Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMN) with Canonical Correlation Analysis

Pancreatic cancer has the poorest prognosis among all cancer types. Intraductal Papillary Mucinous Neoplasms (IPMNs) are radiographically identifiable precursors to pancreatic cancer; hence, early detection and precise risk assessment of IPMN are vital. In this work, we propose a Convolutional Neural Network (CNN) based computer aided diagnosis (CAD) system to perform IPMN diagnosis and risk assessment by utilizing multi-modal MRI. In our proposed approach, we use minimum and maximum intensity projections to ease the annotation variations among different slices and type of MRIs. Then, we present a CNN to obtain deep feature representation corresponding to each MRI modality (T1-weighted and T2-weighted). At the final step, we employ canonical correlation analysis (CCA) to perform a fusion operation at the feature level, leading to discriminative canonical correlation features. Extracted features are used for classification. Our results indicate significant improvements over other potential approaches to solve this important problem. The proposed approach doesn't require explicit sample balancing in cases of imbalance between positive and negative examples. To the best of our knowledge, our study is the first to automatically diagnose IPMN using multi-modal MRI.Comment: Accepted for publication in IEEE International Symposium on Biomedical Imaging (ISBI) 201

Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images

Early detection of precancerous cysts or neoplasms, i.e., Intraductal Papillary Mucosal Neoplasms (IPMN), in pancreas is a challenging and complex task, and it may lead to a more favourable outcome. Once detected, grading IPMNs accurately is also necessary, since low-risk IPMNs can be under surveillance program, while high-risk IPMNs have to be surgically resected before they turn into cancer. Current standards (Fukuoka and others) for IPMN classification show significant intra- and inter-operator variability, beside being error-prone, making a proper diagnosis unreliable. The established progress in artificial intelligence, through the deep learning paradigm, may provide a key tool for an effective support to medical decision for pancreatic cancer. In this work, we follow this trend, by proposing a novel AI-based IPMN classifier that leverages the recent success of transformer networks in generalizing across a wide variety of tasks, including vision ones. We specifically show that our transformer-based model exploits pre-training better than standard convolutional neural networks, thus supporting the sought architectural universalism of transformers in vision, including the medical image domain and it allows for a better interpretation of the obtained results

Chemotherapy-associated liver morphological changes in hepatic metastases (CALMCHeM)

PURPOSETo review imaging findings in chemotherapy-associated liver morphological changes in hepatic metastases (CALMCHeM) on computed tomography (CT)/magnetic resonance imaging (MRI) and its association with tumor burden.METHODSWe performed a retrospective chart review to identify patients with hepatic metastases who received chemotherapy and subsequent follow-up imaging where CT or MRI showed morphological changes in the liver. The morphological changes searched for were nodularity, capsular retraction, hypodense fibrotic bands, lobulated outline, atrophy or hypertrophy of segments or lobes, widened fissures, and one or more features of portal hypertension (splenomegaly/venous collaterals/ascites). The inclusion criteria were as follows: a) no known chronic liver disease; b) availability of CT or MRI images before chemotherapy that showed no morphological signs of chronic liver disease; c) at least one follow-up CT or MRI image demonstrating CALMCHeM after chemotherapy. Two radiologists in consensus graded the initial hepatic metastases tumor burden according to number (≤10 and >10), lobe distribution (single or both lobes), and liver parenchyma volume affected (10 in 64.4% of patients. The volume of liver involved was <50% in 79.8% and ≥50% in 20.2% of cases. The severity of CALMCHeM at the first imaging follow-up was associated with a larger number of metastases (P = 0.002) and volume of the liver affected (P = 0.015). The severity of CALMCHeM had progressed to moderate to severe changes in 85.9% of patients, and 72.5% of patients had one or more features of portal hypertension at the last follow-up. The most common features at the final follow-up were nodularity (95.0%), capsular retraction (93.4%), atrophy (66.2%), and ascites (65.7%). The Cox proportional hazard model showed metastases affected ≥50% of the liver (P = 0.033), and the female gender (P = 0.004) was independently associated with severe CALMCHeM.CONCLUSIONCALMCHeM can be observed with a wide variety of malignancies, is progressive in severity, and the severity correlates with the initial metastatic liver disease burden

Pancreatic steatosis on computed tomography is an early imaging feature of pre-diagnostic pancreatic cancer: A preliminary study in overweight patients

