Surgical techniques in the management of rectal cancer: a modified Delphi method by colorectal surgeons in Australia and New Zealand

Factors contributing to the utilization of robotic colorectal surgery: a systematic review and meta-analysis

BACKGROUND

Some studies have suggested disparities in access to robotic colorectal surgery, however, it is unclear which factors are most meaningful in the determination of approach relative to laparoscopic or open surgery. This study aimed to identify the most influential factors contributing to robotic colorectal surgery utilization.

METHODS

We conducted a systematic review and random-effects meta-analysis of published studies that compared the utilization of robotic colorectal surgery versus laparoscopic or open surgery. Eligible studies were identified through PubMed, EMBASE, CINAHL, Cochrane CENTRAL, PsycINFO, and ProQuest Dissertations in September 2021.

RESULTS

Twenty-nine studies were included in the analysis. Patients were less likely to undergo robotic versus laparoscopic surgery if they were female (OR = 0.91, 0.84-0.98), older (OR = 1.61, 1.38-1.88), had Medicare (OR = 0.84, 0.71-0.99), or had comorbidities (OR = 0.83, 0.77-0.91). Non-academic hospitals had lower odds of conducting robotic versus laparoscopic surgery (OR = 0.73, 0.62-0.86). Additional disparities were observed when comparing robotic with open surgery for patients who were Black (OR = 0.78, 0.71-0.86), had lower income (OR = 0.67, 0.62-0.74), had Medicaid (OR = 0.58, 0.43-0.80), or were uninsured (OR = 0.29, 0.21-0.39).

CONCLUSION

When determining who undergoes robotic surgery, consideration of factors such as age and comorbid conditions may be clinically justified, while other factors seem less justifiable. Black patients and the underinsured were less likely to undergo robotic surgery. This study identifies nonclinical disparities in access to robotics that should be addressed to provide more equitable access to innovations in colorectal surgery.

TMSF-Net: Multi-series fusion network with treeconnect for colorectal tumor segmentation

PURPOSE

Colorectal tumors are common clinical diseases. Automatic segmentation of colorectal tumors captured in computed tomography (CT) images can provide numerous possibilities for computer-assisted treatment. Obtaining large datasets is expensive, and completing labeling is time- and manpower-consuming. To solve the challenge using a limited pathological dataset, this paper proposes a multi-series fusion network with treeconnect (TMSF-Net), which can automatically achieve colorectal tumor segmentation using CT images.

METHODS

To drive the TMSF-Net, three-series enhanced CT images were collected from all patients to improve the data characteristics. In the TMSF-Net, the coding path was designed as a three-branch structure to realize the feature extraction of the different series. Subsequently, the three branches were merged to start the feature analysis in the decoding path. To achieve the objective of feature fusion, different layers in the decoding path fused feature maps from the upper layer in the encoding path to achieve a cross-scale fusion. In addition, to reduce the problem of parameter redundancy, this study adopted a three-dimensional treeconnect to complete data connection on three branches.

RESULTS

A total of 22 cases were conducted by ablation and comparative experiments to test the TMSF-Net. The results showed that the TMSF-Net can improve the network performance by multiseries fusion, and its expressiveness is better than many classic networks.

CONCLUSION

The TMSF-Net is a many-to-one structure network, which can enhance the network learning ability and improve the analysis of potential features. Therefore, it yields good results in colorectal tumor segmentation. It can provide a new direction for neural network models based on feature fusion.