Our experience with van nes rotationplasty for locally advanced lower extremity tumours

Abstract OBJECTIVE: To present an early experience with the time-tested technique of Van Ness Rotationplasty to save distal lmbs. METHODS: Van Nes Rotationplasty for locally advanced lower extremity tumours. A reterorespective audit was conducted at Aga Khan University Hospital, Karachi, and comprised cases of bone and soft tissue sarcoma who underwent Van Ness Rotationplasty over seven years from January 2005 to December 2011. Demographic data, family history, past history, co-morbids, date since diagnosis, duration of symptoms, type of tumour, metastasis, pre-op and post-op functional status, recurrence and survival were collected. RESULTS: Of the 351 cases of bone and soft tissue sarcoma, 9 (2.6%) underwent Van Ness Rotationplasty and were included in the study. The mean duration of symptoms was 7±3SD months (range: 8-41 months). All except 1(11.1%) were osteogenic sarcomas. All except 1(11.1%) involved distal femur. Overall, 7(77.8%) had localised Enneking stage IIB disease. Two (22.2%) patients expired due to metastatic disease, but none had local recurrence. Complete excision of tumour was achieved in all (100%) patients. Longest follow-up was of 34 months while the shortest was of 6 months. No local recurrences were noted. Functional recovery was good. Two (22.2%) patients had simultaneous sciatic nerve repair as part of the primary procedure. Both of them had good motor function at the time of final follow-up. Mean Musculoskeletal Tumour Societyscore was 23.88±2SD. CONCLUSIONS: Van Nes Rotationplasty was found to be a successful alternative to amputation in cases of locally advancedtumours of distal femur or proximal tibia

Osteochondral grafting of knee joint using mosaicplasty

Focal cartilage defects of articular surface-traumatic and degenerative are difficult to treat, thus a variety of surgical techniques have been developed and reported for treatment of such defects. Procedures such as Priddies perforations, microfracture, abrasion chondroplasty have shown long-term results which are often less than adequate. One of the reasons is that all these techniques lead to the formation of fibrocartilage which has inferior mechanical properties as compared to the native hyaline cartilage. Mosaicplasty is a procedure which aims at replacing the lost articular cartilage with hyaline cartilage including underlying bone support, thus providing adequate stability to the cartilage and better cartilage/bone integration. A young man underwent this procedure for recalcitrant knee pain at our institution. At 2 years follow-up, his knee pain has significantly improved. We hereby present medium term results (2 years) of this first case report in local literature

Suicidal bus bombing of French nationals in Pakistan: physical injuries and management of survivors

Abstract BACKGROUND: Suicidal bombing is particularly devastating and an increasingly common form of terrorist violence. In this paper, we present an epidemiologic description of the physical injuries of patients who survived the suicidal bombing attack in the context of the limited medical resources of a developing nation. METHODS: The management of individual patients was reviewed from a preprinted trauma form. Information on the nature of injuries, operative management and hospital course was recorded and data analyzed using the Trauma Registry. RESULTS: Twelve survivors out of 36 bomb blast victims brought to the Aga Khan University Hospital were transferred from primary receiving hospitals. The average number of injuries per patient was eight. The mean Injury Severity Score was 10.8. The majority of patients had secondary and tertiary blast injuries. Most of the survivors had calcaneal injuries; these have not been reported in the literature in similar terrorist attacks. Twelve operative interventions were undertaken. All of the 12 patients were stabilized and evacuated within 24 h of admission. CONCLUSIONS: All of the 12 patients transferred to the Aga Khan University Hospital survived. Unlike the reported injuries, calcaneal fractures were most commonly encountered in the survivors

Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images : a retrospective study

