Automatic detection of midfacial fractures in facial bone CT images using deep learning-based object detection models.

BACKGROUND: Midfacial fractures are among the most frequent facial fractures. Surgery is recommended within 2 weeks of injury, but this time frame is often extended because the fracture is missed on diagnostic imaging in the busy emergency medicine setting. Using deep learning technology, which has progressed markedly in various fields, we attempted to develop a system for the automatic detection of midfacial fractures. The purpose of this study was to use this system to diagnose fractures accurately and rapidly, with the intention of benefiting both patients and emergency room physicians. METHODS: One hundred computed tomography images that included midfacial fractures (e.g., maxillary, zygomatic, nasal, and orbital fractures) were prepared. In each axial image, the fracture area was surrounded by a rectangular region to create the annotation data. Eighty images were randomly classified as the training dataset (3736 slices) and 20 as the validation dataset (883 slices). Training and validation were performed using Single Shot MultiBox Detector (SSD) and version 8 of You Only Look Once (YOLOv8), which are object detection algorithms. RESULTS: The performance indicators for SSD and YOLOv8 were respectively: precision, 0.872 and 0.871; recall, 0.823 and 0.775; F1 score, 0.846 and 0.82; average precision, 0.899 and 0.769. CONCLUSIONS: The use of deep learning techniques allowed the automatic detection of midfacial fractures with good accuracy and high speed. The system developed in this study is promising for automated detection of midfacial fractures and may provide a quick and accurate solution for emergency medical care and other settings.

Automatic hip osteoarthritis grading with uncertainty estimation from computed tomography using digitally-reconstructed radiographs.

PURPOSE: Progression of hip osteoarthritis (hip OA) leads to pain and disability, likely leading to surgical treatment such as hip arthroplasty at the terminal stage. The severity of hip OA is often classified using the Crowe and Kellgren-Lawrence (KL) classifications. However, as the classification is subjective, we aimed to develop an automated approach to classify the disease severity based on the two grades using digitally-reconstructed radiographs from CT images. METHODS: Automatic grading of the hip OA severity was performed using deep learning-based models. The models were trained to predict the disease grade using two grading schemes, i.e., predicting the Crowe and KL grades separately, and predicting a new ordinal label combining both grades and representing the disease progression of hip OA. The models were trained in classification and regression settings. In addition, the model uncertainty was estimated and validated as a predictor of classification accuracy. The models were trained and validated on a database of 197 hip OA patients, and externally validated on 52 patients. The model accuracy was evaluated using exact class accuracy (ECA), one-neighbor class accuracy (ONCA), and balanced accuracy. RESULTS: The deep learning models produced a comparable accuracy of approximately 0.65 (ECA) and 0.95 (ONCA) in the classification and regression settings. The model uncertainty was significantly larger in cases with large classification errors ( P < 6 e - 3 ). CONCLUSIONS: In this study, an automatic approach for grading hip OA severity from CT images was developed. The models have shown comparable performance with high ONCA, which facilitates automated grading in large-scale CT databases and indicates the potential for further disease progression analysis. Classification accuracy was correlated with the model uncertainty, which would allow for the prediction of classification errors. The code will be made publicly available at https://github.com/NAIST-ICB/HipOA-Grading .

Hybrid representation-enhanced sampling for Bayesian active learning in musculoskeletal segmentation of lower extremities.

PURPOSE: Manual annotations for training deep learning models in auto-segmentation are time-intensive. This study introduces a hybrid representation-enhanced sampling strategy that integrates both density and diversity criteria within an uncertainty-based Bayesian active learning (BAL) framework to reduce annotation efforts by selecting the most informative training samples. METHODS: The experiments are performed on two lower extremity datasets of MRI and CT images, focusing on the segmentation of the femur, pelvis, sacrum, quadriceps femoris, hamstrings, adductors, sartorius, and iliopsoas, utilizing a U-net-based BAL framework. Our method selects uncertain samples with high density and diversity for manual revision, optimizing for maximal similarity to unlabeled instances and minimal similarity to existing training data. We assess the accuracy and efficiency using dice and a proposed metric called reduced annotation cost (RAC), respectively. We further evaluate the impact of various acquisition rules on BAL performance and design an ablation study for effectiveness estimation. RESULTS: In MRI and CT datasets, our method was superior or comparable to existing ones, achieving a 0.8% dice and 1.0% RAC increase in CT (statistically significant), and a 0.8% dice and 1.1% RAC increase in MRI (not statistically significant) in volume-wise acquisition. Our ablation study indicates that combining density and diversity criteria enhances the efficiency of BAL in musculoskeletal segmentation compared to using either criterion alone. CONCLUSION: Our sampling method is proven efficient in reducing annotation costs in image segmentation tasks. The combination of the proposed method and our BAL framework provides a semi-automatic way for efficient annotation of medical image datasets.

