Development of a Statistical Shape Model and Assessment of Anatomical Shape Variations in the Hemipelvis.

Knowledge about anatomical shape variations in the pelvis is mandatory for selection, fitting, positioning, and fixation in pelvic surgery. The current knowledge on pelvic shape variation mostly relies on point-to-point measurements on 2D X-ray images and computed tomography (CT) slices. Three-dimensional region-specific assessments of pelvic morphology are scarce. Our aim was to develop a statistical shape model of the hemipelvis to assess anatomical shape variations in the hemipelvis. CT scans of 200 patients (100 male and 100 female) were used to obtain segmentations. An iterative closest point algorithm was performed to register these 3D segmentations, so a principal component analysis (PCA) could be performed, and a statistical shape model (SSM) of the hemipelvis was developed. The first 15 principal components (PCs) described 90% of the total shape variation, and the reconstruction ability of this SSM resulted in a root mean square error of 1.58 (95% CI: 1.53-1.63) mm. In summary, an SSM of the hemipelvis was developed, which describes the shape variations in a Caucasian population and is able to reconstruct an aberrant hemipelvis. Principal component analyses demonstrated that, in a general population, anatomical shape variations were mostly related to differences in the size of the pelvis (e.g., PC1 describes 68% of the total shape variation, which is attributed to size). Differences between the male and female pelvis were most pronounced in the iliac wing and pubic rami regions. These regions are often subject to injuries. Future clinical applications of our newly developed SSM may be relevant for SSM-based semi-automatic virtual reconstruction of a fractured hemipelvis as part of preoperative planning. Lastly, for companies, using our SSM might be interesting in order to assess which sizes of pelvic implants should be produced to provide proper-fitting implants for most of the population.

Diminishing Endograft Apposition during Follow-Up Is an Important Indicator of Late Type 1a Endoleak after Endovascular Aneurysm Repair.

Late type 1a endoleaks (T1aELs) after endovascular aneurysm repair (EVAR) are hazardous complications which should be avoided. This study investigated the evolution of the shortest apposition length (SAL) post-EVAR and hypothesised that a declining apposition during follow-up may be an indicator of T1aEL development. Patients with a late T1aEL were selected from a consecutive multicentre database. For each T1aEL patient, the preoperative computed tomography angiography (CTA), first postoperative CTA, and pre-endoleak CTA were analysed. T1aEL patients were matched 1:1 to uncomplicated controls, based on endograft type and follow-up duration. Anatomical characteristics and endograft dimensions, including the post-EVAR SAL, were measured. Included were 28 patients with a late T1aEL and 28 matched controls. The SAL decreased from 11.2 mm (5.6-20.6 mm) to 3.9 mm (0.0-11.4 mm) in the T1aEL group (p = 0.006), whereas an increase in SAL was seen in the control group from 21.3 mm (14.1-25.8 mm) to 25.4 mm (19.0-36.2 mm; p = 0.015). On the pre-endoleak CTA, 18 patients (64%) in the T1aEL group had a SAL < 10 mm, and one (4%) patient in the control group had a SAL < 10 mm on the matched CTAs. Moreover, three mechanisms of decreasing sealing zone were identified, which might be used to determine optimal imaging or reintervention strategies. Diminishing SAL < 10 mm is an indicator for T1aEL during follow-up, it is imperative to include apposition analysis during follow-up.

A Statistical Shape Model of Infrarenal Aortic Necks in Patients With and Without Late Type Ia Endoleak After Endovascular Aneurysm Repair.

PURPOSE: Hostile aortic neck characteristics, including short length, severe suprarenal and infrarenal angulation, conicity, and large diameter, have been associated with increased risk for type Ia endoleak (T1aEL) after endovascular aneurysm repair (EVAR). This study investigates the mid-term discriminative ability of a statistical shape model (SSM) of the infrarenal aortic neck morphology compared with or in combination with conventional measurements in patients who developed T1aEL post-EVAR. MATERIALS AND METHODS: The dataset composed of EVAR patients who developed a T1aEL during follow-up and a control group without T1aEL. Principal component (PC) analysis was performed using a parametrization to create an SSM. Three logistic regression models were created. To discriminate between patients with and without T1aEL, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC) were calculated. RESULTS: In total, 126 patients (84% male) were included. Median follow-up time in T1aEl group and control group was 52 (31, 78.5) and 51 (40, 62.5) months, respectively. Median follow-up time was not statistically different between the groups (p=0.72). A statistically significant difference between the median PC scores of the T1aEL and control groups was found for the first, eighth, and ninth PC. Sensitivity, specificity, and AUC values for the SSM-based versus the conventional measurements-based logistic regression models were 79%, 70%, and 0.82 versus 74%, 73%, and 0.85, respectively. The model of the SSM and conventional measurements combined resulted in sensitivity, specificity, and AUC of 81%, 81%, and 0.92. CONCLUSION: An SSM of the infrarenal aortic neck determines its 3-dimensional geometry. The SSM is a potential valuable tool for risk stratification and T1aEL prediction in EVAR. The SSM complements the conventional measurements of the individual preoperative infrarenal aortic neck geometry by increasing the predictive value for late type Ia endoleak after standard EVAR. CLINICAL IMPACT: A statistical shape model (SSM) determines the 3-dimensional geometry of the infrarenal aortic neck. The SSM complements the conventional measurements of the individual pre-operative infrarenal aortic neck geometry by increasing the predictive value for late type Ia endoleaks post-EVAR. The SSM is a potential valuable tool for risk stratification and late T1aEL prediction in EVAR and it is a first step toward implementation of a treatment planning support tool in daily clinical practice.

A Statistical Shape Model of the Morphological Variation of the Infrarenal Abdominal Aortic Aneurysm Neck.

Hostile aortic neck characteristics, such as short length and large diameter, have been associated with type Ia endoleaks and reintervention after endovascular aneurysm repair (EVAR). However, such characteristics partially describe the complex aortic neck morphology. A more comprehensive quantitative description of 3D neck shape might lead to new insights into the relationship between aortic neck morphology and EVAR outcomes in individual patients. This study identifies the 3D morphological shape components that describe the infrarenal aortic neck through a statistical shape model (SSM). Pre-EVAR CT scans of 97 patients were used to develop the SSM. Parameterization of the morphology was based on the center lumen line reconstruction, a triangular surface mesh of the aortic lumen, 3D coordinates of the renal arteries, and the distal end of the aortic neck. A principal component analysis of the parametrization of the aortic neck coordinates was used as input for the SSM. The SSM consisted of 96 principal components (PCs) that each described a unique shape feature. The first five PCs represented 95% of the total morphological variation in the dataset. The SSM is an objective model that provides a quantitative description of the neck morphology of an individual patient.