies in Hepatopancreatology

Timely instrumental diagnosis of diseases of the hepatopancreatoduodenal region, especially of an oncological nature, is the key to successful treatment, improving prognosis and improving the quality of life of patients. At the moment, the possibilities of radiation diagnostics make it possible to identify and evaluate the nature of the blood supply to the neoplasm, its prevalence, cellularity, and in the case of MRI studies with hepatospecific contrast agents, also evaluate the functional activity of liver cells. Nevertheless, the steady development of methods for treating cancer patients, in particular, chemotherapy, and a personalized approach to the choice of patient management tactics require a detailed assessment of the morphological types of certain neoplasms. The need for dynamic monitoring of the results of treatment, monitoring of accidentally detected, potentially malignant neoplasms, and the development of screening programs determine the steady increase in the number of CT and MR examinations performed annually in the world and in our country. These factors have led to the application of texture analysis or radiomics and machine learning algorithms. At the same time, such techniques as radiography, ultrasound, CT and MRI with extracellular and tissue-specific contrast enhancement, and MRI-DWI do not lose their significance. The ongoing research allows the Federal State Budgetary Institution National Medical Research Center of Surgery named after A.V. Vishnevsky of the Ministry of Health of Russia to implement the concept of preoperative non-invasive diagnosis and differential diagnosis of surgical and oncological diseases of the hepatopancreatoduodenal region and apply the knowledge gained in planning surgical treatment. Implementation of the problem of post-processor data processing of radiation diagnostics of surgical and oncological diseases of the hepatopancreatoduodenal region using radiomics and AI technologies is important and extremely relevant for modern medicine.

Misdiagnosis of pancreatic metastasis from renal cell carcinoma: A case report

BACKGROUND

Pancreatic metastases account for only a small proportion of all pancreatic malignancies. Isolated pancreatic metastasis from renal cell cancer (isPM-RCC) is extremely rare and may be difficult to differentiate from more common primary neoplasms. A history of nephrectomy is crucial for the diagnosis.

CASE SUMMARY

We report the case of a 64-year-old Asian man who was diagnosed with a mass in the pancreatic head using computed tomography. He had no related symptoms, and his medical history was unremarkable, except for unilateral nephrectomy performed to remove a “benign” tumor 19 years ago. All preoperative imaging findings suggested a diagnosis of pancreatic neuroendocrine tumor. However, ultrasound-guided biopsy revealed features of clear cell renal cell carcinoma (ccRCC). Re-examination of the specimen resected 19 years ago confirmed that he had a ccRCC. The pancreatic mass was resected and pathological examination confirmed isPM-RCC.

CONCLUSION

Misdiagnosis of isPM-RCC is common because of its rarity and the long interval from resection of the primary tumor and manifestation of the metastasis. The history of the previous surgery may be the only clue.

GEP-NET radiomics: a systematic review and radiomics quality score assessment

OBJECTIVE

The number of radiomics studies in gastroenteropancreatic neuroendocrine tumours (GEP-NETs) is rapidly increasing. This systematic review aims to provide an overview of the available evidence of radiomics for clinical outcome measures in GEP-NETs, to understand which applications hold the most promise and which areas lack evidence.

METHODS

PubMed, Embase, and Wiley/Cochrane Library databases were searched and a forward and backward reference check of the identified studies was executed. Inclusion criteria were (1) patients with GEP-NETs and (2) radiomics analysis on CT, MRI or PET. Two reviewers independently agreed on eligibility and assessed methodological quality with the radiomics quality score (RQS) and extracted outcome data.

RESULTS

In total, 1364 unique studies were identified and 45 were included for analysis. Most studies focused on GEP-NET grade and differential diagnosis of GEP-NETs from other neoplasms, while only a minority analysed treatment response or long-term outcomes. Several studies were able to predict tumour grade or to differentiate GEP-NETs from other lesions with a good performance (AUCs 0.74-0.96 and AUCs 0.80-0.99, respectively). Only one study developed a model to predict recurrence in pancreas NETs (AUC 0.77). The included studies reached a mean RQS of 18%.

CONCLUSION

Although radiomics for GEP-NETs is still a relatively new area, some promising models have been developed. Future research should focus on developing robust models for clinically relevant aims such as prediction of response or long-term outcome in GEP-NET, since evidence for these aims is still scarce.