Plasma oxidative state induced by exercise in young heat-not-burn cigarette users.

INTRODUCTION: Oxidative state, a risk factor for several diseases, is increased by habitual conventional cigarette (CC) smoking. Reports have demonstrated that heat-not-burn cigarettes (HNBC), which have recently become popular among smokers, generate less oxidative state than CC in smokers with a long smoking history. However, no previous study has examined oxidative state in young HNBC users. Previously, we reported that exercise induces a greater oxidative state in young CC smokers than in never-smokers of similar age, but there was no difference in resting oxidative state. This study aimed to clarify the resting and exercise-induced oxidative states in young HNBC users, compared with those in never-smokers and CC users of similar age. METHODS: Healthy young never-smokers, HNBC users, and CC users were recruited, and they underwent the Wingate anaerobic test. Blood samples were collected before and after exercise, and the plasma hydroperoxide concentration, a marker of oxidative state, was measured. RESULTS: No significant differences in pre-exercise plasma hydroperoxide concentrations were detected among never-smokers, HNBC users, and CC users (n = 10 each). Plasma hydroperoxide concentration was significantly increased after exercise in all participants. The exercise induced a significant increase in plasma hydroperoxide concentration in HNBC users compared with that in never-smokers (P < .005), but it was significantly decreased compared with that in CC users (P < .01). CONCLUSIONS: The use of HNBC increased exercise-induced plasma oxidative state compared with that in never-smokers, indicating that HNBC may lead to the risk of oxidative damage. IMPLICATIONS: This study, for the first time, reports exercise-induced oxidative state in young heat-not-burn cigarette users compared with never-smokers and conventional cigarette users. The exercise-induced oxidative state in heat-not-burn cigarette users was higher than that in never-smokers and lower than that in conventional cigarette users. Our study suggests that the use of heat-not-burn cigarettes increases the risk of acute oxidative damage.

Automatic orbital segmentation using deep learning-based 2D U-net and accuracy evaluation: A retrospective study.

The purpose of this study was to verify whether the accuracy of automatic segmentation (AS) of computed tomography (CT) images of fractured orbits using deep learning (DL) is sufficient for clinical application. In the surgery of orbital fractures, many methods have been reported to create a 3D anatomical model for use as a reference. However, because the orbit bone is thin and complex, creating a segmentation model for 3D printing is complicated and time-consuming. Here, the training of DL was performed using U-Net as the DL model, and the AS output was validated with Dice coefficients and average symmetry surface distance (ASSD). In addition, the AS output was 3D printed and evaluated for accuracy by four surgeons, each with over 15 years of clinical experience. One hundred twenty-five CT images were prepared, and manual orbital segmentation was performed in all cases. Ten orbital fracture cases were randomly selected as validation data, and the remaining 115 were set as training data. AS was successful in all cases, with good accuracy: Dice, 0.860 ± 0.033 (mean ± SD); ASSD, 0.713 ± 0.212 mm. In evaluating AS accuracy, the expert surgeons generally considered that it could be used for surgical support without further modification. The orbital AS algorithm developed using DL in this study is extremely accurate and can create 3D models rapidly at low cost, potentially enabling safer and more accurate surgeries.

Development and validation of an open-source tool for opportunistic screening of osteoporosis from hip CT images.

