Intraoral scanner-based monitoring of tooth wear in young adults: 36-month results.

OBJECTIVES: The study continues our longitudinal observation of wear aiming to further monitoring of progression and lesion morphology and to identify relationships with assumed aetiological factors. MATERIALS AND METHODS: Molars (FDI #36 or #46) of 74 participants (23.8 ± 2.2 years) were scanned (Trios 3, 3Shape) at the third follow-up (T3; observation period 1,111 ± 10 days). Data sets from T3, T2 (24-month follow-up) and T1 (12-month follow-up) were superimposed with baseline in a 3D analysis software (GOM Inspect). Wear was quantified as maximum vertical tissue loss (µm; median, 95% CI) in various occlusal areas (4/5 cusps and 2 ridges). Morphologies were classified into cupping (C), facet (F), and combined cupping-facet (CF). Aetiological factors were assessed with questionnaires. RESULTS: Wear increased at T3 significantly at low rates in all areas of the occlusal surface (median between 7.0 (4.0;10.5) and 9.5 (6.0;15.0) µm). There was a clear trend for higher loss values in males, but no association with other factors such as nutrition. C and CF showed significantly higher loss values than F. Areas without initial wear developed F first, which either persisted or developed into C and CF. CONCLUSIONS: Wear continued at low rates with C/CF morphology and sex as significant factors. Cupped lesions seem to develop from facets and thus may not be a valid diagnostic criterion for erosive tooth wear. CLINICAL RELEVANCE: Wear is a cumulative process that apparently follows complex mechanisms that cannot be conceptualized in simplified terms; C and CF may be indicators for higher progression rates.

Automatic removal of soft tissue from 3D dental photo scans; an important step in automating future forensic odontology identification.

The potential of intraoral 3D photo scans in forensic odontology identification remains largely unexplored, even though the high degree of detail could allow automated comparison of ante mortem and post mortem dentitions. Differences in soft tissue conditions between ante- and post mortem intraoral 3D photo scans may cause ambiguous variation, burdening the potential automation of the matching process and underlining the need for limiting inclusion of soft tissue in dental comparison. The soft tissue removal must be able to handle dental arches with missing teeth, and intraoral 3D photo scans not originating from plaster models. To address these challenges, we have developed the grid-cutting method. The method is customisable, allowing fine-grained analysis using a small grid size and adaptation of how much of the soft tissues are excluded from the cropped dental scan. When tested on 66 dental scans, the grid-cutting method was able to limit the amount of soft tissue without removing any teeth in 63/66 dental scans. The remaining 3 dental scans had partly erupted third molars (wisdom teeth) which were removed by the grid-cutting method. Overall, the grid-cutting method represents an important step towards automating the matching process in forensic odontology identification using intraoral 3D photo scans.

Combined anatomical reduction plate for quadrilateral acetabular fractures via a posterior approach: an anatomical-morphological study.

OBJECTIVE: To digitally measure the fixation trajectory of anatomical plates used in the combined reduction of quadrilateral acetabular fractures via the posterior approach, and to develop anatomical plates that align with the characteristics of the pelvis in the Chinese population. METHODS: Pelvic computed tomography (CT) data from 102 adult patients were collected at the Affiliated Hospital of Zunyi Medical University. This group included 51 males and 51 females, aged between 20 and 60 years. Using Mimics software (version 21.0), a three-dimensional model of each pelvic data point was reconstructed. The fixation path for the combined reset anatomical steel plate was drawn, where the curves on the fixation path were approximated as arcs. The radius of curvature and length of these curves were measured, and an anatomical steel plate was designed to best fit the pelvic structure. RESULTS: The combined anatomical reduction plate fixation system for quadrilateral acetabular fractures using a posterior approach consisted of two parts: a locking plate and a reduction plate. The posterior wall region (r2), ischial region (r3), quadrilateral region (r4), and bending region (r5), and the total length of the reduction plate were significantly smaller in females (P < 0.05). Similarly, the posterior wall region (R3), distal posterior wall region (R4), and the total length of the locking plate were significantly smaller in females (P < 0.05). Additionally, the anterior superior iliac spine side (r1) and the total length of the T-shaped auxiliary plate were significantly smaller in females (P < 0.05). The bending angle (< A) was also significantly smaller in females (P < 0.05). CONCLUSIONS: The pelvic surface structure is irregular and varies greatly among individuals.Compared to the traditional steel plate, The combined reduction anatomical plate designed in this study demonstrated high precision and improved conformity to the anatomical structure of the pelvis.