KEY POINTS

• The majority of radiomics studies in gastroenteropancreatic neuroendocrine tumours is of low quality. • Most evidence for radiomics is available for the identification of tumour grade or differentiation of gastroenteropancreatic neuroendocrine tumours from other neoplasms. • Radiomics for the prediction of response or long-term outcome in gastroenteropancreatic neuroendocrine tumours warrants further research.

Differentiation between pancreatic metastases from renal cell carcinoma and pancreatic neuroendocrine neoplasm using endoscopic ultrasound

OBJECTIVES

Pancreatic metastases from renal cell carcinoma (PRCC) often appear many years after treatment of the primary tumor, and differentiation from pancreatic neuroendocrine neoplasm (PanNEN) can be challenging due to their hypervascularity. Here, we investigated the utility of endoscopic ultrasound (EUS) for differentiation of these conditions.

METHODS

A retrospective analysis was performed in 17 and 79 consecutive patients with pathologically proven PRCC and non-functional PanNEN who were examined by EUS. In cases examined by EUS elastography or contrast-enhanced harmonic EUS (CH-EUS), the lesions were classified as stiff or soft, or into three vascular patterns as hypoechoic, isoechoic, and hyperechoic. CH-EUS images at 20 s, 40 s, 60 s, 3 min and 5 min were used for evaluation. EUS images were independently reviewed by two readers who were blinded to all clinical information.

RESULTS

The patients with PRCC were significantly older than those with PanNEN (median, 71 (range, 45-81) vs. 58 (22-76), P = 0.001) and more often had multiple tumors (6/17 (35%) vs. 7/79 (9%), P = 0.010). In EUS findings, PRCC lesions significantly more frequently had a marginal hypoechoic zone (MHZ) (11/17 (65%) vs. 27/79 (34%), P = 0.028), being classified as soft (12/13 (92%) vs. 26/58 (45%), P = 0.002), and showed sustained hyperechoic vascular patterns at 5 min (7/8 (88%) vs. 4/59 (7%), P < 0.001) compared to PanNEN lesions.

CONCLUSIONS

The presence of a MHZ, a soft lesion, and a sustained hyperechoic vascular pattern in EUS may be useful for differentiating PRCC from PanNEN.

Computed tomography-based radiomics approach in pancreatic tumors characterization

Radiomics (or texture analysis) is a new imaging analysis technique that allows calculating the distribution of texture features of pixel and voxel values depend on the type of ROI (3D or 2D), their relationships in the image. Depending on the software, up to several thousand texture elements can be obtained. Radiomics opens up wide opportunities for differential diagnosis and prognosis of pancreatic neoplasias. The aim of this review was to highlight the main diagnostic advantages of texture analysis in different pancreatic tumors. The review describes the diagnostic performance of radiomics in different pancreatic tumor types, application methods, and problems. Texture analysis in PDAC is able to predict tumor grade and associates with lymphovascular invasion and postoperative margin status. In pancreatic neuroendocrine tumors, texture features strongly correlate with differentiation grade and allows distinguishing it from the intrapancreatic accessory spleen. In pancreatic cystic lesions, radiomics is able to accurately differentiate MCN from SCN and distinguish clinically insignificant lesions from IPMNs with advanced neoplasia. In conclusion, the use of the CT radiomics approach provides a higher diagnostic performance of CT imaging in pancreatic tumors differentiation and prognosis. Future studies should be carried out to improve accuracy and facilitate radiomics workflow in pancreatic imaging.

Utility of CT to Differentiate Pancreatic Parenchymal Metastasis from Pancreatic Ductal Adenocarcinoma

Simple Summary

The purpose of this retrospective study was to report the computed tomography (CT) features of pancreatic parenchymal metastasis (PPM) and identify CT features that may help discriminate between PPM and PDAC. At multivariable analysis, well-defined margins (OR, 6.64; 95% CI: 1.47–29.93; p = 0.014), maximal enhancement during arterial phase (OR, 6.15; 95% CI: 1.13–33.51; p = 0.036), no vessel involvement (OR, 7.19; 95% CI: 1.51–34.14) and no Wirsung duct dilatation (OR, 10.63; 95% CI: 2.27–49.91) were independently associated with PPM. A nomogram based on CT features identified at multivariable analysis yielded an AUC of 0.92 (95% CI: 0.85–0.98) for the diagnosis of PPM vs. PDAC.

