Evaluation of deep learning based dose prediction in head and neck cancer patients using two different types of input contours.

PURPOSE: This study evaluates deep learning (DL) based dose prediction methods in head and neck cancer (HNC) patients using two types of input contours. MATERIALS AND METHODS: Seventy-five HNC patients undergoing two-step volumetric-modulated arc therapy were included. Dose prediction was performed using the AIVOT prototype (AiRato.Inc, Sendai, Japan), a commercial software with an HD U-net-based dose distribution prediction system. Models were developed for the initial plan (46 Gy/23Fr) and boost plan (24 Gy/12Fr), trained with 65 cases and tested with 10 cases. The 8-channel model used one target (PTV) and seven organs at risk (OARs), while the 10-channel model added two dummy contours (PTV ring and spinal cord PRV). Predicted and deliverable doses, obtained through dose mimicking on another radiation treatment planning system, were evaluated using dose-volume indices for PTV and OARs. RESULTS: For the initial plan, both models achieved approximately 2% prediction accuracy for the target dose and maintained accuracy within 3.2 Gy for OARs. The 10-channel model outperformed the 8-channel model for certain dose indices. For the boost plan, both models exhibited prediction accuracies of approximately 2% for the target dose and 1 Gy for OARs. The 10-channel model showed significantly closer predictions to the ground truth for D50% and Dmean. Deliverable plans based on prediction doses showed little significant difference compared to the ground truth, especially for the boost plan. CONCLUSION: DL-based dose prediction using the AIVOT prototype software in HNC patients yielded promising results. While additional contours may enhance prediction accuracy, their impact on dose mimicking is relatively small.

Assessing knowledge about medical physics in language-generative AI with large language model: using the medical physicist exam.

This study aimed to evaluate the performance for answering the Japanese medical physicist examination and providing the benchmark of knowledge about medical physics in language-generative AI with large language model. We used questions from Japan's 2018, 2019, 2020, 2021 and 2022 medical physicist board examinations, which covered various question types, including multiple-choice questions, and mainly focused on general medicine and medical physics. ChatGPT-3.5 and ChatGPT-4.0 (OpenAI) were used. We compared the AI-based answers with the correct ones. The average accuracy rates were 42.2 ± 2.5% (ChatGPT-3.5) and 72.7 ± 2.6% (ChatGPT-4), showing that ChatGPT-4 was more accurate than ChatGPT-3.5 [all categories (except for radiation-related laws and recommendations/medical ethics): p value < 0.05]. Even with the ChatGPT model with higher accuracy, the accuracy rates were less than 60% in two categories; radiation metrology (55.6%), and radiation-related laws and recommendations/medical ethics (40.0%). These data provide the benchmark for knowledge about medical physics in ChatGPT and can be utilized as basic data for the development of various medical physics tools using ChatGPT (e.g., radiation therapy support tools with Japanese input).

Evaluation of deep learning-based deliverable VMAT plan generated by prototype software for automated planning for prostate cancer patients.

This study aims to evaluate the dosimetric accuracy of a deep learning (DL)-based deliverable volumetric arc radiation therapy (VMAT) plan generated using DL-based automated planning assistant system (AIVOT, prototype version) for patients with prostate cancer. The VMAT data (cliDose) of 68 patients with prostate cancer treated with VMAT treatment (70-74 Gy/28-37 fr) at our hospital were used (n = 55 for training and n = 13 for testing). First, a HD-U-net-based 3D dose prediction model implemented in AIVOT was customized using the VMAT data. Thus, a predictive VMAT plan (preDose) comprising AIVOT that predicted the 3D doses was generated. Second, deliverable VMAT plans (deliDose) were created using AIVOT, the radiation treatment planning system Eclipse (version 15.6) and its vender-supplied objective functions. Finally, we compared these two estimated DL-based VMAT treatment plans-i.e. preDose and deliDose-with cliDose. The average absolute dose difference of all DVH parameters for the target tissue between cliDose and deliDose across all patients was 1.32 ± 1.35% (range: 0.04-6.21%), while that for all the organs at risks was 2.08 ± 2.79% (range: 0.00-15.4%). The deliDose was superior to the cliDose in all DVH parameters for bladder and rectum. The blinded plan scoring of deliDose and cliDose was 4.54 ± 0.50 and 5.0 ± 0.0, respectively (All plans scored ≥4 points, P = 0.03.) This study demonstrated that DL-based deliverable plan for prostate cancer achieved the clinically acceptable level. Thus, the AIVOT software exhibited a potential for automated planning with no intervention for patients with prostate cancer.

