The posterior longitudinal ligament (PLL) is described as having deep and superficial layers, though recent studies have suggested that there may be three layers. Additional membranous structures have been reported, although there is no consensus as to their presence or morphology. The vertebral canal and dural sac were opened and the spinal nerve roots and spinal cord removed. The anterior dural ligaments were sectioned at their attachment to the PLL and the dura mater freed from the posterior surface of the vertebral bodies. The borders of the PLL were identified and the superficial and deep layers separated. The PLL is a wide band in the cervical region becoming more denticulate inferiorly, the widest parts being attached to the intervertebral discs (IVD) and adjacent vertebral body where the superficial and deep layers could not be separated. A continuous well developed peridural membrane attaching to the pedicles was present anterior to the deep PLL as well as a separate, thin, incomplete layer in 6 of 18 cadavers, covering the posterior surface of the superficial PLL.

PurposeTo investigate the role of the apparent diffusion coefficient (ADC) as a potential imaging biomarker to predict metastasis (lymph node metastasis and distant metastasis) in colon cancer based on the ADC-value of the primary tumor.MethodsThirty patients (21M, 9F) were included retrospectively. All patients received a 1.5T MRI of the colon including T2 and DWI sequences. ADC maps were calculated for each patient. An expert reader manually delineated all colon tumors to measure mean ADC and histogram metrics (mean, min, max, median, standard deviation (SD), skewness, kurtosis, 5th-95th percentiles) were calculated. Advanced colon cancer was defined as lymph node mestastasis (N+) or distant metastasis (M+). The student Mann Whitney U-test was used to assess the differences between the ADC means of early and advanced colon cancer. To compare the accuracy of lymph node metastasis (N+) prediction based on morpholigical criteria versus ADC-value of the primary tumor, two blinded readers, determined the lymph node metastasis (N0 vs N+) based on morphological criteria. The sensitivity and specificity in predicting lymph node metastasis was calculated for both readers and for the ADC-value of the primary tumor, with histopathology results as the gold standard.ResultsThere was a significant difference between the mean ADC-value of advanced versus early tumors (p = 0.002). The optimal cut off value was 1179 * 10−3 mm2/s with an area under the curve (AUC) of 0.83 and a sensitivity and specificity of 81% and 86% respectively to predict advanced tumors. Histogram analyses did not add any significant additional value.The sensitivity and specificity for the prediction of lymph node metastasis based on morphological criteria were 40% and 63% for reader 1 and 30% and 88% for reader 2 respectively. The primary tumor ADC-value using 1.179 * 10−3 mm2/s as threshold had a 100% sensitivity and specificity in predicting lymph node metastasis.ConclusionThe ADC-value of the primary tumor has the potential to predict advanced colon cancer, defined as lymph node metastasis or distant metastasis, with lower ADC values significantly associated with advanced tumors. Furthermore the ADC-value of the primary tumor increases the prediction accuracy of lymph node metastasis compared with morphological criteria.

Hofmann ligaments are present at most levels between C7 and L5; although most ligaments were limited to a single vertebral segment, some were observed to cross several segments.

Over the past decade, enhanced preoperative imaging and visualization, improved delineation of the complex anatomical structures of the liver and pancreas, and intra-operative technological advances have helped deliver the liver and pancreatic surgery with increased safety and better postoperative outcomes. Artificial intelligence (AI) has a major role to play in 3D visualization, virtual simulation, augmented reality that helps in the training of surgeons and the future delivery of conventional, laparoscopic, and robotic hepatobiliary and pancreatic (HPB) surgery; artificial neural networks and machine learning has the potential to revolutionize individualized patient care during the preoperative imaging, and postoperative surveillance. In this paper, we reviewed the existing evidence and outlined the potential for applying AI in the perioperative care of patients undergoing HPB surgery.