Abstract

Purpose: To report the computed tomography (CT) features of pancreatic parenchymal metastasis (PPM) and identify CT features that may help discriminate between PPM and pancreatic ductal adenocarcinoma (PDAC). Materials and methods: Thirty-four patients (24 men, 12 women; mean age, 63.3 ± 10.2 [SD] years) with CT and histopathologically proven PPM were analyzed by two independent readers and compared to 34 patients with PDAC. Diagnosis performances of each variable for the diagnosis of PPM against PDAC were calculated. Univariable and multivariable analyses were performed. A nomogram was developed to diagnose PPM against PDAC. Results: PPM mostly presented as single (34/34; 100%), enhancing (34/34; 100%), solid (27/34; 79%) pancreatic lesion without visible associated lymph nodes (24/34; 71%) and no Wirsung duct enlargement (29/34; 85%). At multivariable analysis, well-defined margins (OR, 6.64; 95% CI: 1.47–29.93; p = 0.014), maximal enhancement during arterial phase (OR, 6.15; 95% CI: 1.13–33.51; p = 0.036), no vessel involvement (OR, 7.19; 95% CI: 1.512–34.14) and no Wirsung duct dilatation (OR, 10.63; 95% CI: 2.27–49.91) were independently associated with PPM. The nomogram yielded an AUC of 0.92 (95% CI: 0.85–0.98) for the diagnosis of PPM vs. PDAC. Conclusion: CT findings may help discriminate between PPM and PDAC.

Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance

PURPOSE

To present the state-of-art of radiomics in the context of pancreatic neuroendocrine tumors (PanNETs), with a focus on the methodological and technical approaches used, to support the search of guidelines for optimal applications. Furthermore, an up-to-date overview of the current clinical applications of radiomics in the field of PanNETs is provided.

METHODS

Original articles were searched on PubMed and Science Direct with specific keywords. Evaluations of the selected studies have been focused mainly on (i) the general radiomic workflow and the assessment of radiomic features robustness/reproducibility, as well as on the major clinical applications and investigations accomplished so far with radiomics in the field of PanNETs: (ii) grade prediction, (iii) differential diagnosis from other neoplasms, (iv) assessment of tumor behavior and aggressiveness, and (v) treatment response prediction.

RESULTS

Thirty-one articles involving PanNETs radiomic-related objectives were selected. In regard to the grade differentiation task, yielded AUCs are currently in the range of 0.7-0.9. For differential diagnosis, the majority of studies are still focused on the preliminary identification of discriminative radiomic features. Limited information is known on the prediction of tumors aggressiveness and of treatment response.

CONCLUSIONS

Radiomics is recently expanding in the setting of PanNETs. From the analysis of the published data, it is emerging how, prior to clinical application, further validations are necessary and methodological implementations require optimization. Nevertheless, this new discipline might have the potential in assisting the current urgent need of improving the management strategies in PanNETs patients.

Spectral CT in clinical routine imaging of neuroendocrine neoplasms

AIM

To evaluate the potential of new spectral computed tomography (SCT)-based tools in patients with neuroendocrine neoplasms (NEN).

MATERIAL AND METHODS

Eighty-eight consecutive patients with NENs were included prospectively. The patients underwent multiphase CT with spectral and standard mode. The signal-to-noise ratio (SNR)/contrast-to-noise-ratio (CNR)_{tumour-to-liver}, iodine concentrations (ICs, total tumour/hotspot) and attenuation slopes in virtual monochromatic images (VMIs) were used to assess NEN-specific SCT values in primary tumours and metastatic lesions and investigate a possible lesion contrast improvement as well as possible correlations of SCT parameters to primary tumour location and tumour grade. Furthermore, the usability of SCT parameters to differentiate between the primary tumour and metastatic lesions, and to predict tumour response after 6-months follow-up was analyzed. The applied dose of spectral and standard mode was compared intra-individually.