Versatile multiple object tracking in sparse 2D/3D videos via deformable image registration.

Tracking body parts in behaving animals, extracting fluorescence signals from cells embedded in deforming tissue, and analyzing cell migration patterns during development all require tracking objects with partially correlated motion. As dataset sizes increase, manual tracking of objects becomes prohibitively inefficient and slow, necessitating automated and semi-automated computational tools. Unfortunately, existing methods for multiple object tracking (MOT) are either developed for specific datasets and hence do not generalize well to other datasets, or require large amounts of training data that are not readily available. This is further exacerbated when tracking fluorescent sources in moving and deforming tissues, where the lack of unique features and sparsely populated images create a challenging environment, especially for modern deep learning techniques. By leveraging technology recently developed for spatial transformer networks, we propose ZephIR, an image registration framework for semi-supervised MOT in 2D and 3D videos. ZephIR can generalize to a wide range of biological systems by incorporating adjustable parameters that encode spatial (sparsity, texture, rigidity) and temporal priors of a given data class. We demonstrate the accuracy and versatility of our approach in a variety of applications, including tracking the body parts of a behaving mouse and neurons in the brain of a freely moving C. elegans. We provide an open-source package along with a web-based graphical user interface that allows users to provide small numbers of annotations to interactively improve tracking results.

Automated segmentation and recognition of C. elegans whole-body cells.

MOTIVATION: Accurate segmentation and recognition of C.elegans cells are critical for various biological studies, including gene expression, cell lineages, and cell fates analysis at single-cell level. However, the highly dense distribution, similar shapes, and inhomogeneous intensity profiles of whole-body cells in 3D fluorescence microscopy images make automatic cell segmentation and recognition a challenging task. Existing methods either rely on additional fiducial markers or only handle a subset of cells. Given the difficulty or expense associated with generating fiducial features in many experimental settings, a marker-free approach capable of reliably segmenting and recognizing C.elegans whole-body cells is highly desirable. RESULTS: We report a new pipeline, called automated segmentation and recognition (ASR) of cells, and applied it to 3D fluorescent microscopy images of L1-stage C.elegans with 558 whole-body cells. A novel displacement vector field based deep learning model is proposed to address the problem of reliable segmentation of highly crowded cells with blurred boundary. We then realize the cell recognition by encoding and exploiting statistical priors on cell positions and structural similarities of neighboring cells. To the best of our knowledge, this is the first method successfully applied to the segmentation and recognition of C.elegans whole-body cells. The ASR-segmentation module achieves an F1-score of 0.8956 on a dataset of 116 C.elegans image stacks with 64 728 cells (accuracy 0.9880, AJI 0.7813). Based on the segmentation results, the ASR recognition module achieved an average accuracy of 0.8879. We also show ASR's applicability to other cell types, e.g. platynereis and rat kidney cells. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/reaneyli/ASR.

Transcranial volumetric imaging using a conformal ultrasound patch.