Improvement of deep learning prediction model in patient-specific QA for VMAT with MLC leaf position map and patient's dose distribution.

PURPOSE: Deep learning-based virtual patient-specific quality assurance (QA) is a novel technique that enables patient QA without measurement. However, this method could be improved by further evaluating the optimal data to be used as input. Therefore, a deep learning-based model that uses multileaf collimator (MLC) information per control point and dose distribution in patient's CT as inputs was developed. METHODS: Overall, 96 volumetric-modulated arc therapy plans generated for prostate cancer treatment were used. We developed a model (Model 1) that can predict measurement-based gamma passing rate (GPR) for a treatment plan using data stored as a map reflecting the MLC leaf position at each control point (MLPM) and data of the dose distribution in patient's CT as inputs. The evaluation of the model was based on the mean absolute error (MAE) and Pearson's correlation coefficient (r) between the measured and predicted GPR. For comparison, we also analyzed models trained with the dose distribution in patient's CT alone (Model 2) and with dose distributions recalculated on a virtual phantom CT (Model 3). RESULTS: At the 2%/2 mm criterion, MAE[%] and r for Model 1, Model 2, and Model 3 were 2.32% ± 0.43% and 0.54 ± 0.03, 2.70% ± 0.26%, and 0.32 ± 0.08, and 2.96% ± 0.23% and 0.24 ± 0.22, respectively; at the 3%/3 mm criterion, these values were 1.25% ± 0.05% and 0.36 ± 0.18, 1.57% ± 0.35% and 0.19 ± 0.20, and 1.39% ± 0.32% and 0.17 ± 0.22, respectively. This result showed that Model 1 exhibited the lowest MAE and highest r at both criteria of 2%/2 mm and 3%3 mm. CONCLUSIONS: These findings showed that a model that combines the MLPM and dose distribution in patient's CT exhibited a better GPR prediction performance compared with the other two studied models.

Development of a deep learning-based error detection system without error dose maps in the patient-specific quality assurance of volumetric modulated arc therapy.

To detect errors in patient-specific quality assurance (QA) for volumetric modulated arc therapy (VMAT), we proposed an error detection method based on dose distribution analysis using unsupervised deep learning approach and analyzed 161 prostate VMAT beams measured with a cylindrical detector. For performing error simulation, in addition to error-free dose distribution, dose distributions containing nine types of error, including multileaf collimator (MLC) positional errors, gantry rotation errors, radiation output errors and phantom setup errors, were generated. Only error-free data were employed for the model training, and error-free and error data were employed for the tests. As a deep learning model, the variational autoencoder (VAE) was adopted. The anomaly of test data was quantified by calculating Mahalanobis distance based on the feature vectors acquired from a trained encoder. Based on this anomaly, test data were classified as 'error-free' or 'any-error.' For comparison with conventional approaches, gamma (γ)-analysis was performed, and supervised learning convolutional neural network (S-CNN) was constructed. Receiver operating characteristic curves were obtained to evaluate their performance with the area under the curve (AUC). For all error types, except systematic MLC positional and radiation output errors, the performance of the methods was in the order of S-CNN ˃ VAE-based ˃ γ-analysis (only S-CNN required error data for model training). For example, in random MLC positional error simulation, the AUC of our method, S-CNN and γ-analysis were 0.699, 0.921 and 0.669, respectively. Our results showed that the VAE-based method has the potential to detect errors in patient-specific VMAT QA.

Applying Artificial Neural Networks to Develop a Decision Support Tool for Tis-4N0M0 Non-Small-Cell Lung Cancer Treated With Stereotactic Body Radiotherapy.

