Postoperative long­term outcomes of acute normovolemic hemodilution in pancreatic cancer: A propensity score matching analysis.

Acute normovolemic hemodilution (ANH) is a useful intraoperative blood conservation technique. However, the impact on long-term outcomes in pancreatic ductal adenocarcinoma (PDAC) remains unclear. The present study investigated the impact of ANH on long-term outcomes in patients with PDAC undergoing radical surgery. Data from 155 resectable PDAC cases were collected. Patients were categorized according to whether or not they had received intraoperative allogeneic blood transfusion (ABT) or ANH. Postoperative complications, recurrence-free survival (RFS) and disease-specific survival (DSS), before and after propensity score matching (PSM), were compared among patients who did and did not receive ANH. A total of 44 patients (28.4%) were included in the ANH group and 30 patients (19.4%) were included in the ABT group; 81 (52.3%) patients, comprising the standard management (STD) group, received neither ANH nor ABT. The ABT group had the worst prognosis among them. Before PSM, ANH was significantly associated with decreased RFS (P=0.043) and DSS (P=0.029) compared with the STD group before applying Bonferroni correction; however, no significant difference was observed after applying Bonferroni correction. Cox regression analysis identified ANH as an independent prognostic factor for RFS [relative risk (RR), 1.696; P=0.019] and DSS (RR, 1.876; P=0.009). After PSM, the ANH group exhibited less favorable RFS [median survival time (MST), 12.1 vs. 18.1 months; P=0.097] and DSS (MST, 32.1 vs. 50.5 months; P=0.097) compared with the STD group; however, these differences were not statistically significant. In conclusion, while ANH was not as harmful as ABT, it exhibited potentially more negative effects on long-term postoperative outcomes in PDAC than STD.

Noninvasive Computed Tomography-Based Deep Learning Model Predicts In Vitro Chemosensitivity Assay Results in Pancreatic Cancer.

OBJECTIVES: We aimed to predict in vitro chemosensitivity assay results from computed tomography (CT) images by applying deep learning (DL) to optimize chemotherapy for pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS: Preoperative enhanced abdominal CT images and the histoculture drug response assay (HDRA) results were collected from 33 PDAC patients undergoing surgery. Deep learning was performed using CT images of both the HDRA-positive and HDRA-negative groups. We trimmed small patches from the entire tumor area. We established various prediction labels for HDRA results with 5-fluorouracil (FU), gemcitabine (GEM), and paclitaxel (PTX). We built a predictive model using a residual convolutional neural network and used 3-fold cross-validation. RESULTS: Of the 33 patients, effective response to FU, GEM, and PTX by HDRA was observed in 19 (57.6%), 11 (33.3%), and 23 (88.5%) patients, respectively. The average accuracy and the area under the receiver operating characteristic curve (AUC) of the model for predicting the effective response to FU were 93.4% and 0.979, respectively. In the prediction of GEM, the models demonstrated high accuracy (92.8%) and AUC (0.969). Likewise, the model for predicting response to PTX had a high performance (accuracy, 95.9%; AUC, 0.979). CONCLUSIONS: Our CT patch-based DL model exhibited high predictive performance in projecting HDRA results. Our study suggests that the DL approach could possibly provide a noninvasive means for the optimization of chemotherapy.

Differential diagnoses of gallbladder tumors using CT-based deep learning.

Background: The differential diagnosis between gallbladder cancer (GBC) and xanthogranulomatous cholecystitis (XGC) remains quite challenging, and can possibly lead to improper surgery. This study aimed to distinguish between XGC and GBC by combining computed tomography (CT) images and deep learning (DL) to maximize the therapeutic success of surgery. Methods: We collected a dataset, including preoperative CT images, from 28 cases of GBC and 21 XGC patients undergoing surgery at our facility. It was subdivided into training and validation (n = 40), and test (n = 9) datasets. We built a CT patch-based discriminating model using a residual convolutional neural network and employed 5-fold cross-validation. The discriminating performance of the model was analyzed in the test dataset. Results: Of the 40 patients in the training dataset, GBC and XGC were observed in 21 (52.5%), and 19 (47.5%) patients, respectively. A total of 61 126 patches were extracted from the 40 patients. In the validation dataset, the average sensitivity, specificity, and accuracy were 98.8%, 98.0%, and 98.5%, respectively. Furthermore, the area under the receiver operating characteristic curve (AUC) was 0.9985. In the test dataset, which included 11 738 patches, the discriminating accuracy for GBC patients after neoadjuvant chemotherapy (NAC) (n = 3) was insufficient (61.8%). However, the discriminating model demonstrated high accuracy (98.2%) and AUC (0.9893) for cases other than those receiving NAC. Conclusion: Our CT-based DL model exhibited high discriminating performance in patients with GBC and XGC. Our study proposes a novel concept for selecting the appropriate procedure and avoiding unnecessary invasive measures.