Accurate and continuous monitoring of cerebral blood flow is valuable for clinical neurocritical care and fundamental neurovascular research. Transcranial Doppler (TCD) ultrasonography is a widely used non-invasive method for evaluating cerebral blood flow1, but the conventional rigid design severely limits the measurement accuracy of the complex three-dimensional (3D) vascular networks and the practicality for prolonged recording2. Here we report a conformal ultrasound patch for hands-free volumetric imaging and continuous monitoring of cerebral blood flow. The 2 MHz ultrasound waves reduce the attenuation and phase aberration caused by the skull, and the copper mesh shielding layer provides conformal contact to the skin while improving the signal-to-noise ratio by 5 dB. Ultrafast ultrasound imaging based on diverging waves can accurately render the circle of Willis in 3D and minimize human errors during examinations. Focused ultrasound waves allow the recording of blood flow spectra at selected locations continuously. The high accuracy of the conformal ultrasound patch was confirmed in comparison with a conventional TCD probe on 36 participants, showing a mean difference and standard deviation of difference as -1.51 ± 4.34 cm s-1, -0.84 ± 3.06 cm s-1 and -0.50 ± 2.55 cm s-1 for peak systolic velocity, mean flow velocity, and end diastolic velocity, respectively. The measurement success rate was 70.6%, compared with 75.3% for a conventional TCD probe. Furthermore, we demonstrate continuous blood flow spectra during different interventions and identify cascades of intracranial B waves during drowsiness within 4 h of recording.

3-D Printed Skull Model: Role in Identification of Site of Bone Defect in Cases with CSF Rhinorrhea.

Role of 3-D models in the identification of the site and extent of bone defects in the skull base for the treatment of CSF rhinorrhea is analyzed. Such models were used successfully in the management of two patients who failed previous attempts at basal reconstruction. The principal advantage of the models was in exact delineation of the size and site of bone defect and deciphering of its relationship with adjoining critical regions of the brain.

Free roaming of 3D stratum models based on internal and external boundary identification.

3D stratum roaming can visualize the complex geomorphology, underground structure and stratum distribution of geotechnical engineering. Conventional 3D stratum roaming technology is generally aimed at roaming outside the 3D stratum model but rarely roams inside the 3D stratum model, and the efficiency of switching between external and internal roaming is low. In practical engineering, especially in geological and geotechnical engineering, the underground structure and stratum situation are critical. Therefore, focusing on this problem, this paper adopts a three-dimensional roaming engine to connect the inside and outside of a three-dimensional model. Based on an internal and external boundary identification method, the combination of external roaming and internal roaming of the three-dimensional stratum model is implemented by using a stratum virtual surface, and lightweight loading is carried out by controlling the stratum virtual surface to establish the internal and external roaming of the 3D stratum model, and to give full play to the advantages of lightweight loading to provide more intuitive and comprehensive geological information for the project.

MCE: Medical Cognition Embedded in 3D MRI feature extraction for advancing glioma staging.

In recent years, various data-driven algorithms have been applied to the classification and staging of brain glioma MRI detection. However, the restricted availability of brain glioma MRI data in purely data-driven deep learning algorithms has presented challenges in extracting high-quality features and capturing their complex patterns. Moreover, the analysis methods designed for 2D data necessitate the selection of ideal tumor image slices, which does not align with practical clinical scenarios. Our research proposes an novel brain glioma staging model, Medical Cognition Embedded (MCE) model for 3D data. This model embeds knowledge characteristics into data-driven approaches to enhance the quality of feature extraction. Approach includes the following key components: (1) Deep feature extraction, drawing upon the imaging technical characteristics of different MRI sequences, has led to the design of two methods at both the algorithmic and strategic levels to mimic the learning process of real image interpretation by medical professionals during film reading; (2) We conduct an extensive Radiomics feature extraction, capturing relevant features such as texture, morphology, and grayscale distribution; (3) By referencing key points in radiological diagnosis, Radiomics feature experimental results, and the imaging characteristics of various MRI sequences, we manually create diagnostic features (Diag-Features). The efficacy of proposed methodology is rigorously evaluated on the publicly available BraTS2018 and BraTS2020 datasets. Comparing it to most well-known purely data-driven models, our method achieved higher accuracy, recall, and precision, reaching 96.14%, 93.4%, 97.06%, and 97.57%, 92.80%, 95.96%, respectively.

Markerless 3D kinematics and force estimation in cheetahs.