Aims: This study aimed to develop and validate a fully automated system that quantifies proximal femoral bone mineral density (BMD) from CT images. Methods: The study analyzed 978 pairs of hip CT and dual-energy X-ray absorptiometry (DXA) measurements of the proximal femur (DXA-BMD) collected from three institutions. From the CT images, the femur and a calibration phantom were automatically segmented using previously trained deep-learning models. The Hounsfield units of each voxel were converted into density (mg/cm3). Then, a deep-learning model trained by manual landmark selection of 315 cases was developed to select the landmarks at the proximal femur to rotate the CT volume to the neutral position. Finally, the CT volume of the femur was projected onto the coronal plane, and the areal BMD of the proximal femur (CT-aBMD) was quantified. CT-aBMD correlated to DXA-BMD, and a receiver operating characteristic (ROC) analysis quantified the accuracy in diagnosing osteoporosis. Results: CT-aBMD was successfully measured in 976/978 hips (99.8%). A significant correlation was found between CT-aBMD and DXA-BMD (r = 0.941; p < 0.001). In the ROC analysis, the area under the curve to diagnose osteoporosis was 0.976. The diagnostic sensitivity and specificity were 88.9% and 96%, respectively, with the cutoff set at 0.625 g/cm2. Conclusion: Accurate DXA-BMD measurements and diagnosis of osteoporosis were performed from CT images using the system developed herein. As the models are open-source, clinicians can use the proposed system to screen osteoporosis and determine the surgical strategy for hip surgery.

Hand-assisted laparoscopic surgery on a gigantic primary retroperitoneal mature cystic teratoma: A case report.

INTRODUCTION: Primary teratomas in the retroperitoneum are rare, incidentally identified in children, and resected using a laparoscopic approach. However, when it increases in size, the laparoscopic approach is technically demanding, leaving a large skin incision for tumor removal. PRESENTATION OF CASE: The patient was a 20-year-old woman who presented with chronic left flank pain. Abdominal and pelvic computed tomography (CT) revealed a 25-cm wide giant polycystic and solid retroperitoneal tumor containing calcification located in the upper portion of the left kidney, strongly compressing the pancreas and spleen. No other metastatic lesions were observed. Additionally, abdominal magnetic resonance imaging (MRI) revealed that the polycystic tumor consisted of serous fluid and fatty components, and bone and tooth components were found in the tumor center. Therefore, the patient was diagnosed with retroperitoneal mature teratoma and a hand-assisted laparoscopic surgery using bikini line skin incision was performed. The specimen was 27 × 25 cm in size, weighing 2512 g. Histology revealed that the tumor was a benign, mature teratoma without a malignant component. The postoperative course was uneventful and the patient was discharged on postoperative day 7. The patient remained healthy without any recurrence and the postoperative scar is barely visible under direct vision. DISCUSSION: Primary retroperitoneal mature teratomas can enlarge without initially causing symptoms and can be incidentally identified using imaging studies. CONCLUSION: A hand-assisted laparoscopic approach using a bikini line skin incision is safe, minimally invasive, and provides better cosmesis.

Artificial intelligence-based volumetric analysis of muscle atrophy and fatty degeneration in patients with hip osteoarthritis and its correlation with health-related quality of life.

PURPOSE: Artificial intelligence (AI) technologies have enabled precise three-dimensional analysis of individual muscles on computed tomography (CT) or magnetic resonance images via automatic segmentation. This study aimed to perform three-dimensional assessments of pelvic and thigh muscle atrophy and fatty degeneration in patients with unilateral hip osteoarthritis using CT and to evaluate the correlation with health-related quality of life (HRQoL). METHODS: The study included one man and 43 women. Six muscle groups were segmented, and the muscle atrophy ratio was calculated volumetrically. The degree of fatty degeneration was defined as the difference between the mean CT values (Hounsfield units [HU]) of the healthy and affected sides. HRQoL was evaluated using the Western Ontario and McMaster Universities Osteoarthritis (WOMAC) index and the Japanese Orthopaedic Association Hip Disease Evaluation Questionnaire (JHEQ). RESULTS: The mean muscle atrophy rate was 16.3%, and the mean degree of muscle fatty degeneration was 7.9 HU. Multivariate correlation analysis revealed that the WOMAC stiffness subscale was significantly related to fatty degeneration of the hamstrings, the WOMAC physical function subscale was significantly related to fatty degeneration of the iliopsoas muscle, and the JHEQ movement subscale was significantly related to fatty degeneration of the hip adductors. CONCLUSION: We found that fatty degeneration of the hamstrings, iliopsoas, and hip adductor muscles was significantly related to HRQoL in patients with hip osteoarthritis. These findings suggest that these muscles should be targeted during conservative rehabilitation for HOA and perioperative rehabilitation for THA.