Heterogeneity of metabolic adaptive capacity affects the prognosis among pancreatic ductal adenocarcinomas.

BACKGROUND: Evolutionary cancer has a supply mechanism to satisfy higher energy demands even in poor-nutrient conditions. Metabolic reprogramming is essential to supply sufficient energy. The relationship between metabolic reprogramming and the clinical course of pancreatic ductal adenocarcinoma (PDAC) remains unclear. We aimed to clarify the differences in metabolic status among PDAC patients. METHODS: We collected clinical data from 128 cases of resectable PDAC patients undergoing surgery. Sixty-three resected tissues, 15 tissues from the low carbohydrate antigen 19-9 (CA19-9), 38-100 U/mL, and high CA19-9, > 500 U/mL groups, and 33 non-tumor control parts, were subjected to tandem mass spectrometry workflow to systematically explore metabolic status. Clinical and proteomic data were compared on the most used PDAC biomarker, preoperative CA19-9 value. RESULTS: Higher CA19-9 levels were clearly associated with higher early recurrence (p < 0.001), decreased RFS (p < 0.001), and decreased DSS (p = 0.025). From proteomic analysis, we discovered that cancer evolution-related as well as various metabolism-related pathways were more notable in the high group. Using resected tissue immunohistochemical staining, we learned that high CA19-9 PDAC demonstrated aerobic glycolysis enhancement, yet no decrease in protein synthesis. We found a heterogeneity of various metabolic processes, including carbohydrates, proteins, amino acids, lipids, and nucleic acids, between the low and the high groups, suggesting differences in metabolic adaptive capacity. CONCLUSIONS: Our study found metabolic adaptation differences among PDAC cases, pertaining to both cancer evolution and the prognosis. CA19-9 can help estimate the metabolic adaptive capacity of energy supply for PDAC evolution.

CT-based deep learning enables early postoperative recurrence prediction for intrahepatic cholangiocarcinoma.

Preoperatively accurate evaluation of risk for early postoperative recurrence contributes to maximizing the therapeutic success for intrahepatic cholangiocarcinoma (iCCA) patients. This study aimed to investigate the potential of deep learning (DL) algorithms for predicting postoperative early recurrence through the use of preoperative images. We collected the dataset, including preoperative plain computed tomography (CT) images, from 41 patients undergoing curative surgery for iCCA at multiple institutions. We built a CT patch-based predictive model using a residual convolutional neural network and used fivefold cross-validation. The prediction accuracy of the model was analyzed. We defined early recurrence as recurrence within a year after surgical resection. Of the 41 patients, early recurrence was observed in 20 (48.8%). A total of 71,081 patches were extracted from the entire segmented tumor area of each patient. The average accuracy of the ResNet model for predicting early recurrence was 98.2% for the training dataset. In the validation dataset, the average sensitivity, specificity, and accuracy were 97.8%, 94.0%, and 96.5%, respectively. Furthermore, the area under the receiver operating characteristic curve was 0.994. Our CT-based DL model exhibited high predictive performance in projecting postoperative early recurrence, proposing a novel insight into iCCA management.

Development of a Biomarker-Based Scoring System Predicting Early Recurrence of Resectable Pancreatic Duct Adenocarcinoma.