The complex dynamics of animal manoeuvrability in the wild is extremely challenging to study. The cheetah (Acinonyx jubatus) is a perfect example: despite great interest in its unmatched speed and manoeuvrability, obtaining complete whole-body motion data from these animals remains an unsolved problem. This is especially difficult in wild cheetahs, where it is essential that the methods used are remote and do not constrain the animal's motion. In this work, we use data obtained from cheetahs in the wild to present a trajectory optimisation approach for estimating the 3D kinematics and joint torques of subjects remotely. We call this approach kinetic full trajectory estimation (K-FTE). We validate the method on a dataset comprising synchronised video and force plate data. We are able to reconstruct the 3D kinematics with an average reprojection error of 17.69 pixels (62.94% PCK using the nose-to-eye(s) length segment as a threshold), while the estimates produce an average root-mean-square error of 171.3N ( ≈ 17.16% of peak force during stride) for the estimated ground reaction force when compared against the force plate data. While the joint torques cannot be directly validated against ground truth data, as no such data is available for cheetahs, the estimated torques agree with previous studies of quadrupeds in controlled settings. These results will enable deeper insight into the study of animal locomotion in a more natural environment for both biologists and roboticists.

Interpreting an Archaean paleoenvironment through 3D imagery of microbialites.

While stromatolites, and to a lesser extent thrombolites, have been extensively studied in order to unravel Precambrian (>539 Ma) biological evolution, studies of clastic-dominated microbially induced sedimentary structures (MISS) are relatively scarce. The lack of a consolidated record of clastic microbialites creates questions about how much (and what) information on depositional and taphonomic settings can be gleaned from these fossils. We used µCT scanning, a non-destructive X-ray-based 3D imaging method, to reconstruct morphologies of ancient MISS and mat textures in two previously described coastal Archaean samples from the ~3.48 Ga Dresser Formation, Pilbara, Western Australia. The aim of this study was to test the ability of µCT scanning to visualize and make 3D measurements that can be used to interpret the biotic-environmental interactions. Fossil MISS including mat laminae with carpet-like textures in one sample and mat rip-up chips in the second sample were investigated. Compiled δ13C and δ34S analyses of specimens from the Dresser Fm. are consistent with a taxonomically diverse community that could be capable of forming such MISS. 3D measurements of fossil microbial mat chips indicate significant biostabilization and suggest formation in flow velocities >25 cm s-1. Given the stratigraphic location of these chips in a low-flow lagoonal layer, we conclude that these chips formed due to tidal influence, as these assumed velocities are consistent with recent modeling of Archaean tides. The success of µCT scanning in documenting these microbialite features validates this technique both as a first step analysis for rare samples prior to the use of more destructive techniques and as a valuable tool for gaining insight into microbialite taphonomy.

High-confidence 3D template matching for cryo-electron tomography.

Visual proteomics attempts to build atlases of the molecular content of cells but the automated annotation of cryo electron tomograms remains challenging. Template matching (TM) and methods based on machine learning detect structural signatures of macromolecules. However, their applicability remains limited in terms of both the abundance and size of the molecular targets. Here we show that the performance of TM is greatly improved by using template-specific search parameter optimization and by including higher-resolution information. We establish a TM pipeline with systematically tuned parameters for the automated, objective and comprehensive identification of structures with confidence 10 to 100-fold above the noise level. We demonstrate high-fidelity and high-confidence localizations of nuclear pore complexes, vaults, ribosomes, proteasomes, fatty acid synthases, lipid membranes and microtubules, and individual subunits inside crowded eukaryotic cells. We provide software tools for the generic implementation of our method that is broadly applicable towards realizing visual proteomics.

Accurate object localization facilitates automatic esophagus segmentation in deep learning.