Background: The prevalence of pancreatic ductal adenocarcinoma (PDAC) is on the rise, driven by factors such as aging and an increasing prevalence of obesity and diabetes mellitus. To improve the poor survival rate of PDAC, early detection is vital. Recently, pancreatic steatosis has gained novel interest as a risk factor for PDAC. This study aimed to investigate if pancreatic steatosis on computed tomography (CT) is an early imaging feature in patients with pre-diagnostic PDAC. Methods: A retrospective case-control study was performed. Patients diagnosed with PDAC (2010–2016) were reviewed for abdominal non-contrast CT-imaging 1 month-3 years prior to their diagnosis. Cases were matched 1:4 with controls based on age, gender and imaging date. Unenhanced CT-images were evaluated for pancreatic steatosis (pancreas-to-spleen ratio in Hounsfield Units <0.70) by a blinded radiologist and results were compared between cases and controls. Results: In total, 32 cases and 117 controls were included in the study with a comparable BMI (29.6 and 29.2 respectively, p = 0.723). Pancreatic steatosis was present in 71.9% of cases compared to 45.3% of controls (Odds ratio (OR) 3.09(1.32–7.24), p = 0.009). Adjusted for BMI and diabetes mellitus, pancreatic steatosis on CT remained a significant independent risk factor for PDAC (Adjusted OR 2.70(1.14–6.58), p = 0.037). Conclusion: Pancreatic steatosis measured on CT is independently associated with PDAC up to three years before the clinical diagnosis in overweight patients. If these data are confirmed, this novel imaging feature may be used to identify high-risk individuals and to stratify the risk of PDAC in individuals that already undergo PDAC screening

Survival of Patients with Oligometastatic Pancreatic Ductal Adenocarcinoma Treated with Combined Modality Treatment Including Surgical Resection: A Pilot Study

Purpose: To evaluate the overall survival of patients with oligometastatic pancreatic ductal adenocarcinoma (PDAC; metastatic tumor &lt;4 cm, ≤2 metastatic tumors total) receiving neoadjuvant therapy, metastasectomy and/or ablation, and primary tumor resection. Methods: We performed a case–control study from January 2005 to December 2015. Patients who underwent curative-intent surgery combined modality therapy (M1 surgery group; 6 [14%], tumor [T]3, node [N]1, and oligo-metastases [M]1) were matched 1 to 3 based on TN stage with two control groups (M0 surgery and M1 no surgery). The M0 surgery group (18 [43%], T3, N1, and M0) included patients without metastases who underwent resection. The M1 no surgery group (18 [43%], T3, N1, and M1) included patients with metastatic PDAC who received palliative chemotherapy without surgical resection. Results: Median overall survival in the M1 surgery, M0 surgery, and M1 no surgery groups was 2.7 years (95% confidence interval [CI], 0.71–3.69), 2.02 years (95% CI, 0.98–3.05), and 0.98 years (95% CI, 0.55–1.25), respectively. Eastern Cooperative Oncology Group (ECOG) status was associated with survival (p = 0.01) after univariate analysis. After adjusting for ECOG status, multivariate analysis showed M1 surgery patients had improved survival compared with M1 no surgery patients and similar survival to M0 surgery patients. Conclusion: Multimodal therapy benefitted our M1 surgery patients. A larger, prospective study of this multidisciplinary management strategy is currently under way

Impact of measurement method on interobserver variability of apparent diffusion coefficient of lesions in prostate MRI.

PurposeTo compare the inter-observer variability of apparent diffusion coefficient (ADC) values of prostate lesions measured by 2D-region of interest (ROI) with and without specific measurement instruction.MethodsForty lesions in 40 patients who underwent prostate MR followed by targeted prostate biopsy were evaluated. A multi-reader study (10 readers) was performed to assess the agreement of ADC values between 2D-ROI without specific instruction and 2D-ROI with specific instruction to place a 9-pixel size 2D-ROI covering the lowest ADC area. The computer script generated multiple overlapping 9-pixel 2D-ROIs within a 3D-ROI encompassing the entire lesion placed by a single reader. The lowest mean ADC values from each 2D-small-ROI were used as reference values. Inter-observer agreement was assessed using the Bland-Altman plot. Intraclass correlation coefficient (ICC) was assessed between ADC values measured by 10 readers and the computer-calculated reference values.ResultsTen lesions were benign, 6 were Gleason score 6 prostate carcinoma (PCa), and 24 were clinically significant PCa. The mean±SD ADC reference value by 9-pixel-ROI was 733 ± 186 (10-6 mm2/s). The 95% limits of agreement of ADC values among readers were better with specific instruction (±112) than those without (±205). ICC between reader-measured ADC values and computer-calculated reference values ranged from 0.736-0.949 with specific instruction and 0.349-0.919 without specific instruction.ConclusionInterobserver agreement of ADC values can be improved by indicating a measurement method (use of a specific ROI size covering the lowest ADC area)