Rotational-motion measurement of the sacroiliac joint using upright MRI scanning and intensity-based registration: is there a sex difference?

PURPOSE: The sacroiliac joint (SIJ) has attracted increasing attention as a source of low back and groin pain, but the kinematics of SIJ against standing load and its sex difference remain unclear due to the difficulty of in vivo load study. An upright magnetic resonance imaging (MRI) system can provide in vivo imaging both in the supine and standing positions. The reliability of the mobility of SIJ against the standing load was evaluated and its sex difference was examined in healthy young volunteers using an upright MRI. METHOD: Static (reliability) and kinematic studies were performed. In the static study, a dry bone of pelvic ring embedded in gel form and frozen in the plastic box was used. In the kinematic study, 19 volunteers (10 males, 9 females) with a mean age of 23.9 years were included. The ilium positions for the sacrum in supine and standing positions were measured against the pelvic coordinates to evaluate the mobility of the SIJ. RESULTS: In the static study, the residual error of the rotation of the SIJ study was < 0.2°. In the kinematic study, the mean values of SIJ sagittal rotation from supine to standing position in males and females were - 0.9° ± 0.7° (mean ± standard deviation) and - 1.7° ± 0.8°, respectively. The sex difference was statistically significant (p = 0.04). The sagittal rotation of the SIJ showed a significant correlation with the sacral slope. CONCLUSION: The residual error for measuring the SIJ rotation using the upright MRI was < 0.2°. The young healthy participants showed sex differences in the sagittal rotation of the SIJ against the standing load and the females showed a larger posterior rotation of the ilium against the sacrum from the supine to standing position than the males. Therefore, upright MRI is useful to investigate SIJ motion.

[A Case of Goblet Cell Adenocarcinoma Incidentally Diagnosed after Appendectomy That Required Additional Bowel Resection with Lymph Nodes Dissection].

A 54-year-old male presented to the clinic, complaining of dull lower abdominal pain that started a day ago. There was a tenderness on right lower quadrant on palpation and abdominal computed tomography(CT)showed that dilated appendix with a diameter of 12 mm. The patient was diagnosed with acute appendicitis and laparoscopic appendectomy was performed on the same day. The tip of the appendix was swollen and looked purple, gangrenous appendicitis findings were identified. However, histopathology detected GCA on resected appendix with positive surgical margin and additional tumor resection was indicated. Laparoscopic ileocecal resection with D3 lymph nodes dissection was performed 24 days after the first surgery. Resected specimen showed that the stump of the appendix was palpable as a mass in the orifice of the appendix and histopathology revealed the remnant of the appendiceal GCA. No lymph nodes tumor metastasis was identified. Chromogranin A and synaptophysin were positive and Ki-67 was approximately 50%. According to the guideline of neoadjuvant chemotherapy for colon cancer, oral 5-fluorouracil therapy was performed for half a year after the second surgery and the patient remains still healthy without recurrence 1 year after the surgery. Here, we experienced a rare case of GCA of the appendix that was detected incidentally after appendectomy for acute appendicitis.

[A Case of Monomorphic Epitheliotropic Intestinal T-Cell Lymphoma Causing Perforation of the Small Intestine].

Monomorphic epitheliotropic intestinal T-cell lymphoma(MEITL)is very rare and aggressive subtype of lymphoma with poor prognosis. A 60-year-old man complaining of abdominal pain was underwent partial resection of the jejunum for panperitonitis with a small intestinal perforation. The histopathological and immunohistochemical findings led to the diagnosis of MEITL. Postoperative course was uneventful. One month after the operation, the patient was scheduled for 6 courses of CHOP regimens. He presented with bowel obstruction twice during the 3 courses of CHOP. As the recurrence of MEITL could not be ruled out, diagnostic laparoscopy was performed. Laparoscopic findings revealed no recurrence and adhesive small bowel obstruction. The patient was followed closely without treatment after 6 courses of CHOP. The patient has been alive without recurrence 18 months after the resection. We reported a case of monomorphic epithelial intestinal T- cell lymphoma causing jejunal perforation.

Objective stress values during radiation emergency medicine for future human resources: Findings from a survey of nurses.