BACKGROUND: Currently, automatic esophagus segmentation remains a challenging task due to its small size, low contrast, and large shape variation. We aimed to improve the performance of esophagus segmentation in deep learning by applying a strategy that involves locating the object first and then performing the segmentation task. METHODS: A total of 100 cases with thoracic computed tomography scans from two publicly available datasets were used in this study. A modified CenterNet, an object location network, was employed to locate the center of the esophagus for each slice. Subsequently, the 3D U-net and 2D U-net_coarse models were trained to segment the esophagus based on the predicted object center. A 2D U-net_fine model was trained based on the updated object center according to the 3D U-net model. The dice similarity coefficient and the 95% Hausdorff distance were used as quantitative evaluation indexes for the delineation performance. The characteristics of the automatically delineated esophageal contours by the 2D U-net and 3D U-net models were summarized. Additionally, the impact of the accuracy of object localization on the delineation performance was analyzed. Finally, the delineation performance in different segments of the esophagus was also summarized. RESULTS: The mean dice coefficient of the 3D U-net, 2D U-net_coarse, and 2D U-net_fine models were 0.77, 0.81, and 0.82, respectively. The 95% Hausdorff distance for the above models was 6.55, 3.57, and 3.76, respectively. Compared with the 2D U-net, the 3D U-net has a lower incidence of delineating wrong objects and a higher incidence of missing objects. After using the fine object center, the average dice coefficient was improved by 5.5% in the cases with a dice coefficient less than 0.75, while that value was only 0.3% in the cases with a dice coefficient greater than 0.75. The dice coefficients were lower for the esophagus between the orifice of the inferior and the pulmonary bifurcation compared with the other regions. CONCLUSION: The 3D U-net model tended to delineate fewer incorrect objects but also miss more objects. Two-stage strategy with accurate object location could enhance the robustness of the segmentation model and significantly improve the esophageal delineation performance, especially for cases with poor delineation results.

Three-dimensional evaluation of dental characteristics in patients with Cleidocranial dysplasia.

BACKGROUND: Cleidocranial dysplasia (CCD) is an autosomal dominant hereditary disorder. Besides skeletal abnormalities, CCD is often associated with dental complications, such as multiple supernumerary teeth and permanent teeth impaction or delayed eruption. METHODS: Supernumerary teeth of axial, sagittal and coronal CBCT view was characterized in detail and 3D image reconstruction was performed. Number and location of teeth, morphology of supernumerary teeth, positional relationship between supernumerary and adjacent permanent teeth, direction of supernumerary teeth in CCD patients were analyzed. RESULTS: The mean age of the 3 CCD patients in this study was 16.7 years. Among 36 supernumerary teeth, the majority of them were identified as apical side located and lingual side located. Normal orientation was the most common type in this study, followed by sagittal orientation, and horizontal orientation. Horizontal orientation teeth were all distributed in the mandible. Supernumerary teeth exhibited significantly shorter crown and dental-root lengths, as well as smaller crown mesiodistal and buccolingual diameters (P < 0.01). There was no difference in the number of supernumerary teeth between the maxilla and mandible, and the premolars region had the largest number of supernumerary teeth and the incisor region had the smallest number. CONCLUSIONS: This study compares number and location of teeth, morphology of supernumerary teeth, positional relationship between supernumerary and adjacent permanent teeth and direction of supernumerary teeth, this study also provides a reference for the comprehensive evaluation of CCD patients before surgery.

Photoacoustic Tomography with Temporal Encoding Reconstruction (PATTERN) for cross-modal individual analysis of the whole brain.

Cross-modal analysis of the same whole brain is an ideal strategy to uncover brain function and dysfunction. However, it remains challenging due to the slow speed and destructiveness of traditional whole-brain optical imaging techniques. Here we develop a new platform, termed Photoacoustic Tomography with Temporal Encoding Reconstruction (PATTERN), for non-destructive, high-speed, 3D imaging of ex vivo rodent, ferret, and non-human primate brains. Using an optimally designed image acquisition scheme and an accompanying machine-learning algorithm, PATTERN extracts signals of genetically-encoded probes from photobleaching-based temporal modulation and enables reliable visualization of neural projection in the whole central nervous system with 3D isotropic resolution. Without structural and biological perturbation to the sample, PATTERN can be combined with other whole-brain imaging modalities to acquire the whole-brain image with both high resolution and morphological fidelity. Furthermore, cross-modal transcriptome analysis of an individual brain is achieved by PATTERN imaging. Together, PATTERN provides a compatible and versatile strategy for brain-wide cross-modal analysis at the individual level.

