Hong JH, et al. J Korean Med Sci. 2016 Jan;31(1):39-46. http://dx.doi.org/10.3346/jkms.2016.31.1.39

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.

Background: The prognostic role of the translational factor, elongation factor-1 alpha 1 (EEF1A1), in colon cancer is unclear. Objectives: The present study aimed to investigate the expression of EEF1A in tissues obtained from patients with stage II and III colon cancer and analyze its association with patient prognosis. Methods: A total of 281 patients with colon cancer who underwent curative resection were analyzed according to EEF1A1 expression. Results: The five-year overall survival in the high-EEF1A1 group was 87.7%, whereas it was 65.6% in the low-EEF1A1 expression group (hazard ratio (HR) 2.47, 95% confidence interval (CI) 1.38–4.44, p = 0.002). The five-year disease-free survival of patients with high EEF1A1 expression was 82.5%, which was longer than the rate of 55.4% observed for patients with low EEF1A1 expression (HR 2.94, 95% CI 1.72–5.04, p < 0.001). Univariate Cox regression analysis indicated that age, preoperative carcinoembryonic antigen level, adjuvant treatment, total number of metastatic lymph nodes, and EEF1A1 expression level were significant prognostic factors for death. In multivariate analysis, expression of EEF1A1 was an independent prognostic factor associated with death (HR 3.01, 95% CI 1.636–5.543, p < 0.001). EEF1A1 expression was also an independent prognostic factor for disease-free survival in multivariate analysis (HR 2.54, 95% CI 1.459–4.434, p < 0.001). Conclusions: Our study demonstrated that high expression of EEF1A1 has a favorable prognostic effect on patients with colon adenocarcinoma.

Background: Propofol-based total intravenous anesthesia (TIVA) improves long-term outcomes after cancer surgery compared with inhalation anesthesia. However, its effect on patients undergoing non-small cell lung cancer (NSCLC) surgery remains unclear. We aimed to compare the oncological outcomes of TIVA and inhalation anesthesia after curative resection of early-stage NSCLC. Methods: We analyzed the medical records of patients diagnosed with stage I or II NSCLC who underwent curative resection at a tertiary university hospital between January 2010 and December 2017. The primary outcomes were recurrence-free survival (RFS) and overall survival (OS) according to anesthesia type.Results: We included 1,508 patients with stage I/II NSCLC. The patients were divided into the TIVA (n = 980) and Inhalation (n = 528) groups. The two groups were well-balanced in terms of baseline clinical characteristics. The TIVA group demonstrated significantly improved RFS (7.7 years, 95% CI [7.37, 8.02]) compared with the Inhalation group (6.8 years, 95% CI [6.30, 7.22], P = 0.003). Similarly, TIVA was superior to inhalation agents with respect to OS (median OS; 8.4 years, 95% CI [8.08, 8.69] vs. 7.3 years, 95% CI [6.81, 7.71]; P < 0.001). Multivariable Cox regression analysis revealed that TIVA was an independent prognostic factor related to recurrence (hazard ratio [HR]: 1.24, 95% CI [1.04, 1.47], P = 0.014) and OS (HR: 1.39, 95% CI [1.12, 1.72], P = 0.002).Conclusions: Propofol-based TIVA was associated with better RFS and OS than inhalation anesthesia in patients with stage I/II NSCLC who underwent curative resection.

We investigated the prognostic role of metabolic parameters from preoperative ¹⁸F-FDG PET/CT in stage II/III colorectal adenocarcinoma. A total of 327 stage II/III colorectal adenocarcinoma patients who underwent curative resection were included. The maximal standard uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were analyzed for optimal cut-offs and their effect on DFS. Differences in DFS rates and hazard ratios for DFS between cut-offs were statistically significant in SUVmax, MTV2.5, MTV3, TLG 2.5, TLG3, and TLG30%. Factors significantly related to DFS in univariate Cox regression were age, sex, stage, preoperative CEA, SUVmax, MTV2.5, MTV3, TLG2.5, TLG3, and TLG30%. Age, sex, preoperative CEA, and TLG2.5 (p = 0.009) sustained statistically significant difference in multivariate analysis. The 1-, 3-, and 5-year DFS rates for TLG2.5 ≤ 448.5 were 98.1%, 79.6%, and 74.8%, significantly higher than 78.4%, 68.5%, and 61.1% of TLG2.5 > 448.5, respectively (p = 0.012). TLG, a parameter indicating both the metabolic activity and metabolic volume, was the strongest predictor independently associated with DFS, among several PET parameters with statistical significance. These results suggest the potential prognostic value of preoperative ¹⁸F-FDG PET/CT in stage II/III resectable colorectal cancer.

Hyung Sun Kim, Mee Joo Kang, Jingu Kang, Kyubo Kim, Bohyun Kim, Seong-Hun Kim, Soo Jin Kim, Yong-Il Kim, Joo Young Kim, Jin Sil Kim, Haeryoung Kim, Hyo Jung Kim, Ji Hae Nahm, Won Suk Park, Eunkyu Park, Joo Kyung Park, Jin Myung Park, Byeong Jun Song, Yong Chan Shin, Keun Soo Ahn, Sang Myung Woo, Jeong Il Yu, Changhoon Yoo, Kyoungbun Lee, Dong Ho Lee, Myung Ah Lee, Seung Eun Lee, Ik Jae Lee, Huisong Lee, Jung Ho Im, Kee-Taek Jang, Hye Young Jang, Sun-Young Jun, Hong Jae Chon, Min Kyu Jung, Yong Eun Chung, Jae Uk Chong, Eunae Cho, Eui Kyu Chie, Sae Byeol Choi, Seo-Yeon Choi, Seong Ji Choi, Joon Young Choi, Hye-Jeong Choi, Seung-Mo Hong, Ji Hyung Hong, Tae Ho Hong, Shin Hye Hwang, In Gyu Hwang, Joon Seong Park. Ann Hepatobiliary Pancreat Surg 2024;28:161-202. https://doi.org/10.14701/ahbps.23-170