INTRODUCTION: Shortages of human resources in radiation emergency medicine (REM) caused by the anxiety and stress of due to working in REM, are a major concern. The present study aimed to quantify stress and identify which tasks involved in REM response are most stressful to help educate (human resource development) and effectively reduce stress in workers. Furthermore, the final goal was to reduce the anxiety and stress of medical personnel in the future, which will lead to sufficient human resources in the field of REM. METHODS: In total, 74 nurses who attended an REM seminar were asked to answer a questionnaire (subjective) survey and wear a shirt-type electrocardiogram (objective survey). Then, informed consent was obtained from 39 patients included in the analysis. In the objective survey, average stress values of participants for each activity during the seminar were calculated based on heart rate variability (HRV). The average stress value was output as stress on a relative scale of 0-100, based on the model which is the percentile of the low-frequency/high-frequency ratio derived from HRV at any point in time obtained over time. RESULTS: A total of 35 (89.7%) participants answered that they had little or no knowledge of nuclear disaster and 33 (84.6%) had more than moderate anxiety. Stress values observed during the decontamination process were significantly higher than those observed when wearing and removing protective gear and during the general medical treatment process (P = 0.001, 0.004, and 0.023, respectively). Stress values did not increase during general medical treatment performed in protective clothing, but increased during the decontamination process, which is the task characteristic of REM. DISCUSSION: Stress felt by medical personnel throughout the entire REM response may be effectively reduced by providing careful education/training to reduce stress during the decontamination process. Reducing stress during REM response effectively could contribute to resolving the shortage of human resources in this field.

Artificial intelligence trained with integration of multiparametric MR-US imaging data and fusion biopsy trajectory-proven pathology data for 3D prediction of prostate cancer: A proof-of-concept study.

BACKGROUND: We aimed to develop an artificial intelligence (AI) algorithm that predicts the volume and location of clinically significant cancer (CSCa) using convolutional neural network (CNN) trained with integration of multiparametric MR-US image data and MRI-US fusion prostate biopsy (MRI-US PBx) trajectory-proven pathology data. METHODS: Twenty consecutive patients prospectively underwent MRI-US PBx, followed by robot-assisted radical prostatectomy (RARP). The AI algorithm was trained with the integration of MR-US image data with a MRI-US PBx trajectory-proven pathology. The relationship with the 3D-cancer-mapping of RARP specimens was compared between AI system-suggested 3D-CSCa mapping and an experienced radiologist's suggested 3D-CSCa mapping on MRI alone according to the Prostate Imaging Reporting and Data System (PI-RADS) version 2. The characteristics of detected and undetected tumors at AI were compared in 22,968 image data. The relationships between CSCa volumes and volumes predicted by AI as well as the radiologist's reading based on PI-RADS were analyzed. RESULTS: The concordance of the CSCa center with that in RARP specimens was significantly higher in the AI prediction than the radiologist' reading (83% vs. 54%, p = 0.036). CSCa volumes predicted with AI were more accurate (r = 0.90, p < 0.001) than the radiologist's reading. The limitations include that the elastic fusion technology has its own registration error. CONCLUSIONS: We presented a novel pilot AI algorithm for 3D prediction of PCa. AI was trained by integration of multiparametric MR-US image data and fusion biopsy trajectory-proven pathology data. This deep learning AI model may more precisely predict the 3D mapping of CSCa in its volume and center location than a radiologist's reading based on PI-RADS version 2, and has potential in the planning of focal therapy.

Quantitative Comparison of Surgical Device Usage in Laparoscopic Gastrectomy Between Surgeons' Skill Levels: an Automated Analysis Using a Neural Network.