Automatic extraction of facial median sagittal plane for patients with asymmetry based on the EDMA alignment algorithm.

BACKGROUND: We aimed to establish a novel method for automatically constructing three-dimensional (3D) median sagittal plane (MSP) for mandibular deviation patients, which can increase the efficiency of aesthetic evaluating treatment progress. We developed a Euclidean weighted Procrustes analysis (EWPA) algorithm for extracting 3D facial MSP based on the Euclidean distance matrix analysis, automatically assigning weight to facial anatomical landmarks. METHODS: Forty patients with mandibular deviation were recruited, and the Procrustes analysis (PA) algorithm based on the original mirror alignment and EWPA algorithm developed in this study were used to construct the MSP of each facial model of the patient as experimental groups 1 and 2, respectively. The expert-defined regional iterative closest point algorithm was used to construct the MSP as the reference group. The angle errors of the two experimental groups were compared to those of the reference group to evaluate their clinical suitability. RESULTS: The angle errors of the MSP constructed by the two EWPA and PA algorithms for the 40 patients were 1.39 ± 0.85°, 1.39 ± 0.78°, and 1.91 ± 0.80°, respectively. The two EWPA algorithms performed best in patients with moderate facial asymmetry, and in patients with severe facial asymmetry, the angle error was below 2°, which was a significant improvement over the PA algorithm. CONCLUSIONS: The clinical application of the EWPA algorithm based on 3D facial morphological analysis for constructing a 3D facial MSP for patients with mandibular deviated facial asymmetry deformity showed a significant improvement over the conventional PA algorithm and achieved the effect of a dental clinical expert-level diagnostic strategy.

Three-dimensional magnetic resonance imaging-based statistical shape analysis and machine learning-based prediction of patellofemoral instability.

This study performed three-dimensional (3D) magnetic resonance imaging (MRI)-based statistical shape analysis (SSA) by comparing patellofemoral instability (PFI) and normal femur models, and developed a machine learning (ML)-based prediction model. Twenty (19 patients) and 31 MRI scans (30 patients) of femurs with PFI and normal femurs, respectively, were used. Bone and cartilage segmentation of the distal femurs was performed and subsequently converted into 3D reconstructed models. The pointwise distance map showed anterior elevation of the trochlea, particularly at the central floor of the proximal trochlea, in the PFI models compared with the normal models. Principal component analysis examined shape variations in the PFI group, and several principal components exhibited shape variations in the trochlear floor and intercondylar width. Multivariate analysis showed that these shape components were significantly correlated with the PFI/non-PFI distinction after adjusting for age and sex. Our ML-based prediction model for PFI achieved a strong predictive performance with an accuracy of 0.909 ± 0.015, and an area under the curve of 0.939 ± 0.009 when using a support vector machine with a linear kernel. This study demonstrated that 3D MRI-based SSA can realistically visualize statistical results on surface models and may facilitate the understanding of complex shape features.

New diagnostic criteria for metopic ridges and trigonocephaly: a 3D geometric approach.

BACKGROUND: Trigonocephaly occurs due to the premature fusion of the metopic suture, leading to a triangular forehead and hypotelorism. This condition often requires surgical correction for morphological and functional indications. Metopic ridges also originate from premature metopic closure but are only associated with mid-frontal bulging; their surgical correction is rarely required. Differential diagnosis between these two conditions can be challenging, especially in minor trigonocephaly. METHODS: Two hundred seven scans of patients with trigonocephaly (90), metopic rigdes (27), and controls (90) were collected. Geometric morphometrics were used to quantify skull and orbital morphology as well as the interfrontal angle and the cephalic index. An innovative method was developed to automatically compute the frontal curvature along the metopic suture. Different machine-learning algorithms were tested to assess the predictive power of morphological data in terms of classification. RESULTS: We showed that control patients, trigonocephaly and metopic rigdes have distinctive skull and orbital shapes. The 3D frontal curvature enabled a clear discrimination between groups (sensitivity and specificity > 92%). Furthermore, we reached an accuracy of 100% in group discrimination when combining 6 univariate measures. CONCLUSION: Two diagnostic tools were proposed and demonstrated to be successful in assisting differential diagnosis for patients with trigonocephaly or metopic ridges. Further clinical assessments are required to validate the practical clinical relevance of these tools.

