A novel artificial intelligence-based endoscopic ultrasonography diagnostic system for diagnosing the invasion depth of early gastric cancer.

BACKGROUND: We developed an artificial intelligence (AI)-based endoscopic ultrasonography (EUS) system for diagnosing the invasion depth of early gastric cancer (EGC), and we evaluated the performance of this system. METHODS: A total of 8280 EUS images from 559 EGC cases were collected from 11 institutions. Within this dataset, 3451 images (285 cases) from one institution were used as a development dataset. The AI model consisted of segmentation and classification steps, followed by the CycleGAN method to bridge differences in EUS images captured by different equipment. AI model performance was evaluated using an internal validation dataset collected from the same institution as the development dataset (1726 images, 135 cases). External validation was conducted using images collected from the other 10 institutions (3103 images, 139 cases). RESULTS: The area under the curve (AUC) of the AI model in the internal validation dataset was 0.870 (95% CI: 0.796-0.944). Regarding diagnostic performance, the accuracy/sensitivity/specificity values of the AI model, experts (n = 6), and nonexperts (n = 8) were 82.2/63.4/90.4%, 81.9/66.3/88.7%, and 68.3/60.9/71.5%, respectively. The AUC of the AI model in the external validation dataset was 0.815 (95% CI: 0.743-0.886). The accuracy/sensitivity/specificity values of the AI model (74.1/73.1/75.0%) and the real-time diagnoses of experts (75.5/79.1/72.2%) in the external validation dataset were comparable. CONCLUSIONS: Our AI model demonstrated a diagnostic performance equivalent to that of experts.

A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria.

BACKGROUND: We developed a machine learning (ML) model to predict the risk of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) who did not meet the existing Japanese endoscopic curability criteria and compared its performance with that of the most common clinical risk scoring system, the eCura system. METHODS: We used data from 4,042 consecutive patients with EGC from 21 institutions who underwent endoscopic submucosal dissection (ESD) and/or surgery between 2010 and 2021. All resected EGCs were histologically confirmed not to satisfy the current Japanese endoscopic curability criteria. Of all patients, 3,506 constituted the training cohort to develop the neural network-based ML model, and 536 constituted the validation cohort. The performance of our ML model, as measured by the area under the receiver operating characteristic curve (AUC), was compared with that of the eCura system in the validation cohort. RESULTS: LNM rates were 14% (503/3,506) and 7% (39/536) in the training and validation cohorts, respectively. The ML model identified patients with LNM with an AUC of 0.83 (95% confidence interval, 0.76-0.89) in the validation cohort, while the eCura system identified patients with LNM with an AUC of 0.77 (95% confidence interval, 0.70-0.85) (P = 0.006, DeLong's test). CONCLUSIONS: Our ML model performed better than the eCura system for predicting LNM risk in patients with EGC who did not meet the existing Japanese endoscopic curability criteria. We developed a neural network-based machine learning model that predicts the risk of lymph node metastasis in patients with early gastric cancer who did not meet the endoscopic curability criteria.

Water-slider method reduced colonic polyp fragmentation compared with conventional suction retrieval: A prospective randomized controlled study.

OBJECTIVES: After resecting colonic polyps, retrieval through the scope channel may lead to polyp fragmentation, making pathologic evaluation challenging. An easy and reliable method for complete polyp retrieval is needed. METHODS: We developed the water-slider method (WSM), in which the resected polyp is surrounded by water from an auxiliary water channel during suctioning. We prospectively randomized patients who underwent endoscopic resection for colonic polyps in our institute into WSM and non-WSM groups, and evaluated the polyp fragmentation rate. RESULTS: Analysis of the data regarding small polyps (≤10 mm in size) revealed that the WSM group had a significantly lower polyp fragmentation rate (8.2%) than the non-WSM group (23.8%, p < 0.001). Polyp retrieval time did not differ significantly between groups. The rate of a clear-cut end on neoplastic polyps was significantly higher in the WSM group (63.8%) than in the non-WSM group (50.0%; p = 0.029). CONCLUSIONS: The WSM achieved a significantly lower polyp fragmentation rate, allowing for more accurate histologic evaluation than the conventional method. The WSM did not influence the polyp retrieval time.

Acute compartment syndrome as the initial manifestation of chronic-phase chronic myeloid leukemia: a case report and review of the literature.

BACKGROUND: Acute compartment syndrome is an orthopedic emergency requiring urgent fasciotomy to prevent irreversible damage. In hematological malignancies, acute compartment syndrome caused by severe soft tissue bleeding is extremely rare. We present a patient with chronic-phase chronic myeloid leukemia who had acute compartment syndrome caused by severe soft tissue bleeding in her right forearm. CASE PRESENTATION: A 72-year-old Japanese woman was referred to our hospital with swelling and pain of her right forearm without a previous history of trauma. She was diagnosed with chronic-phase chronic myeloid leukemia. Extreme thrombocytosis was present, although no evidence of acquired von Willebrand disorder was found. Compartment syndrome caused by soft tissue bleeding was confirmed. An emergency fasciotomy for decompression was conducted. However, sustained postoperative bleeding occurred and required massive red cell concentrate transfusion. As her platelet count decreased by cytoreductive therapy, complete hemostasis was achieved. CONCLUSIONS: Patients with an extremely high platelet count might be at high risk for severe bleeding complications even without acquired von Willebrand disease. For the control of severe bleeding complications in patients with myeloproliferative disorder, the importance of thrombocyte reduction should be recognized.

Diuretics aggravate zinc deficiency in patients with liver cirrhosis by increasing zinc excretion in urine.

AIM: Liver cirrhosis is often accompanied by zinc deficiency. The exact mechanisms underlying zinc deficiency remain unclear. This study was undertaken to clarify the influence of diuretics on blood zinc levels and zinc excretion in urine in liver cirrhosis. METHODS: Seventy-nine outpatients with liver cirrhosis were divided into four groups: (i) patients receiving no zinc preparations or diuretics (LC group); (ii) those receiving zinc preparations only (LCZ group); (iii) those receiving diuretics only (LCD group); and (iv) those receiving both zinc preparations and diuretics (LCDZ group). Among these groups, the effects of the administrated drugs on blood zinc levels and urinary zinc excretion were analyzed. RESULTS: Blood zinc levels were significantly lower in the LCD group (47.8 ± 10.5 µg/dL) than in the other groups (LC: 68.8 ± 17.1 µg/dL, P = 0.0056, post-hoc test; LCZ: 78.4 ± 18.1, P < 0.0001; LCDZ: 70.3 ± 21.4, P = 0.0008). The creatinine-adjusted urinary zinc excretion was significantly higher in the LCDZ group (548.1 ± 407.6 µg/mg creatinine) than in the other groups (LC, 58.5 ± 43.7; LCZ, 208.1 ± 227.8; LCD, 105.2 ± 154.4; each P < 0.0001). The fraction of urinary zinc excretion was also significantly higher in the LCDZ group (5.6 ± 2.9%) than in the other groups (LC, 0.6 ± 0.5; LCD, 1.7 ± 1.5; LCZ, 1.6 ± 1.2; each P < 0.0001). CONCLUSION: In patients with liver cirrhosis, treatment with diuretics can increase zinc excretion by suppressing the reabsorption of zinc through renal tubules, which might lead to zinc deficiency.