BACKGROUND: Whether surgical device usage in laparoscopic gastrectomy differs with respect to operator's skill levels is unknown. Further, device usage analysis using artificial intelligence has not been reported to date. Herein, we compared the patterns of surgical device usage during laparoscopic gastrectomy for gastric cancer among surgeons at different skill levels. The data of device usage was acquired from laparoscopic video recordings using an automated surgical-instrument detection system. METHODS: In total, 100 video recordings of infrapyloric lymphadenectomy and 33 of D2 suprapancreatic lymphadenectomy during laparoscopic gastrectomy for gastric cancer were analyzed in this retrospective study. The system's accuracy was evaluated by comparing the automatic and the manual usage time. Surgical device usage patterns were compared between qualified and nonqualified surgeons of The Japan Society for Endoscopic Surgery Endoscopic Surgical Skill Qualification System. RESULTS: For every device, the automatic detection time and manual detection time were consistent with each other. In infrapyloric lymphadenectomy, the usage time proportions of dissector forceps and clip applier were higher among nonqualified operators than among qualified operators (dissector, 5.1% vs. 2.3%, P < 0.001; clip applier, 1.6% vs. 1.3%, P < 0.01). In suprapancreatic lymphadenectomy, the usage time proportions of energy devices, clip applier, and grasper forceps were significantly different (energy devices, 59.6% vs. 50.6%, P < 0.001; clip applier, 1.4% vs. 0.9%, P < 0.001; only grasper forceps; 18.3% vs. 27.9%, P = 0.022). CONCLUSIONS: Quantitative analysis of laparoscopic device usage using the automated surgical device detection system showed that the patterns of device usage during laparoscopic gastrectomy differed depending on surgeons' skill levels. These differences could suggest how the qualified and nonqualified surgeons performed the procedures.

Automated segmentation of an intensity calibration phantom in clinical CT images using a convolutional neural network.

PURPOSE: In quantitative computed tomography (CT), manual selection of the intensity calibration phantom's region of interest is necessary for calculating density (mg/cm3) from the radiodensity values (Hounsfield units: HU). However, as this manual process requires effort and time, the purposes of this study were to develop a system that applies a convolutional neural network (CNN) to automatically segment intensity calibration phantom regions in CT images and to test the system in a large cohort to evaluate its robustness. METHODS: This cross-sectional, retrospective study included 1040 cases (520 each from two institutions) in which an intensity calibration phantom (B-MAS200, Kyoto Kagaku, Kyoto, Japan) was used. A training dataset was created by manually segmenting the phantom regions for 40 cases (20 cases for each institution). The CNN model's segmentation accuracy was assessed with the Dice coefficient, and the average symmetric surface distance was assessed through fourfold cross-validation. Further, absolute difference of HU was compared between manually and automatically segmented regions. The system was tested on the remaining 1000 cases. For each institution, linear regression was applied to calculate the correlation coefficients between HU and phantom density. RESULTS: The source code and the model used for phantom segmentation can be accessed at https://github.com/keisuke-uemura/CT-Intensity-Calibration-Phantom-Segmentation . The median Dice coefficient was 0.977, and the median average symmetric surface distance was 0.116 mm. The median absolute difference of the segmented regions between manual and automated segmentation was 0.114 HU. For the test cases, the median correlation coefficients were 0.9998 and 0.999 for the two institutions, with a minimum value of 0.9863. CONCLUSION: The proposed CNN model successfully segmented the calibration phantom regions in CT images with excellent accuracy.

Hybrid laparoscopic Inokuchi shunt procedure for refractory pleural effusion and ascites associated with primary biliary cirrhosis: A case report.

INTRODUCTION: Portosystemic shunts associated with portal hypertension are generally treated using non-invasive procedures, with open surgery required for refractory cases. Herein, we present a case of refractory pleural fluid and ascites associated with primary biliary cirrhosis (PBC), successfully treated using a hybrid laparoscopic Inokuchi shunt procedure. CASE PRESENTATION: The patient was a 74-year-old woman with a history of PBC, presenting with breathing difficulty. Computed tomography revealed massive pleural fluid and ascites and engorged coronary and azygos veins, indicative of a portosystemic circulation shunt and a 3-cm wide hepatocellular carcinoma (HCC) on liver segment 2. The Child-Pugh score was 11. With the portosystemic shunt outflow considered as the cause of pleural fluid and ascites; therefore, we proceeded with a hybrid laparoscopic selective Inokuchi shunt procedure and tumor enucleation. Laparoscopically, the greater omentum was divided for devascularization, a 7-cm right subcostal skin incision was made, and the abdomen was opened for HCC enucleation under direct vision. The left gastric vein was divided at its junction with the portal vein and connected to the vena cava using a left external iliac vein graft through the omental foramen. After this procedure, the pleural effusion and ascites disappeared, blood ammonium level decreased to normal, and Child-Pugh score decreased to 9. DISCUSSION: Using the Inokuchi shunt procedure, the portosystemic shunt, via the left gastric vein, was closed to increase portal blood flow and improve liver function. CONCLUSION: As a less invasive procedure, hybrid laparoscopic approach should be considered for portosystemic shunt via the left gastric vein.