PURPOSE: Clear evidence indicating whether surgery or stereotactic body radiation therapy (SBRT) is best for non-small-cell lung cancer (NSCLC) is lacking. SBRT has many advantages. We used artificial neural networks (NNs) to predict treatment outcomes for patients with NSCLC receiving SBRT, aiming to aid in decision making. PATIENTS AND METHODS: Among consecutive patients receiving SBRT between 2005 and 2019 in our institution, we retrospectively identified those with Tis-T4N0M0 NSCLC. We constructed two NNs for prediction of overall survival (OS) and cancer progression in the first 5 years after SBRT, which were tested using an internal and an external test data set. We performed risk group stratification, wherein 5-year OS and cancer progression were stratified into three groups. RESULTS: In total, 692 patients in our institution and 100 patients randomly chosen in the external institution were enrolled. The NNs resulted in concordance indexes for OS of 0.76 (95% CI, 0.73 to 0.79), 0.68 (95% CI, 0.60 to 0.75), and 0.69 (95% CI, 0.61 to 0.76) and area under the curve for cancer progression of 0.80 (95% CI, 0.75 to 0.84), 0.72 (95% CI, 0.60 to 0.83), and 0.70 (95% CI, 0.57 to 0.81) in the training, internal test, and external test data sets, respectively. The survival and cumulative incidence curves were significantly stratified. NNs selected low-risk cancer progression groups of 5.6%, 6.9%, and 7.0% in the training, internal test, and external test data sets, respectively, suggesting that 48% of patients with peripheral Tis-4N0M0 NSCLC can be at low-risk for cancer progression. CONCLUSION: Predictions of SBRT outcomes using NNs were useful for Tis-4N0M0 NSCLC. Our results are anticipated to open new avenues for NN predictions and provide decision-making guidance for patients and physicians.

CT Findings and Treatment Outcomes of Ground-Glass Opacity Predominant Lung Cancer After Stereotactic Body Radiotherapy.

BACKGROUND: Stereotactic body radiotherapy (SBRT) has been rapidly evolving and increasingly performed in patients with ground-glass opacity (GGO) predominant lung cancer (GGOp-LC). PURPOSE: To evaluate early-phase CT findings of GGOp-LC after SBRT. MATERIALS AND METHODS: Patients with GGOp-LC staged as cTis-2bN0M0 treated with SBRT were retrospectively identified. The CT images were analyzed using radiologists' interpretation and CT-density histograms. Long-term treatment outcomes were also assessed. RESULTS: This study evaluated 126 patients with 133 cases of GGOp-LC, comprising GGOp-LC with pure GGO (pureGGO-LC) (n = 31) and part-solid tumors (partsolid-LC) (n = 102). The median follow-up duration was 64.3 months (range, 10.8-178.9 months). Most GGOp-LC cases were interpreted as stable disease at 1 and 3 months after SBRT (96% [125/130] and 85% [62/73], respectively). However, the solid component was often interpreted as progressive disease (42% [34/82] and 60% [29/48], respectively). The GGO component was interpreted as denser in 47% (61/130) and 86% (63/73) of cases, respectively. For 25 evaluable pureGGO-LC cases at 3 months, the median tumor density values increased over time (P < .001). For 48 evaluable partsolid-LC cases at 3 months, the median areas of CT-density ≥ -160 HU increased over time (P < .001). The 5-year overall survival for GGOp-LC patients was 78.0%. No local or regional recurrence were observed. CONCLUSION: Clinical outcomes of SBRT for GGOp-LC were excellent, without local or regional recurrence. In the interpretation of early-phase follow-up CT scans of GGOp-LC after SBRT, it should be noted that most GGOp-LC remains stable disease, solid component increases in size, and GGO component is denser.

[A case of unresectable intrahepatic cholangiocarcinoma receiving sustainable pembrolizumab therapy].