RESULTS

SNR/CNR_{tumour-to-liver} significantly increased in low-energy VMIs. NENs showed significant differences in ICs between primary and metastatic lesions for both absolute and normalised values (p<0.001) regardless of whether the total tumour or the hotspot was measured. There was also a significant difference in the attenuation slope (p<0.001). No significant correlations were found between SCT and tumour grade. A tumour response prediction by SCT parameters was not possible. The applied dose was comparable between the scan modes.

CONCLUSION

SCT was comparable regarding applied dose, improved tumour contrast, and contributed to differentiation between primary NEN and metastasis.

Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient's Pathological Grades

Purpose

To evaluate the value of multiple machine learning methods in classifying pathological grades (G1,G2, and G3), and to provide the best machine learning method for the identification of pathological grades of pancreatic neuroendocrine tumors (PNETs) based on radiomics.

Materials and Methods

A retrospective study was conducted on 82 patients with Pancreatic Neuroendocrine tumors. All patients had definite pathological diagnosis and grading results. Using Lifex software to extract the radiomics features from CT images manually. The sensitivity, specificity, area under the curve (AUC) and accuracy were used to evaluate the performance of the classification model.

Result

Our analysis shows that the CT based radiomics features combined with multi algorithm machine learning method has a strong ability to identify the pathological grades of pancreatic neuroendocrine tumors. DC + AdaBoost, DC + GBDT, and Xgboost+RF were very valuable for the differential diagnosis of three pathological grades of PNET. They showed a strong ability to identify the pathological grade of pancreatic neuroendocrine tumors. The validation set AUC of DC + AdaBoost is 0.82 (G1 vs G2), 0.70 (G2 vs G3), and 0.85 (G1 vs G3), respectively.

Conclusion

In conclusion, based on enhanced CT radiomics features could differentiate between different pathological grades of pancreatic neuroendocrine tumors. Feature selection method Distance Correlation + classifier method Adaptive Boosting show a good application prospect.

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.

When to biopsy Prostate Imaging and Data Reporting System version 2 (PI-RADSv2) assessment category 3 lesions? Use of clinical and imaging variables to predict cancer diagnosis at targeted biopsy

INTRODUCTION

We aimed to determine if clinical and imaging features can stratify men at higher risk for clinically significant (CS, International Society of Urological Pathology [ISUP] grade group ≥2) prostate cancer (PCa) in equivocal Prostate Imaging and Data Reporting System (PI-RADS) category 3 lesions on magnetic resonance imaging (MRI).

METHODS

Approved by the institutional review board, this retrospective study involved 184 men with 198 lesions who underwent 3T-MRI and MRI-directed transrectal ultrasound biopsy for PI-RADS 3 lesions. Men were evaluated including clinical stage, prostate-specific antigen density (PSAD), indication, and MRI lesion size. Diagnoses for all men and by indication (no cancer, any PCa, CSPCa) were compared using multivariate logistic regression, including stage, PSAD, and lesion size.

RESULTS

We found an overall PCa rate of 31.8% (63/198) and 10.1% (20/198) CSPCa (13 grade group 2, five group 3, and two group 4). Higher stage (p=0.001), PSAD (p=0.007), and lesion size (p=0.015) were associated with CSPCa, with no association between CSPCa and age, PSA, or prostate volume (p>0.05). PSAD modestly predicted CSPCa area under the curve (AUC) 0.66 (95% confidence interval [CI] 0.518-0.794) in all men and 0.64 (0.487-0.799) for those on active surveillance (AS). Model combining clinical stage, PSAD, and lesion size improved accuracy for all men and AS (AUC 0.82 [0.736-0.910], p<0.001 and 0.785 [0.666-0.904], p<0.001). In men with prior negative biopsy and persistent suspicion, PSAD (0.90 [0.767-1.000]) was not different from the model (p>0.05), with optimal cutpoint of ≥0.215 ng/mL/cc achieving sensitivity/specificity of 85.7/84.4%.

CONCLUSIONS

PI-RADSv2 category 3 lesions are often not CSPCa. PSAD predicted CSPCa in men with a prior negative biopsy; however, PSAD alone had limited value, and accuracy improved when using a model incorporating PSAD with clinical stage and MRI lesion size.

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.

Hypovascular pancreas head adenocarcinoma: CT texture analysis for assessment of resection margin status and high-risk features

OBJECTIVES

To determine if CT texture analysis features are associated with hypovascular pancreas head adenocarcinoma (PHA) postoperative margin status, nodal status, grade, lymphovascular invasion (LVI), and perineural invasion (PNI).