BACKGROUND: Resectable pancreatic ductal adenocarcinoma (R-PDAC) often recurs early after radical resection, which is associated with poor prognosis. Predicting early recurrence preoperatively is useful for determining the optimal treatment. PATIENTS AND METHODS: One hundred and seventy-eight patients diagnosed with R-PDAC on computed tomography (CT) imaging and undergoing radical resection at Hirosaki University Hospital from 2005 to 2019 were retrospectively analyzed. Patients with recurrence within 6 months after resection formed the early recurrence (ER) group, while other patients constituted the non-early recurrence (non-ER) group. Early recurrence prediction score (ERP score) was developed using preoperative parameters. RESULTS: ER was observed in 45 patients (25.3%). The ER group had significantly higher preoperative CA19-9 (p = 0.03), serum SPan-1 (p = 0.006), and CT tumor diameter (p = 0.01) compared with the non-ER group. The receiver operating characteristic (ROC) curve analysis identified cutoff values for CA19-9 (133 U/mL), SPan-1 (78.2 U/mL), and preoperative tumor diameter (23 mm). When the parameter exceeded the cutoff level, 1 point was given, and the total score of the three factors was defined as the ERP score. The group with an ERP score of 3 had postoperative recurrence-free survival (RFS) of 5.5 months (95% CI 3.02-7.98). Multivariate analysis for ER-related perioperative and surgical factors identified ERP score of 3 [odds ratio (OR) 4.63 (95% CI 1.82-11.78), p = 0.0013] and R1 resection [OR 3.20 (95% CI 1.01-10.17), p = 0.049] as independent predictors of ER. CONCLUSIONS: For R-PDAC, ER could be predicted by the scoring system using preoperative serum CA19-9 and SPan-1 levels and CT tumor diameter, which may have great significance in identifying patients with poor prognoses and avoiding unnecessary surgery.

Intraoperative Allogeneic Red Blood Cell Transfusion Negatively Influences Prognosis After Radical Surgery for Pancreatic Cancer: A Propensity Score Matching Analysis.

OBJECTIVE: We aimed to investigate the real impact of allogeneic red blood cell transfusion (ABT) on postoperative outcomes in resectable pancreatic ductal adenocarcinoma (PDAC) patients. METHODS: Of 128 patients undergoing resectable PDAC surgery at our facility, 24 (18.8%) received ABT. Recurrence-free survival (RFS) and disease-specific survival (DSS), before and after propensity score matching (PSM), were compared among patients who did and did not receive ABT. RESULTS: In the entire cohort, ABT was significantly associated with decreased RFS (P = 0.002) and DSS (P = 0.014) before PSM. Cox regression analysis identified ABT (risk ratio, 1.884; 95% confidence interval, 1.015-3.497; P = 0.045) as an independent prognostic factor for RFS. Univariate and multivariate analysis identified preoperative hemoglobin value, preoperative total bilirubin value, and intraoperative blood loss as significant independent risk factors for ABT. Using these 3 variables, PSM analysis created 16 pairs of patients. After PSM, the ABT group had significantly poorer RFS rates than the non-ABT group (median, 9.8 vs 15.8 months, P = 0.022). Similar tendencies were found in DSS rates (median, 19.4 vs 40.0 months, P = 0.071). CONCLUSIONS: This study revealed certain negative effects of intraoperative ABT on postoperative survival outcomes in patients with resectable PDAC.

Prediction of massive bleeding in pancreatic surgery based on preoperative patient characteristics using a decision tree.

Massive intraoperative blood loss (IBL) negatively influence outcomes after surgery for pancreatic ductal adenocarcinoma (PDAC). However, few data or predictive models are available for the identification of patients with a high risk for massive IBL. This study aimed to build a model for massive IBL prediction using a decision tree algorithm, which is one machine learning method. One hundred and seventy-five patients undergoing curative surgery for resectable PDAC at our facility between January 2007 and October 2020 were allocated to training (n = 128) and testing (n = 47) sets. Using the preoperatively available data of the patients (34 variables), we built a decision tree classification algorithm. Of the 175 patients, massive IBL occurred in 88 patients (50.3%). Binary logistic regression analysis indicated that alanine aminotransferase and distal pancreatectomy were significant predictors of massive IBL occurrence with an overall correct prediction rate of 70.3%. Decision tree analysis automatically selected 14 predictive variables. The best predictor was the surgical procedure. Though massive IBL was not common, the outcome of patients with distal pancreatectomy was secondarily split by glutamyl transpeptidase. Among patients who underwent PD (n = 83), diabetes mellitus (DM) was selected as the variable in the second split. Of the 21 patients with DM, massive IBL occurred in 85.7%. Decision tree sensitivity was 98.5% in the training data set and 100% in the testing data set. Our findings suggested that a decision tree can provide a new potential approach to predict massive IBL in surgery for resectable PDAC.

Eukaryotic initiation factor 2 signaling behind neural invasion linked with lymphatic and vascular invasion in pancreatic cancer.