A 65-year-old man had unresectable intrahepatic cholangiocarcinoma with a malignant biliary stricture. We used an endoscopic plastic stent to drain the bile. Despite receiving standard chemotherapy, the tumor eventually progressed and cancerous peritonitis developed. We had to exchange plastic stents frequently because of stent occlusion. We had a re-biopsy with EUS-FNA and tested for microsatellite instability, which came back as MSI-high. We administered pembrolizumab, which resulted in a significant reduction of tumor size. We were able to administer long-term chemotherapy without serious side effects by repeatedly exchanging plastic stents for stent occlusion. He has maintained partial response for more than 20 months after receiving pembrolizumab.

[A case of thrombotic microangiopathy during chemotherapy with gemcitabine in a patient with pancreatic cancer].

A 57-year-old male patient with unresectable pancreatic head cancer was treated with chemotherapy, 5 courses of gemcitabine plus nab paclitaxel therapy, and 9 courses of gemcitabine monotherapy. After 12 months of treatment, he was admitted to our hospital with headache and dyspnea. He was diagnosed with gemcitabine-induced thrombotic microangiopathy (TMA) due to acute kidney dysfunction, hemolytic anemia, and thrombocytopenia. Gemcitabine was discontinued, and symptoms were improved without using hemodialysis and plasma exchange. After his renal function recovered, we started S-1 chemotherapy. Eighteen months later, the patient was alive. Looking back, we realized that fragment red blood cells appeared in complete blood count and serum LDH elevated at 5 months prior to admission, serum creatinine level increased slowly at 4 months prior to admission, and blood pressure elevated significantly at 2 months prior to admission. Therefore, physicians must be aware of TMA as a possible adverse event to gemcitabine. As in this case, hemolytic findings and hypertension in patients treated with gemcitabine may help early detection of TMA.

[A case of SIADH due to pancreatic cancer was improved by tolvaptan].

We report the case of a 68-year-old man, who presented in emergency care with inarticulate speech. The patient was diagnosed with syndrome of inappropriate antidiuretic hormone (SIADH) associated with pancreatic cancer. All diagnostic criteria for SIADH were met, and cancer of the pancreatic tail was identified by computed tomography. Standard treatment for SIADH includes water restriction, oral NaCl, continuous intravenous infusion of 3% NaCl, and intravenous infusion of furosemide. However, these treatments have varying effectiveness and are difficult for both patients and medical staff. Furthermore, unless treatment of the underlying disease is successful, continued hospitalization is needed and the patient's quality of life is significantly impaired. In this case, hyponatremia improved with this standard treatment, but ascites and edema developed. We treated the patient with tolvaptan due to decreased cardiac function, and symptoms improved rapidly. Although surgery and chemotherapy could not be performed for pancreatic cancer, the SIADH was treated for 7 months without relapse. In summary, a case of SIADH complicated by pancreatic cancer was difficult to control with standard treatment, but responded rapidly to tolvaptan, and outpatient treatment could be continued for a long period. Tolvaptan is useful for the treatment of SIADH associated with cancer.

Feasibility of marker-less stereotactic body radiotherapy for hepatocellular carcinoma.

BACKGROUND: The feasibility of marker-less stereotactic body radiotherapy (SBRT) for hepatocellular carcinoma (HCC) has not yet been established, and, thus, was examined in the present study. MATERIAL AND METHODS: We retrospectively investigated patients who received marker-less SBRT for locally untreated HCC tumors between July 2005 and December 2018. Radiotherapy planning CT was performed under fixation with vacuum cushions and abdominal compression. The clinical target volume (CTV) was equivalent to the gross tumor volume (GTV). The internal target volume (ITV) margin to CTV was determined from calculations based on the motion of the diaphragm. The planning target volume (PTV) margin to ITV was 5-6 mm. In the set-up, radiotherapy planning CT and linac-integrated cone-beam CT performed in the same imaging and fixation settings were merged by referring to the anatomical components surrounding target tumors. The primary endpoint was the 3-year cumulative local tumor progression rate. The upper limit of the 95% confidence interval for the 3-year cumulative local tumor progression rate was less than 7.0%, which was interpreted as favorable local control and feasible for marker-less SBRT. Local tumor progression was assessed by mRECIST. RESULTS: We reviewed 180 patients treated with 35-40 Gy/5 fractions. The median follow-up time for the local tumor progression of censored tumors was 32.3 months (range, 0.3-104). The 3-year cumulative local tumor progression rate was 3.0% (95% CI, 1.1-6.5%). The 3-year overall survival rate was 71.6% (95% CI, 63.5-78.2%). Regarding acute hematologic toxicities, grade 3 hypoalbuminemia and thrombocytopenia were detected in 1 (0.6%) and 5 (2.9%) patients, respectively. Treatment-related death from SBRT was not observed. SBRT was initiated within 7 days after radiotherapy planning CT for 84% (152/180) of patients. CONCLUSIONS: Marker-less SBRT for HCC achieved favorable local control that fulfilled the threshold. This result suggests that marker-less SBRT with appropriate settings is a feasible treatment strategy.

