The objective of this study was to clarify the impact of robotic surgery (RS) in the management of mid and low rectal cancer in comparison with open surgery (OS) and laparoscopic surgery (LS). The benefits of RS in the treatment of rectal cancer have not yet been clearly described. Using propensity scores for adjustment of sex, age, body mass index, tumor stage, and tumor height, a well-balanced cohort with 165 patients in each group, was created by matching each patient who underwent RS as the study group with one who underwent OS or LS as the control group (RS:OS = 1:1, RS:LS = 1:1 match). Pathological results, morbidity, perioperative recovery, and short-term oncological results were compared between the 3 groups. In RS and LS, the time to first flatus and resumed soft diet and length of hospital stay were significantly shortened compared with OS. Robotic surgery showed better recovery outcomes than LS with regard to time to resumed soft diet and length of hospital stay. The visual analog scale was significantly lower in the RS than in the OS and LS from postoperative days 1 to 5. The voiding problem and circumferential resection margin involvement rate were significantly lower in the RS group than in the OS group. No significant difference in 2-year disease-free survival was observed among the 3 groups. Robotic surgery may be an effective tool in the effort to maximize the advantages of minimally invasive surgery in the management of mid to low rectal cancer.

Computational histopathology is focused on the automatic analysis of rich phenotypic information contained in gigabyte whole slide images, aiming at providing cancer patients with more accurate diagnosis, prognosis, and treatment recommendations. Nowadays deep learning is the mainstream methodological choice in computational histopathology. Transformer, as the latest technological advance in deep learning, learns feature representations and global dependencies based on self-attention mechanisms, which is increasingly gaining prevalence in this field. This paper presents a comprehensive review of state-of-the-art vision transformers that have been explored in histopathological image analysis for classification, segmentation, and survival risk regression applications. We first overview preliminary concepts and components built into vision transformers. Various recent applications including whole slide image classification, histological tissue component segmentation, and survival outcome prediction with tailored transformer architectures are then discussed. We finally discuss key challenges revolving around the use of vision transformers and envisioned future perspectives. We hope that this review could provide an elaborate guideline for readers to explore vision transformers in computational histopathology, such that more advanced techniques assisting in the precise diagnosis and treatment of cancer patients could be developed.

Tumor mutational burden (TMB) is a genomic biomarker that predicts favorable responses to immune checkpoint inhibitors (ICIs). Here, we set out to assess the predictive value of TMB on long-term survival outcomes in patients undergoing ICIs. We systematically searched PubMed, Embase, CENTRAL and clinicaltrials.gov from inception to 6 August 2019. We included retrospective studies or clinical trials of ICIs that reported hazard ratios (HRs) for overall survival (OS) and/or progression-free survival (PFS) according to TMB. Data on 5712 patients from 26 studies were included. Among patients who received ICIs, high TMB groups showed better OS (HR 0.53, 95% CI 0.42 to 0.67) and PFS (HR 0.52, 95% CI 0.40 to 0.67) compared to low TMB groups. In patients with high TMB, those who received ICIs had a better OS (HR 0.69, 95% CI 0.50 to 0.95) and PFS (HR = 0.66, 95% CI = 0.47 to 0.92) compared to those who received chemotherapy alone, while in patients with low TMB, such ICI benefits of OS or PFS were not statistically significant. In conclusion, TMB may be an effective biomarker to predict survival in patients undergoing ICI treatment. The role of TMB in identifying patient groups who may benefit from ICIs should be determined in future randomized controlled trials.

Background: Myosteatosis has been reported to be a novel biomarker that could predict survival outcomes in patients with colorectal cancer. However, results have been conflicting. This systematic review and meta‐analysis aimed to evaluate the long‐term impact of myosteatosis on the survival of these patients.Methods: A systematic search of PubMed, Embase, and Cochrane up to 27 November 2019 generated 7022 records. Studies that reported hazard ratio (HR) for overall survival, cancer‐specific survival, or disease‐free survival based on myosteatosis or radiodensity were included. A total of 110 full‐text articles were considered for inclusion, and 14 were selected for qualitative analysis. Inverse variance method was used with random effects model for data analysis.Results: The total number of enrolled patients included in the meta‐analysis was 6518 for univariate and 8572 for multivariate HR analysis, from 12 and 10 studies, respectively. Patients with myosteatosis had a significant increase in overall mortality compared with non‐myosteatosis patients by both univariate analysis [HR 1.38, 95% confidence interval (CI) 1.21 to 1.58, P < 0.00001] and multivariate analysis (HR 1.55, 95% CI 1.23 to 1.96, P < 0.00001). In subgroup analysis based on studies that reported HRs of both sarcopenia and myosteatosis, the negative effect of myosteatosis on overall survival was independent of sarcopenia using univariate values (sarcopenia HR 1.48, 95% CI 1.14 to 1.91, P = 0.003 vs. myosteatosis HR 1.51, 95% CI 1.17 to 1.96, P = 0.002) and multivariate values (sarcopenia HR 1.28, 95% CI 1.09 to 1.49, P = 0.002 vs. myosteatosis HR 1.38, 95% CI 1.07 to 1.80, P = 0.001).Conclusions: This meta‐analysis demonstrates that myosteatosis is associated with worse overall survival in patients with colorectal cancer. More investigation is needed to standardize the measurement protocol for myosteatosis and to further optimize its prognostic power for colorectal cancer patients.

Hyperprogressive disease (HPD) is a recently acknowledged pattern of rapid tumor progression after the initiation of immune checkpoint inhibitors. HPD has been observed across various types of tumors and has been associated with poor survival. We performed a meta-analysis to identify baseline (i.e., prior to programmed cell death 1 [PD-1, PDCD1] / programmed cell death 1 ligand 1 [PD-L1, CD274] inhibitor therapy) patient factors associated with risks of developing HPD during PD-1/PD-L1 inhibitor therapy. We searched eight databases until 6 June 2019. We calculated the summary odds ratio (OR) and its 95% confidence interval (CI) using the random-effects model and explored between-study heterogeneity and small-study effects. A total of nine articles was eligible (217 HPD cases, 1519 cancer patients) for meta-analysis. There was no standard definition of HPD, and the incidence of HPD ranged from 1 to 30%. We identified twenty-three baseline patient factors, of which five factors were statistically significantly associated with HPD. These were serum lactate dehydrogenase (LDH) above the upper normal limit (OR = 1.89, 95% CI = 1.02–3.49, p = 0.043), more than two metastatic sites (OR = 1.86, 1.34–2.57, p < 0.001), liver metastases (OR = 3.33, 2.07–5.34, p < 0.001), Royal Marsden Hospital prognostic score of 2 or above (OR = 3.33, 1.96–5.66, p < 0.001), and positive PD-L1 expression status that was inversely correlated with HPD (OR = 0.60, 0.36–0.99, p = 0.044). Between-study heterogeneity was low. Evidence of small-study effect was found in one association (PD-L1 expression). Subset analyses of patients with non-small cell lung cancer showed similar results. Future studies are warranted to identify underlying molecular mechanisms and to test their roles as predictive biomarkers of HPD.