METHODS

This Research Ethics Board-approved retrospective cohort study included 131 consecutive patients with resected PHA. Tumors were segmented on preoperative contrast-enhanced CT. Tumor diameter and texture analysis features including mean, minimum and maximum Hounsfield units, standard deviation, skewness, kurtosis, and entropy and gray-level co-occurrence matrix (GLCM) features correlation and dissimilarity were extracted. Two-sample t test and logistic regression were used to compare parameters for prediction of margin status, nodal status, grade, LVI, and PNI. Diagnostic accuracy was assessed using receiver operating characteristic curves and Youden method was used to establish cutpoints.

RESULTS

Margin status was associated with GLCM correlation (p = 0.012) and dissimilarity (p = 0.003); nodal status was associated with standard deviation (p = 0.026) and entropy (p = 0.031); grade was associated with kurtosis (p = 0.031); LVI was associated with standard deviation (p = 0.047), entropy (p = 0.026), and GLCM correlation (p = 0.033) and dissimilarity (p = 0.011). No associations were found for PNI (p > 0.05). Logistic regression yielded an area under the curve of 0.70 for nodal disease, 0.70 for LVI, 0.68 for grade, and 0.65 for margin status. Optimal sensitivity/specificity was as follows: nodal disease 73%/72%, LVI 72%/65%, grade 55%/83%, and margin status 63%/66%.

CONCLUSIONS

CT texture analysis features demonstrate fair diagnostic accuracy for assessment of hypovascular PHA nodal disease, LVI, grade, and postoperative margin status. Additional research is rapidly needed to identify these high-risk features with better accuracy.

KEY POINTS

• CT texture analysis features are associated with pancreas head adenocarcinoma postoperative margin status which may help inform treatment decisions as a negative resection margin is required for cure. • CT texture analysis features are associated with pancreas head adenocarcinoma nodal disease, a poor prognostic feature. • Indicators of more aggressive pancreas head adenocarcinoma biology including tumor grade and LVI can be diagnosed using CT texture analysis with fair accuracy.

Shape Analysis of Peripheral Zone Observations on Prostate DWI: Correlation to Histopathology Outcomes After Radical Prostatectomy

OBJECTIVE. The objective of our study was to subjectively and quantitatively assess shape features of peripheral zone (PZ) tumors at DWI compared with pathologic outcomes. MATERIALS AND METHODS. During the study period, 241 consecutive men with PZ dominant prostate tumors underwent 3-T MRI including DWI before undergoing radical prostatectomy. DW images of these patients were retrospectively assessed by two blinded radiologists. The reviewers assigned Prostate Imaging Reporting and Data System (PI-RADS) shape categories (round or oval, crescentic [i.e., conforming to PZ], linear or wedge-shaped) and segmented tumors for quantitative shape analysis. Discrepancies were resolved by consensus. Comparisons were performed with Gleason score (GS) and pathologic stage. RESULTS. Consensus review results were as follows: 63.9% (154/241) of tumors were round or oval; 22.8% (55/241), crescentic; and 13.3% (32/241), linear or wedge-shaped. Agreement for shape assessment was moderate (κ = 0.41). Round or oval tumors were higher grade (GS 6 = 1.3%, GS 7 = 78.0%, GS ≥ 8 = 20.7%) than crescentic tumors (GS 6 = 9.1%, GS 7 = 74.6%, GS ≥ 8 = 16.3%) and linear or wedge-shaped tumors (GS 6 = 6.3%, GS 7 = 78.1%, GS ≥ 8 = 15.6%) (p = 0.011). In addition, round or oval tumors had higher rates of extraprostatic extension (EPE) and seminal vesicle invasion (SVI) (EPE and SVI: 70.1% and 26.0%) than crescentic tumors (67.3% and 9.1%; p = 0.003) and linear or wedge-shaped tumors (40.6% and 9.4%; p = 0.008). Quantitatively, the shape features termed "circularity" and "roundness" were associated with EPE (p < 0.001 and p = 0.003), SVI (p < 0.001 and p = 0.029), and increasing GS (p = 0.009 and p = 0.021), but there was overlap between groups. CONCLUSION. In this study, approximately 10% of resected PZ tumors were linear or wedge-shaped on DWI. PZ tumors that were judged subjectively and evaluated quantitatively to be round or oval were associated with increased prostate cancer aggressiveness.