Error detection model developed using a multi-task convolutional neural network in patient-specific quality assurance for volumetric-modulated arc therapy.

PURPOSE: In patient-specific quality assurance (QA) for static beam intensity-modulated radiation therapy (IMRT), machine-learning-based dose analysis methods have been developed to identify the cause of an error as an alternative to gamma analysis. Although these new methods have revealed that the cause of the error can be identified by analyzing the dose distribution obtained from the two-dimensional detector, they have not been extended to the analysis of volumetric-modulated arc therapy (VMAT) QA. In this study, we propose a deep learning approach to detect various types of errors in patient-specific VMAT QA. METHODS: A total of 161 beams from 104 prostate VMAT plans were analyzed. All beams were measured using a cylindrical detector (Delta4; ScandiDos, Uppsala, Sweden), and predicted dose distributions in a cylindrical phantom were calculated using a treatment planning system (TPS). In addition to the error-free plan, we simulated 12 types of errors: two types of multileaf collimator positional errors (systematic or random leaf offset of 2 mm), two types of monitor unit (MU) scaling errors (±3%), two types of gantry rotation errors (±2° in clockwise and counterclockwise direction), and six types of phantom setup errors (±1 mm in lateral, longitudinal, and vertical directions). The error-introduced predicted dose distributions were created by editing the calculated dose distributions using a TPS with in-house software. Those 13 types of dose difference maps, consisting of an error-free map and 12 error maps, were created from the measured and predicted dose distributions and were used to train the convolutional neural network (CNN) model. Our model was a multi-task model that individually detected each of the 12 types of errors. Two datasets, Test sets 1 and 2, were prepared to evaluate the performance of the model. Test set 1 consisted of 13 types of dose maps used for training, whereas Test set 2 included the dose maps with 25 types of errors in addition to the error-free dose map. The dose map, which introduced 25 types of errors, was generated by combining two of the 12 types of simulated errors. For comparison with the performance of our model, gamma analysis was performed for Test sets 1 and 2 with the criteria set to 3%/2 mm and 2%/1 mm (dose difference/distance to agreement). RESULTS: For Test set 1, the overall accuracy of our CNN model, gamma analysis with the criteria set to 3%/2 mm, and gamma analysis with the criteria set to 2%/1 mm was 0.92, 0.19, and 0.81, respectively. Similarly, for Test set 2, the overall accuracy was 0.44, 0.42, and 0.95, respectively. Our model outperformed gamma analysis in the classification of dose maps containing a single type error, and the performance of our model was inferior in the classification of dose maps containing compound errors. CONCLUSIONS: A multi-task CNN model for detecting errors in patient-specific VMAT QA using a cylindrical measuring device was constructed, and its performance was evaluated. Our results demonstrate that our model was effective in identifying the error type in the dose map for VMAT QA.

Polyphenol-solubility alters amyloid fibril formation of α-synuclein.