Background: Determining the status of molecular pathways and key mutations in colorectal cancer is crucial for optimal therapeutic decision making. We therefore aimed to develop a novel deep learning pipeline to predict the status of key molecular pathways and mutations from whole-slide images of haematoxylin and eosin-stained colorectal cancer slides as an alternative to current tests. Methods: In this retrospective study, we used 502 diagnostic slides of primary colorectal tumours from 499 patients in The Cancer Genome Atlas colon and rectal cancer (TCGA-CRC-DX) cohort and developed a weakly supervised deep learning framework involving three separate convolutional neural network models. Whole-slide images were divided into equally sized tiles and model 1 (ResNet18) extracted tumour tiles from non-tumour tiles. These tumour tiles were inputted into model 2 (adapted ResNet34), trained by iterative draw and rank sampling to calculate a prediction score for each tile that represented the likelihood of a tile belonging to the molecular labels of high mutation density (vs low mutation density), microsatellite instability (vs microsatellite stability), chromosomal instability (vs genomic stability), CpG island methylator phenotype (CIMP)-high (vs CIMP-low), BRAFmut (vs BRAFWT), TP53mut (vs TP53WT), and KRASWT (vs KRASmut). These scores were used to identify the top-ranked titles from each slide, and model 3 (HoVer-Net) segmented and classified the different types of cell nuclei in these tiles. We calculated the area under the convex hull of the receiver operating characteristic curve (AUROC) as a model performance measure and compared our results with those of previously published methods. Findings: Our iterative draw and rank sampling method yielded mean AUROCs for the prediction of hypermutation (0·81 [SD 0·03] vs 0·71), microsatellite instability (0·86 [0·04] vs 0·74), chromosomal instability (0·83 [0·02] vs 0·73), BRAFmut (0·79 [0·01] vs 0·66), and TP53mut (0·73 [0·02] vs 0·64) in the TCGA-CRC-DX cohort that were higher than those from previously published methods, and an AUROC for KRASmut that was similar to previously reported methods (0·60 [SD 0·04] vs 0·60). Mean AUROC for predicting CIMP-high status was 0·79 (SD 0·05). We found high proportions of tumour-infiltrating lymphocytes and necrotic tumour cells to be associated with microsatellite instability, and high proportions of tumour-infiltrating lymphocytes and a low proportion of necrotic tumour cells to be associated with hypermutation. Interpretation: After large-scale validation, our proposed algorithm for predicting clinically important mutations and molecular pathways, such as microsatellite instability, in colorectal cancer could be used to stratify patients for targeted therapies with potentially lower costs and quicker turnaround times than sequencing-based or immunohistochemistry-based approaches. Funding: The UK Medical Research Council

Development and validation of artificial intelligence-based prescreening of large-bowel biopsies taken in the UK and Portugal: a retrospective cohort study

Background Histopathological examination is a crucial step in the diagnosis and treatment of many major diseases. Aiming to facilitate diagnostic decision making and improve the workload of pathologists, we developed an artificial intelligence (AI)-based prescreening tool that analyses whole-slide images (WSIs) of large-bowel biopsies to identify typical, non-neoplastic, and neoplastic biopsies. Methods This retrospective cohort study was conducted with an internal development cohort of slides acquired from a hospital in the UK and three external validation cohorts of WSIs acquired from two hospitals in the UK and one clinical laboratory in Portugal. To learn the differential histological patterns from digitised WSIs of large-bowel biopsy slides, our proposed weakly supervised deep-learning model (Colorectal AI Model for Abnormality Detection [CAIMAN]) used slide-level diagnostic labels and no detailed cell or region-level annotations. The method was developed with an internal development cohort of 5054 biopsy slides from 2080 patients that were labelled with corresponding diagnostic categories assigned by pathologists. The three external validation cohorts, with a total of 1536 slides, were used for independent validation of CAIMAN. Each WSI was classified into one of three classes (ie, typical, atypical non-neoplastic, and atypical neoplastic). Prediction scores of image tiles were aggregated into three prediction scores for the whole slide, one for its likelihood of being typical, one for its likelihood of being non-neoplastic, and one for its likelihood of being neoplastic. The assessment of the external validation cohorts was conducted by the trained and frozen CAIMAN model. To evaluate model performance, we calculated area under the convex hull of the receiver operating characteristic curve (AUROC), area under the precision-recall curve, and specificity compared with our previously published iterative draw and rank sampling (IDaRS) algorithm. We also generated heat maps and saliency maps to analyse and visualise the relationship between the WSI diagnostic labels and spatial features of the tissue microenvironment. The main outcome of this study was the ability of CAIMAN to accurately identify typical and atypical WSIs of colon biopsies, which could potentially facilitate automatic removing of typical biopsies from the diagnostic workload in clinics. Findings A randomly selected subset of all large bowel biopsies was obtained between Jan 1, 2012, and Dec 31, 2017. The AI training, validation, and assessments were done between Jan 1, 2021, and Sept 30, 2022. WSIs with diagnostic labels were collected between Jan 1 and Sept 30, 2022. Our analysis showed no statistically significant differences across prediction scores from CAIMAN for typical and atypical classes based on anatomical sites of the biopsy. At 0·99 sensitivity, CAIMAN (specificity 0·5592) was more accurate than an IDaRS-based weakly supervised WSI-classification pipeline (0·4629) in identifying typical and atypical biopsies on cross-validation in the internal development cohort (p<0·0001). At 0·99 sensitivity, CAIMAN was also more accurate than IDaRS for two external validation cohorts (p<0·0001), but not for a third external validation cohort (p=0·10). CAIMAN provided higher specificity than IDaRS at some high-sensitivity thresholds (0·7763 vs 0·6222 for 0·95 sensitivity, 0·7126 vs 0·5407 for 0·97 sensitivity, and 0·5615 vs 0·3970 for 0·99 sensitivity on one of the external validation cohorts) and showed high classification performance in distinguishing between neoplastic biopsies (AUROC 0·9928, 95% CI 0·9927–0·9929), inflammatory biopsies (0·9658, 0·9655–0·9661), and atypical biopsies (0·9789, 0·9786–0·9792). On the three external validation cohorts, CAIMAN had AUROC values of 0·9431 (95% CI 0·9165–0·9697), 0·9576 (0·9568–0·9584), and 0·9636 (0·9615–0·9657) for the detection of atypical biopsies. Saliency maps supported the representation of disease heterogeneity in model predictions and its association with relevant histological features. Interpretation CAIMAN, with its high sensitivity in detecting atypical large-bowel biopsies, might be a promising improvement in clinical workflow efficiency and diagnostic decision making in prescreening of typical colorectal biopsies. Funding The Pathology Image Data Lake for Analytics, Knowledge and Education Centre of Excellence; the UK Government's Industrial Strategy Challenge Fund; and Innovate UK on behalf of UK Research and Innovation

