Objectives: In the SPRINT trial, 18% of patients with a tibial shaft fracture (TSF) treated with intramedullary nailing (IMN) had one or more unplanned subsequent surgical procedures. It is... Show moreObjectives: In the SPRINT trial, 18% of patients with a tibial shaft fracture (TSF) treated with intramedullary nailing (IMN) had one or more unplanned subsequent surgical procedures. It is clinically relevant for surgeon and patient to anticipate unplanned secondary procedures, other than operations that can be readily expected such as reconstructive procedures for soft tissue defects. Therefore, the objective of this study was to develop a machine learning (ML) prediction model using the SPRINT data that can give individual patients and their care team an estimate of their particular probability of an unplanned second surgery. Methods: Patients from the SPRINT trial with unilateral TSFs were randomly divided into a training set (80%) and test set (20%). Five ML algorithms were trained in recognizing patterns associated with subsequent surgery in the training set based on a subset of variables identified by random forest algorithms. Performance of each ML algorithm was evaluated and compared based on (1) area under the ROC curve, (2) calibration slope and intercept, and (3) the Brier score. Results: Total data set comprised 1198 patients, of whom 214 patients (18%) underwent subsequent surgery. Seven variables were used to train ML algorithms: (1) Gustilo-Anderson classification, (2) Tscherne classification, (3) fracture location, (4) fracture gap, (5) polytrauma, (6) injury mechanism, and (7) OTA/AO classification. The best-performing ML algorithm had an area under the ROC curve, calibration slope, calibration intercept, and the Brier score of 0.766, 0.954, -0.002, and 0.120 in the training set and 0.773, 0.922, 0, and 0.119 in the test set, respectively. Conclusions: An ML algorithm was developed to predict the probability of subsequent surgery after IMN for TSFs. This ML algorithm may assist surgeons to inform patients about the probability of subsequent surgery and might help to identify patients who need a different perioperative plan or a more intensive approach. Show less
Ryan, N.A.J.; Walker, T.D.J.; Bolton, J.; Haar, N. ter; Wezel, T. van; Glaire, M.A.; ... ; Crosbie, E.J. 2021
Simple Summary Endometrial cancers can arise due to an error in DNA mending known as mismatch repair. This can happen because of an error in the cancer itself (somatic) or due to an inherited error... Show moreSimple Summary Endometrial cancers can arise due to an error in DNA mending known as mismatch repair. This can happen because of an error in the cancer itself (somatic) or due to an inherited error (Lynch syndrome). Treatment trials have considered endometrial cancers caused by either of these errors as identical. As it is easier to recruit people with Lynch syndrome, they may be overrepresented in this group despite being less numerous in clinical practice. This would not be an issue if somatic and Lynch syndrome-related endometrial cancers were similar at a molecular level. The data presented herein, however, indicates that these two routes to mismatch repair, although sharing many similarities, lead to endometrial cancers with distinct molecular and pathological features. This may explain the range of outcomes observed in clinical trials of endometrial cancers with mismatch repair errors. Background: Mismatch repair deficient (MMRd) tumours may arise from somatic events acquired during carcinogenesis or in the context of Lynch syndrome (LS), an inherited cancer predisposition condition caused by germline MMR pathogenic variants. Our aim was to explore whether sporadic and hereditary MMRd endometrial cancers (EC) display distinctive tumour biology. Methods: Clinically annotated LS-EC were collected. Histological slide review was performed centrally by two specialist gynaecological pathologists. Mutational analysis was by a bespoke 75- gene next-generation sequencing panel. Comparisons were made with sporadic MMRd EC. Multiple correspondence analysis was used to explore similarities and differences between the cohorts. Results: After exclusions, 135 LS-EC underwent independent histological review, and 64 underwent mutational analysis. Comparisons were made with 59 sporadic MMRd EC. Most tumours were of endometrioid histological subtype (92% LS-EC and 100% sporadic MMRd EC, respectively, p = NS). Sporadic MMRd tumours had significantly fewer tumour infiltrating lymphocytes (p <= 0.0001) and showed more squamous/mucinous differentiation than LS-EC (p = 0.04/p = 0.05). PTEN mutations were found in 88% sporadic MMRd and 61% LS-EC, respectively (p < 0.001). Sporadic MMRd tumours had significantly more mutations in PDGFRA, ALK, IDH1, CARD11, CIC, MED12, CCND1, PTPN11, RB1 and KRAS, while LS-EC showed more mutations affecting SMAD4 and ARAF. LS-EC showed a propensity for TGF-beta signalling disruption. Cluster analysis found that wild type PTEN associates predominantly with LS-EC, whilst co-occurring mutations in PTEN, PIK3CA and KRAS predict sporadic MMRd EC. Conclusions: Whilst MMRd EC of hereditary and sporadic aetiology may be difficult to distinguish by histology alone, differences in infiltrating immune cell counts and mutational profile may predict heterogenous responses to novel targeted therapies and warrant further study. Show less
Crosbie, E.J.; Ryan, N.A.J.; Arends, M.J.; Bosse, T.; Burn, J.; Cornes, J.M.; ... ; Manchester Int Consensus Grp 2019