Amyloid fibril formation is associated with various amyloidoses, including neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Amyloid fibrils form above the solubility of amyloidogenic proteins or peptides upon breaking supersaturation, followed by a nucleation and elongation mechanism, which is similar to the crystallization of solutes. Many additives, including salts, detergents, and natural compounds, promote or inhibit amyloid formation. However, the underlying mechanisms of the opposing effects are unclear. We examined the effects of two polyphenols, that is, epigallocatechin gallate (EGCG) and kaempferol-7─O─glycoside (KG), with high and low solubilities, respectively, on the amyloid formation of α-synuclein (αSN). EGCG and KG inhibited and promoted amyloid formation of αSN, respectively, when monitored by thioflavin T (ThT) fluorescence or transmission electron microscopy (TEM). Nuclear magnetic resonance (NMR) analysis revealed that, although interactions of αSN with soluble EGCG increased the solubility of αSN, thus inhibiting amyloid formation, interactions of αSN with insoluble KG reduced the solubility of αSN, thereby promoting amyloid formation. Our study suggests that opposing effects of polyphenols on amyloid formation of proteins and peptides can be interpreted based on the solubility of polyphenols.

Catalytic asymmetric synthesis of 5-membered alicyclic α-quaternary ß-amino acids via [3 + 2]-photocycloaddition of α-substituted acrylates.

The photocatalytically active salt of a cationic iridium polypyridyl complex and a chiral borate is competent to promote a highly stereoselective [3 + 2]-cycloaddition of cyclopropylurea with α-substituted acrylates. This protocol provides straightforward access to a variety of stereochemically defined 5-membered alicyclic α-quaternary ß-amino acids, useful building blocks of ß-peptides and peptidomimetics.

Systematic method for a deep learning-based prediction model for gamma evaluation in patient-specific quality assurance of volumetric modulated arc therapy.

PURPOSE: This study aimed to develop and evaluate a novel strategy for establishing a deep learning-based gamma passing rate (GPR) prediction model for volumetric modulated arc therapy (VMAT) using dummy target plan data, one measurement process, and a multicriteria prediction method. METHODS: A total of 147 VMAT plans were used for the training set (two sets of 48 dummy target plans) and test set (51 clinical target plans). The dummy plans were measured using a diode array detector. We developed an original convolutional neural network that accepts coronal and sagittal dose distributions to predict the GPRs of 36 pairs of gamma criteria from 0.5%/0.5 mm to 3%/3 mm. Sixfold cross-validation and model averaging were performed, and the mean training result and mean test result were derived from six trained models that were produced during cross-validation. RESULTS: Strong or moderate correlations were observed between the measured and predicted GPRs in all criteria. The mean absolute errors and root mean squared errors of the test set (clinical target plan) were 0.63 and 1.11 in 3%/3 mm, 1.16 and 1.73 in 3%/2 mm, 1.96 and 2.66 in 2%/2 mm, 5.00 and 6.35 in 1%/1 mm, and 5.42 and 6.78 in 0.5%/1 mm, respectively. The Pearson correlation coefficients were 0.80 in the training set and 0.68 in the test set at the 0.5%/1 mm criterion. CONCLUSION: Our results suggest that the training of the deep learning-based quality assurance model can be performed using a dummy target plan.

Urea as a Redox-Active Directing Group under Asymmetric Photocatalysis of Iridium-Chiral Borate Ion Pairs.

The development of a photoinduced, highly diastereo- and enantioselective [3 + 2]-cycloaddition of N-cyclopropylurea with α-alkylstyrenes is reported. This asymmetric radical cycloaddition relies on the strategic placement of urea on cyclopropylamine as a redox-active directing group (DG) with anion-binding ability and the use of an ion pair, comprising an iridium polypyridyl complex and a weakly coordinating chiral borate ion, as a photocatalyst. The structure of the anion component of the catalyst governs reactivity, and pertinent structural modification of the borate ion enables high levels of catalytic activity and stereocontrol. This system tolerates a range of α-alkylstyrenes and hence offers rapid access to various aminocyclopentanes with contiguous tertiary and quaternary stereocenters, as the urea DG is readily removable.

Error detection using a convolutional neural network with dose difference maps in patient-specific quality assurance for volumetric modulated arc therapy.