Evaluating the reliability of the lateral femoral condyle measuring methods by different modalities for patients with lateral patellar dislocation.

BACKGROUND: A variety of measurement methods and imaging modalities are in use to quantify the morphology of lateral femoral condyle (LFC), but the most reliable method remains elusive in patients with lateral patellar dislocation (LPD). The purpose of this study was to determine the intra- and inter-observer reliability of different measurement methods for evaluating the morphology of LFC on different imaging modalities in patients with LPD. METHODS: Seventy-three patients with LPD were included. Four parameters for quantifying the morphology of LFC were retrospectively measured by three observers on MRI, sagittal CT image, conventional radiograph (CR), and three-dimensional CT (3D-CT). The intra-class correlation coefficient was calculated to determine the intra- and inter-observer reliability. Bland-Altman analysis was conducted to identify the bias between observers. RESULTS: The lateral femoral condyle index (LFCI) showed better intra- and inter-observer reliability on MRI and 3D-CT than on CR and sagittal CT images. The mean difference in the LFCI between observers was lowest on 3D-CT (0.047), higher on MRI (0.053), and highest on sagittal CT images (0.062). The LFCI was associated with the lateral femoral condyle ratio (ρ = 0.422, P = 0.022), lateral condyle index (r = 0.413, P = 0.037), and lateral femoral condyle distance (r = 0.459, P = 0.014). The LFCI could be reliably measured by MRI and 3D-CT. CONCLUSION: The LFCI could be reliably measured by MRI and 3D-CT. The LFCI was associated with both the height and length of LFC and could serve as a comprehensive parameter for quantifying the morphology of LFC in patients with LPD.

Construction of a nomogram model to predict technical difficulty in performing laparoscopic sphincter-preserving radical resection for rectal cancer.

BACKGROUND: Colorectal surgeons are well aware that performing surgery for rectal cancer becomes more challenging in obese patients with narrow and deep pelvic cavities. Therefore, it is essential for colorectal surgeons to have a comprehensive understanding of pelvic structure prior to surgery and anticipate potential surgical difficulties. AIM: To evaluate predictive parameters for technical challenges encountered during laparoscopic radical sphincter-preserving surgery for rectal cancer. METHODS: We retrospectively gathered data from 162 consecutive patients who underwent laparoscopic radical sphincter-preserving surgery for rectal cancer. Three-dimensional reconstruction of pelvic bone and soft tissue parameters was conducted using computed tomography (CT) scans. Operative difficulty was categorized as either high or low, and multivariate logistic regression analysis was employed to identify predictors of operative difficulty, ultimately creating a nomogram. RESULTS: Out of 162 patients, 21 (13.0%) were classified in the high surgical difficulty group, while 141 (87.0%) were in the low surgical difficulty group. Multivariate logistic regression analysis showed that the surgical approach using laparoscopic intersphincteric dissection, intraoperative preventive ostomy, and the sacrococcygeal distance were independent risk factors for highly difficult laparoscopic radical sphincter-sparing surgery for rectal cancer (P < 0.05). Conversely, the anterior-posterior diameter of pelvic inlet/sacrococcygeal distance was identified as a protective factor (P < 0.05). A nomogram was subsequently constructed, demonstrating good predictive accuracy (C-index = 0.834). CONCLUSION: The surgical approach, intraoperative preventive ostomy, the sacrococcygeal distance, and the anterior-posterior diameter of pelvic inlet/sacrococcygeal distance could help to predict the difficulty of laparoscopic radical sphincter-preserving surgery.