[A Case of Type 4 Gastric Cancer with Peritoneal Dissemination Successfully Treated with Conversion Surgery after Intensive S-1 plus Oxaliplatin Chemotherapy].

S-1 plus oxaliplatin(SOX)chemotherapy is now widely used for the treatment of unresectable gastric cancer but there are few case reports about conversion surgery following SOX. Hereby, we report a case of type 4 gastric cancer with peritoneal dissemination successfully treated with conversion surgery after intensive SOX chemotherapy. A 69-year-old female was diagnosed of type 4 gastric cancer by upper endoscopy(por1, HER2 negative)and peritoneal disseminations were identified on left diaphragm and mesentery under direct vision. After 11 courses of SOX chemotherapy, CT revealed that primary tumor markedly decreased in size. Therefore, staging laparoscopy was performed and peritoneal disseminated lesions disappeared. Peritoneal cytology also turned negative. Subsequently, total gastrectomy and splenectomy were performed. Histology revealed that tumor was categorized as por2, ypT2N3M0, ypStage ⅢA, and Grade 2 in histological evaluation criteria. SOX was continued as an adjuvant chemotherapy for another 6 months and the patients remain healthy without recurrence. Unresectable gastric cancer with peritoneal dissemination can be successfully treated with conversion surgery following SOX chemotherapy and staging laparoscopy was useful to evaluate peritoneal dissemination. When conversion surgery is indicated for gastric cancer with peritoneal dissemination, downstaging should be confirmed by staging laparoscopy.

[A Case of Multiple Metachronous Metastases from Hepatocellular Carcinoma to Thoracic Spine and Right Adrenal Invading Right Kidney with Tumor Thrombus in the Inferior Vena Cava That Was Treated with Chemotherapy, Radiotherapy, Intravascular Therapy, and Surgery].

For extrahepatic recurrence after primary hepatocellular carcinoma resection, molecular targeted therapy is the first- choice and no consensus is reached on the indication of surgical resection of extrahepatic metastasis. However, when the extrahepatic lesion extends to vena cava, tumor thrombus can cause acute pulmonary embolism that can lead to fatal consequences. Here, we experienced a case of multiple metachronous metastases from hepatocellular carcinoma to thoracic spine and right adrenal invading right kidney with tumor thrombus in the inferior vena cava. Local radiation therapy to thoracic vertebra, molecular targeted therapy, and transcatheter arterial chemoembolization were performed but tumor thrombus still occluded vena cava. Therefore, to prevent pulmonary embolism and to bridge to immunotherapy, right adrenalectomy, right nephrectomy, thrombectomy and replacement of inferior vena cava were performed. The patient remains healthy 6 months after the surgery and still receiving immunochemotherapy.

Automated Surgical Instrument Detection from Laparoscopic Gastrectomy Video Images Using an Open Source Convolutional Neural Network Platform.

BACKGROUND: The common use of laparoscopic intervention produces impressive amounts of video data that are difficult to review for surgeons wishing to evaluate and improve their skills. Therefore, a need exists for the development of computer-based analysis of laparoscopic video to accelerate surgical training and assessment. We developed a surgical instrument detection system for video recordings of laparoscopic gastrectomy procedures. This system, the use of which might increase the efficiency of the video reviewing process, is based on the open source neural network platform, YOLOv3. STUDY DESIGN: A total of 10,716 images extracted from 52 laparoscopic gastrectomy videos were included in the training and validation data sets. We performed 200,000 iterations of training. Video recordings of 10 laparoscopic gastrectomies, independent of the training and validation data set, were analyzed by our system, and heat maps visualizing trends of surgical instrument usage were drawn. Three skilled surgeons evaluated whether each heat map represented the features of the corresponding operation. RESULTS: After training, the testing data set precision and sensitivity (recall) was 0.87 and 0.83, respectively. The heat maps perfectly represented the devices used during each operation. Without reviewing the video recordings, the surgeons accurately recognized the type of anastomosis, time taken to initiate duodenal and gastric dissection, and whether any irregular procedure was performed, from the heat maps (correct answer rates ≥ 90%). CONCLUSIONS: A new automated system to detect manipulation of surgical instruments in video recordings of laparoscopic gastrectomies based on the open source neural network platform, YOLOv3, was developed and validated successfully.