The aim of this study was to evaluate the use of dose difference maps with a convolutional neural network (CNN) to detect multi-leaf collimator (MLC) positional errors in patient-specific quality assurance for volumetric modulated radiation therapy (VMAT). A cylindrical three-dimensional detector (Delta4, ScandiDos, Uppsala, Sweden) was used to measure 161 beams from 104 clinical prostate VMAT plans. For the simulation used error-free plans plus plans with two types of MLC error were introduced: systematic error and random error. A total of 483 dose distributions in a virtual cylindrical phantom were calculated with a treatment planning system. Dose difference maps were created from two planar dose distributions from the measured and calculated dose distributions, and these were used as the input for the CNN, with 375 datasets assigned for training and 108 datasets assigned for testing. The CNN model had three convolution layers and was trained with five-fold cross-validation. The CNN model classified the error types of the plans as "error-free," "systematic error," or "random error," with an overall accuracy of 0.944. The sensitivity values for the "error-free," "systematic error," and "random error" classifications were 0.889, 1.000, and 0.944, respectively, and the specificity values were 0.986, 0.986, and 0.944, respectively. This approach was superior to those based on gamma analysis. Using dose difference maps with a CNN model may provide an effective solution for detecting MLC errors for patient-specific VMAT quality assurance.

Serum S100A8/A9 as a Potentially Sensitive Biomarker for Inflammatory Bowel Disease.

BACKGROUND: The clinical significance of human S100A8/A9 (h-S100A8/A9) in patients with inflammatory bowel disease (IBD) is poorly understood. OBJECTIVE: To clarify whether serum S100A8/A9 is a sensitive biomarker for IBD. METHODS: Serum specimens from outpatients with IBD (n = 101) and healthy volunteers (HVs) (n = 101) were used in this study. Enzyme-linked immunosorbent assays for h-S100A8/A9 and inflammatory cytokines were performed using these specimens. Further, correlation analysis was performed to investigate the significance of h-S100A8/A9 fluctuation in patients with IBD. RESULTS: The average of serum h-S100A8/A9 concentration in outpatients with IBD was significantly higher than that in HVs. The concentration of h-S100A8/A9 in patients with IBD was barely correlated with that of CRP and inflammatory cytokines. Despite that finding, the serum level of h-S100A8/A9 in patients with ulcerative colitis (UC) was correlated with the severity of IBD, compared with other inflammatory proteins. CONCLUSION: Serum h-S100A8/A9 is superior to CRP as a sensitive biomarker for IBD.

Nardilysin inhibits pancreatitis and suppresses pancreatic ductal adenocarcinoma initiation in mice.

OBJECTIVE: Nardilysin (NRDC), a zinc peptidase, exhibits multiple localisation-dependent functions including as an enhancer of ectodomain shedding in the extracellular space and a transcriptional coregulator in the nucleus. In this study, we investigated its functional role in exocrine pancreatic development, homeostasis and the formation of pancreatic ductal adenocarcinoma (PDA). DESIGN: We analysed Ptf1a-Cre; Nrdcflox/flox mice to investigate the impact of Nrdc deletion. Pancreatic acinar cells were isolated from Nrdcflox/flox mice and infected with adenovirus expressing Cre recombinase to examine the impact of Nrdc inactivation. Global gene expression in Nrdc-cKO pancreas was analysed compared with wild-type pancreas by microarray analysis. We also analysed Ptf1a-Cre; KrasG12D; Nrdcflox/flox mice to investigate the impact of Nrdc deletion in the context of oncogenic Kras. A total of 51 human samples of pancreatic intraepithelial lesions (PanIN) and PDA were examined by immunohistochemistry for NRDC. RESULTS: We found that pancreatic deletion of Nrdc leads to spontaneous chronic pancreatitis concomitant with acinar-to-ductal conversion, increased apoptosis and atrophic pancreas in mice. Acinar-to-ductal conversion was observed mainly through a non-cell autonomous mechanism, and the expression of several chemokines was significantly increased in Nrdc-null pancreatic acinar cells. Furthermore, pancreatic deletion of Nrdc dramatically accelerated KrasG12D -driven PanIN and subsequent PDA formation in mice. These data demonstrate a previously unappreciated anti-inflammatory and tumour suppressive functions of Nrdc in the pancreas in mice. Finally, absence of NRDC expression was observed in a subset of human PanIN and PDA. CONCLUSION: Nrdc inhibits pancreatitis and suppresses PDA initiation in mice.