Screening of normal endoscopic large bowel biopsies with interpretable graph learning: a retrospective study

Objective To develop an interpretable artificial intelligence algorithm to rule out normal large bowel endoscopic biopsies, saving pathologist resources and helping with early diagnosis. Design A graph neural network was developed incorporating pathologist domain knowledge to classify 6591 whole-slides images (WSIs) of endoscopic large bowel biopsies from 3291 patients (approximately 54% female, 46% male) as normal or abnormal (non-neoplastic and neoplastic) using clinically driven interpretable features. One UK National Health Service (NHS) site was used for model training and internal validation. External validation was conducted on data from two other NHS sites and one Portuguese site. Results Model training and internal validation were performed on 5054 WSIs of 2080 patients resulting in an area under the curve-receiver operating characteristic (AUC-ROC) of 0.98 (SD=0.004) and AUC-precision-recall (PR) of 0.98 (SD=0.003). The performance of the model, named Interpretable Gland-Graphs using a Neural Aggregator (IGUANA), was consistent in testing over 1537 WSIs of 1211 patients from three independent external datasets with mean AUC-ROC=0.97 (SD=0.007) and AUC-PR=0.97 (SD=0.005). At a high sensitivity threshold of 99%, the proposed model can reduce the number of normal slides to be reviewed by a pathologist by approximately 55%. IGUANA also provides an explainable output highlighting potential abnormalities in a WSI in the form of a heatmap as well as numerical values associating the model prediction with various histological features. Conclusion The model achieved consistently high accuracy showing its potential in optimising increasingly scarce pathologist resources. Explainable predictions can guide pathologists in their diagnostic decision-making and help boost their confidence in the algorithm, paving the way for its future clinical adoption

Necrotizing fasciitis of the breast

Necrotizing fasciitis is a potentially fatal condition that can affect any part of the body. It can occur after trauma, around foreign bodies in surgical wounds, or can be idiopathic. We describe a case of necrotizing fasciitis involving the breast following an initial debridement of an inflammatory lesion

A comparison of external fixation alone or combined with intramedullary nailing in the treatment of segmental tibial defects

The purpose of this study was to compare the results of external fixation alone versus external fixation combined with intramedullary nailing in the reconstruction of segmental defects of the tibia resulting from chronic osteomyelitis

Treatment of infected nonunion of the juxta-articular region of the distal tibia

Objective: The purpose of this study was to summarize our clinical results with distraction osteogenesis for the treatment of infected tibial nonunion around the ankle joint

Physicians in Cyberspace: Finding Boundaries

10.1353/asb.2016.0023Asian Bioethics Review84272-28