Validation of static and dynamic radiostereometric analysis of the knee joint using bone-models from CT data

Orthopaedics, Trauma Surgery and Rehabilitatio

Influence of the Anterolateral Ligament on Knee Laxity:A Biomechanical Cadaveric Study Measuring Knee Kinematics in 6 Degrees of Freedom Using Dynamic Radiostereometric Analysis

Orthopaedics, Trauma Surgery and Rehabilitatio

Deep learning for prediction of colorectal cancer outcome: a discovery and validation study

Background Improved markers of prognosis are needed to stratify patients with early-stage colorectal cancer to refine selection of adjuvant therapy. The aim of the present study was to develop a biomarker of patient outcome after primary colorectal cancer resection by directly analysing scanned conventional haematoxylin and eosin stained sections using deep learning. Methods More than 12 000 000 image tiles from patients with a distinctly good or poor disease outcome from four cohorts were used to train a total of ten convolutional neural networks, purpose-built for classifying supersized heterogeneous images. A prognostic biomarker integrating the ten networks was determined using patients with a non-distinct outcome. The marker was tested on 920 patients with slides prepared in the UK, and then independently validated according to a predefined protocol in 1122 patients treated with single-agent capecitabine using slides prepared in Norway. All cohorts included only patients with resectable tumours, and a formalin-fixed, paraffin-embedded tumour tissue block available for analysis. The primary outcome was cancer-specific survival. Findings 828 patients from four cohorts had a distinct outcome and were used as a training cohort to obtain clear ground truth. 1645 patients had a non-distinct outcome and were used for tuning. The biomarker provided a hazard ratio for poor versus good prognosis of 3·84 (95% CI 2·72–5·43; p<0·0001) in the primary analysis of the validation cohort, and 3·04 (2·07–4·47; p<0·0001) after adjusting for established prognostic markers significant in univariable analyses of the same cohort, which were pN stage, pT stage, lymphatic invasion, and venous vascular invasion. Interpretation A clinically useful prognostic marker was developed using deep learning allied to digital scanning of conventional haematoxylin and eosin stained tumour tissue sections. The assay has been extensively evaluated in large, independent patient populations, correlates with and outperforms established molecular and morphological prognostic markers, and gives consistent results across tumour and nodal stage. The biomarker stratified stage II and III patients into sufficiently distinct prognostic groups that potentially could be used to guide selection of adjuvant treatment by avoiding therapy in very low risk groups and identifying patients who would benefit from more intensive treatment regimes

Automated measurement of diagnostic angles for hip dysplasia

A fully automatic method for measuring diagnostic angles of hip dysplasia is presented. The method consists of the automatic segmentation of CT images and detection of anatomical landmarks on the femur and acetabulum. The standard angles used in the diagnosis of hip dysplasia are subsequently automatically calculated. Previous work in automating the measuring of angles required the manual segmentation or delineation of the articular joint surface. In the current work automatic segmentation is established using graph-cuts with a cost function based on a sheetness score to detect the sheet-like structure of the bone. Anatomical landmarks are subsequently detected using heuristics based on ray-tracing and the distance to the approximated acetabulur joint surface. Standard diagnositic angles are finally calculated and presented for interpretation. Experiments using 26 patients, showed a good agreement with gold standard manual measurements by an expert radiologist as performed in daily practice. The mean difference for the five angles was between-1:1 and 2:0 degrees with a concordance correlation coefficient between 0:87 and 0:93. The standard deviation varied between 2:3 and 4:1 degrees. These values correspond to values found in evaluating interobserver and intraobserver variation for manual measurements. The method can be used in clinical practice to replace the current manual measurements performed by radiologists. In the future, the method will be integrated into an intraoperative surgical guidance system

Dynamic radiostereometric analysis for evaluation of hip joint pathomechanics

Abstract Background Dynamic RSA (dRSA) enables non-invasive 3D motion-tracking of bones and may be used to evaluate in-vivo hip joint kinematics including hip pathomechanics such as femoroacetabular impingement (FAI) and the biomechanical effects of arthroscopic cheilectomy and -rim trimming (ACH). The study aim was to evaluate the kinematic changes in the hip joint after ACH. Methods Seven non-FAI affected human cadaveric hips were CT-scanned and CT-bone models were created. dRSA recordings of the hip joints were acquired at five frames/s during passive flexion, adduction to stop, and internal rotation to stop (FADIR). ACH was performed and dRSA was repeated. dRSA images were analyzed using model-based RSA. Hip joint kinematics before and after ACH were compared pairwise. The volume of removed bone was quantified and compared to the postoperative range of motion (ROM). Results Mean hip internal rotation increased from 19.1 to 21.9° (p = 0.04, Δ2.8°, SD 2.7) after ACH surgery. Mean adduction of 3.9° before and 2.7° after ACH surgery was unchanged (p = 0.48, Δ-1.2°; SD 4.3). Mean flexion angles during dRSA tests were 82.4° before and 80.8° after ACH surgery, which were similar (p = 0.18, Δ-1.6°, SD = 2.7). No correlation between volume of removed bone and ROM was observed. Conclusions A small increase in internal rotation, but not in adduction, was observed after arthroscopic cheilectomy and -rim trimming in cadaver hips. The hip flexion angle of the FADIR test was reproducible. dRSA kinematic analysis is a new and clinically applicable method with good potential to evaluate hip joint kinematics and to test FAI pathomechanics and other surgical corrections of the hip

Designing deep learning studies in cancer diagnostics

The number of publications on deep learning for cancer diagnostics is rapidly increasing, and systems are frequently claimed to perform comparable with or better than clinicians. However, few systems have yet demonstrated real-world medical utility. In this Perspective, we discuss reasons for the moderate progress and describe remedies designed to facilitate transition to the clinic. Recent, presumably influential, deep learning studies in cancer diagnostics, of which the vast majority used images as input to the system, are evaluated to reveal the status of the field. By manipulating real data, we then exemplify that much and varied training data facilitate the generalizability of neural networks and thus the ability to use them clinically. To reduce the risk of biased performance estimation of deep learning systems, we advocate evaluation in external cohorts and strongly advise that the planned analyses, including a predefined primary analysis, are described in a protocol preferentially stored in an online repository. Recommended protocol items should be established for the field, and we present our suggestions