Perineural invasion (PNI) is a typical poor prognostic factor in pancreatic ductal adenocarcinoma (PDAC). The mechanisms linking PNI to poor prognosis remain unclear. This study aimed to clarify what changes occurred alongside PNI in PDAC. A 128-patient cohort undergoing surgery for early-stage PDAC was evaluated. Subdivided into two groups, according to pathological state, a pancreatic nerve invasion (ne) score of less than three (from none to moderate invasion) was designated as the low-grade ne group. The high-grade (marked invasion) ne group (74 cases, 57.8%) showed a higher incidence of lymphatic metastasis (P = 0.002), a higher incidence of early recurrence (P = 0.004), decreased RFS (P < 0.001), and decreased DSS (P < 0.001). The severity of lymphatic (r = 0.440, P = 0.042) and venous (r = 0.610, P = 0.002) invasions was positively correlated with the ne score. Tumors having abundant stroma often displayed severe ne. Proteomics identified eukaryotic initiation factor 2 (EIF2) signaling as the most significantly enriched pathway in high-grade ne PDAC. Additionally, EIF2 signaling-related ribosome proteins decreased according to severity. Results showed that PNI is linked with lymphatic and vascular invasion in early-stage PDAC. Furthermore, the dysregulation of proteostasis and ribosome biogenesis can yield a difference in PNI severity.

Roles of the nervous system in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC), with its extremely poor prognosis, presents a substantial health problem worldwide. Outcomes have improved thanks to progress in surgical technique, chemotherapy, pre-/postoperative management, and centralization of patient care to high-volume centers. However, our goals are yet to be met. Recently, exome sequencing using PDAC surgical specimens has demonstrated that the most frequently altered genes were the axon guidance genes, indicating involvement of the nervous system in PDAC carcinogenesis. Moreover, perineural invasion has been widely identified as one poor prognostic factor. The combination of innovative technologies and extensive clinician experience with the nervous system come together here to create a new treatment option. However, evidence has emerged that suggests that the relationship between cancer and nerves in PDAC, the underlying mechanism, is not fully understood. In an attempt to tackle this lethal cancer, this review summarizes the anatomy and physiology of the pancreas and discusses the role of the nervous system in the pathophysiology of PDAC.

[A Case of Descending Colon and Rectal Cancer with Acute Myeloid Leukemia Performed Robot‒Assisted Hartmann's Procedure].

The case is a 68‒year‒old male, who had been diagnosed with acute myeloid leukemia(AML)prior to rectal cancer surgery, was referred to our hospital for treatment in July 2019. We planned to treat the AML first, and then the colorectal cancer. After completion of 1 course of CAG therapy(cytarabine, aclarubicin, G‒CSF), his white blood cell count increased sufficiently, so he underwent a robot‒assisted Hartmann operation in October. A second course of CAG therapy was started 15 days postoperatively. However, he was then diagnosed with exacerbation of the AML; remission induction therapy (daunorubicin, cytarabine)was started in November. In December, he developed a fever and abdominal pain, and on CT scan, it was discovered that an abscess had formed around the rectal resection site. Myelosuppression from AML led to prolonged sepsis; and by January 2020, the sepsis was systemic. His actual cause of death was given as circulatory failure. We report this, because only a few cases on the treatment of overlapping AML and colorectal cancers can be found in the literature.

[A Case of Two Stage taTME for Perforated Rectal Cancer during Chemotherapy].

The patient was a 57-year-old male. He was diagnosed with locally advanced rectal cancer infiltrating the left levator ani muscle. Chemotherapy(S-1 plus L-OHP plus bevacizumab regimen)was started for the purpose of obtaining a negative circumferential radial margin. After the second course, he presented with perforation of the sigmoid colon for which an emergency operation was performed. The perforation was located 5 centimeters above the tumor in the sigmoid colon. We performed partial resection of the sigmoid colon to repair the perforation and create a sigmoid colostomy. CT, after the initial S-1 plus L-OHP plus bevacizumab chemotherapy regimen, revealed tumor shrinkage. Following 2 more courses of chemotherapy( S-1 plus L-OHP regimen), we performed transanal total mesenteric excision(taTME)as curative surgery. R0 resection was achieved. The combined transanal and laparoscopic approach was highly effective for a patient with pan-peritonitis.