Lateral pelvic lymph node dissection (LPLND) provides oncologic benefits in patients with rectal cancer who have enlarged lateral nodes. However, anatomical complexity in the lateral pelvis makes the procedure technically challenging,  which may lead to  increased intraoperative blood loss, prolonged operative time, postoperative complications and incomplete lymph node dissection. To address such technical  challenges, various surgical navigation tools have been developed. In this up-to-date narrative review, we summarize the current evidence on surgical navigation for LPLND and discuss their advantages,  limitations and future perspectives.

Artificial intelligence (AI) has rapidly evolved and demonstrated its potential in transforming biomedical cancer research, offering innovative solutions for cancer diagnosis, treatment, and overall patient care. Over the past two decades, AI has played a pivotal role in revolutionizing various facets of cancer clinical research. In this comprehensive review, we delve into the diverse applications of AI across the cancer care continuum, encompassing radiodiagnosis, radiotherapy, chemotherapy, immunotherapy, targeted therapy, surgery, and nanotechnology. AI has revolutionized cancer diagnosis, enabling early detection and precise characterization through advanced image analysis techniques. In radiodiagnosis, AI-driven algorithms enhance the accuracy of medical imaging, making it an invaluable tool for clinicians in the detection and assessment of cancer. AI has also revolutionized radiotherapy, facilitating precise tumor boundary delineation, optimizing treatment planning, and enabling real-time adjustments to improve therapeutic outcomes while minimizing collateral damage to healthy tissues. In chemotherapy, AI models have emerged as powerful tools for predicting patient responses to different treatment regimens, allowing for more personalized and effective strategies. In immunotherapy, AI analyzes genetic and imaging data to select ideal candidates for treatment and predict responses. Targeted therapy has seen great advancements with AI, aiding in the identification of specific molecular targets for tailored treatments. AI plays a vital role in surgery by offering real-time navigation and support, enhancing surgical precision. Moreover, the synergy between AI and nanotechnology promises the development of personalized nanomedicines, offering more efficient and targeted cancer treatments. While challenges related to data quality, interpretability, and ethical considerations persist, the future of AI in cancer research holds tremendous promise for improving patient outcomes through advanced and individualized care.

Recognition of the inferior mesenteric artery (IMA) during colorectal cancer surgery is crucial to avoid intraoperative hemorrhage and define the appropriate lymph node dissection line. This retrospective feasibility study aimed to develop an IMA anatomical recognition model for laparoscopic colorectal resection using deep learning, and to evaluate its recognition accuracy and real-time performance.

A complete multi-institutional surgical video database, LapSig300 was used for this study. Intraoperative videos of 60 patients who underwent laparoscopic sigmoid colon resection or high anterior resection were randomly extracted from the database and included. Deep learning-based semantic segmentation accuracy and real-time performance of the developed IMA recognition model were evaluated using Dice similarity coefficient (DSC) and frames per second (FPS), respectively.

In a fivefold cross-validation conducted using 1200 annotated images for the IMA semantic segmentation task, the mean DSC value was 0.798 (± 0.0161 SD) and the maximum DSC was 0.816. The proposed deep learning model operated at a speed of over 12 FPS.

To the best of our knowledge, this is the first study to evaluate the feasibility of real-time vascular anatomical navigation during laparoscopic colorectal surgery using a deep learning-based semantic segmentation approach. This experimental study was conducted to confirm the feasibility of our model; therefore, its safety and usefulness were not verified in clinical practice. However, the proposed deep learning model demonstrated a relatively high accuracy in recognizing IMA in intraoperative images. The proposed approach has potential application in image navigation systems for unfixed soft tissues and organs during various laparoscopic surgeries.

We carried out robot-assisted lateral pelvic lymph node dissection (LPLND) for rectal cancer with a stereotactic navigation system. The purpose of this study was to evaluate the accuracy and feasibility of the system.

We constructed a navigation system based on the Polaris Spectra optical tracking device (Northern Digital Inc., Canada) and the open-source software 3D Slicer (version 3.8.1; http://www.slicer.org ). We used the landmark-based registration method for patient-to-image registration. Body surface landmarks and intra-abdominal landmarks were used. We evaluated the time required for registration and target registration error (TRE; the distance between corresponding points after registration) for the root of the superior gluteal artery the root of the obturator or superior vesical artery, and the obturator foramen during minimally invasive LPLND for rectal cancer. Five patients who had LPLND for rectal cancer at the University of Tokyo Hospital between September 2020 and May 2021 were enrolled.

The mean time required for registration was 49 s with the body surface landmarks and 88 s with the intra-abdominal landmarks. The mean TRE improved markedly when the registration was performed using intra-abdominal landmarks. The mean TRE of the root of the superior gluteal artery, the root of the obturator or superior vesical artery, and the obturator foramen were 55.8 mm, 53.4 mm, and 55.2 mm with the body surface landmarks and 11.8 mm, 10.0 mm, and 12.6 mm with the intra-abdominal landmarks, respectively. There were no adverse events related to the registration process.

When stereotactic navigation systems are used for minimally invasive LPLND, the use of intra-abdominal landmarks for registration is feasible and may allow simpler and more accurate navigation than the use of body surface landmarks.

In median arcuate ligament syndrome (MALS), a hyperplastic MAL causes compression and stenosis of the celiac artery (CA). The treatment involves releasing the external pressure on this artery by dissecting the ligament. However, it is difficult to identify the artery because of its deep anatomical location. Stereotactic navigation provides real-time information regarding the surgical instrument's location on computed tomography (CT) images. We utilized this system to overcome the difficulty of anatomical identification.

We present a case of aneurysm rupture caused by MALS, which was treated with laparoscopic MAL dissection with real-time stereotactic navigation. Surgery was performed in a hybrid operating room with three-dimensional C-arm CT (Artis Zeego, Siemens) and an installed Curve navigation system (BrainLab). Preoperative CT images were aligned with intraoperative C-arm CT-like images and the surgical instrument position was projected onto preoperative CT images. After the left gastric artery isolation, the fibrous tissue surrounding the left gastric artery was dissected toward the CA while confirming the location of the CA and aortic wall using the navigation system. The CA's diameter was dilated from 1.8 to 2.6 mm with intraoperative angiography.

This is the first report of laparoscopic MAL dissection using real-time stereotactic navigation. Although navigation setting was time-intensive, this system helped us understand the anatomical structures and in safely and precisely dissecting the MAL.