Effect of observation size and apparent diffusion coefficient (ADC) value in PI-RADS v2.1 assessment category 4 and 5 observations compared to adverse pathological outcomes

OBJECTIVE

To compare observation size and apparent diffusion coefficient (ADC) values in Prostate Imaging Reporting and Data System (PI-RADS) v2.1 category 4 and 5 observations to adverse pathological features.

MATERIALS AND METHODS

With institutional review board approval, 267 consecutive men with 3-T MRI before radical prostatectomy (RP) between 2012 and 2018 were evaluated by two blinded radiologists who assigned PI-RADS v2.1 scores. Discrepancies were resolved by consensus. A third blinded radiologist measured observation size and ADC (ADC.mean, ADC.min [lowest ADC within an observation], ADC.ratio [ADC.mean/ADC.peripheral zone {PZ}]). Size and ADC were compared to pathological stage and Gleason score (GS) using t tests, ANOVA, Pearson correlation, and receiver operating characteristic (ROC) analysis.

RESULTS

Consensus review identified 267 true positive category 4 and 5 observations representing 83.1% (222/267) PZ and 16.9% (45/267) transition zone (TZ) tumors. Inter-observer agreement for PI-RADS v2.1 scoring was moderate (K = 0.45). Size was associated with extra-prostatic extension (EPE) (19 ± 8 versus 14 ± 6 mm, p < 0.001) and seminal vesicle invasion (SVI) (24 ± 9 versus 16 ± 7 mm, p < 0.001). Size ≥ 15 mm optimized the accuracy for EPE with area under the ROC curve (AUC) and sensitivity/specificity of 0.68 (CI 0.62-0.75) and 63.2%/65.6%. Size ≥ 19 mm optimized the accuracy for SVI with AUC/sensitivity/specificity of 0.75 (CI 0.66-0.83)/69.4%/70.6%. ADC metrics were not associated with pathological stage. Larger observation size (p = 0.032), lower ADC.min (p = 0.010), and lower ADC.ratio (p = 0.010) were associated with higher GS. Size correlated better to higher Gleason scores (p = 0.002) compared to ADC metrics (p = 0.09-0.11).

CONCLUSION

Among PI-RADS v2.1 category 4 and 5 observations, size was associated with higher pathological stage whereas ADC metrics were not. Size, ADC.minimum, and ADC.ratio differed in tumors stratified by Gleason score.

KEY POINTS

• Among PI-RADS category 4 and 5 observations, size but not ADC can differentiate between tumors by pathological stage. • An observation size threshold of 15 mm and 19 mm optimized the accuracy for diagnosis of extra-prostatic extension and seminal vesicle invasion. • Among PI-RADS category 4 and 5 observations, size, ADC.minimum, and ADC.ratio differed comparing tumors by Gleason score.

Diagnostic Accuracy of Single-Phase Computed Tomography Texture Analysis for Prediction of LI-RADS v2018 Category

OBJECTIVE

The aim of this study was to determine if texture analysis can classify liver observations likely to be hepatocellular carcinoma based on the Liver Imaging Reporting and Data System (LI-RADS) using single portal venous phase computed tomography.

METHODS

This research ethics board-approved retrospective cohort study included 64 consecutive LI-RADS observations. Individual observation texture analysis features were compared using Kruskal-Wallis and 2 sample t tests. Logistic regression was used for prediction of LI-RADS group. Diagnostic accuracy was assessed using receiver operating characteristic curves and Youden method.

RESULTS

Multiple texture features were associated with LI-RADS including the mean HU (P = 0.003), median (P = 0.002), minimum (P = 0.010), maximum (P = 0.013), standard deviation (P = 0.009), skewness (P = 0.007), and entropy (P < 0.001). On logistic regression, LI-RADS group could be predicted with area under the curve, sensitivity, and specificity of 0.98, 96%, and 100%, respectively.

CONCLUSIONS

Texture analysis features on portal venous phase computed tomography can identify liver observations likely to be hepatocellular carcinoma, which may preclude the need to recall some patients for additional multiphase imaging.