The mitotic rate as a prognostic biomarker after preoperative radiotherapy for high-grade limb/trunk soft tissue sarcoma.

PURPOSE: Currently there is no generally accepted standardized approach for the pathological evaluation of soft tissue sarcoma (STS) histology appearance after preoperative radiotherapy (PORT). This study aimed to investigate the prognostic value of pathological appearance after PORT for patients with high-grade limb/trunk STS. METHODS: A cohort of 116 patients with high-grade STS of the limb/trunk treated with PORT followed by resection were evaluated. Patient characteristics, imaging tumor morphology (size, volume), and histopathology (mitotic and necrosis rate, viable cell, hyalinization/fibrosis cytopathic effect) were reviewed and reassessed. Disease free survival (DFS) and overall survival (OS) were calculated using the Kaplan-Meier method, and the hazard ratio was derived from Cox proportional hazard models. Two predictive nomograms were calculated based on significant predictors identified. RESULTS: The 5-year DFS and OS were 52.9% and 70.3%, respectively. Tumor size before (HR:1.07, 95%CI: 1.01-1.14) and after PORT (HR:1.08, 95%CI: 1.01-1.14), tumor volume (HR:1.06, 95%CI: 1.01-1.12), mitotic rate after PORT (HR: 1.06, 95%CI: 1.02-1.11), mitotic rate change after PORT (HR:1.04, 95%CI:1.00-1.09) were independent risk factors for DFS. Tumor size before (HR:1.08, 95%CI: 1.03-1.14) and after PORT (HR:1.09, 95%CI: 1.04-1.15), tumor volume (HR:1.05, 95%CI: 1.01-1.09), mitotic rate after PORT (HR: 1.09, 95%CI: 1.04-1.13), mitotic rate change after PORT (HR:1.05, 95%CI:1.01-1.09) were independent risk factors for OS. The C-index of pathologic predictive nomogram based on mitotic rate for DFS and OS were 0.67 and 0.73, respectively. The C-index of morphology-pathology predictive nomogram for OS was 0.79. CONCLUSION: Tumor size before and after PORT, tumor volume, mitotic rate after PORT, mitotic rate change after PORT were independent risk factors for DFS and OS in high-grade STS patients treated with PORT. The mitotic rate, independent of tumor morphology, showed its potential as a prognostic biomarker for pathologic evaluation in patients treated with PORT.

Controversies in orthopaedic oncology.

Chondrosarcoma is the second most common surgically treated primary bone sarcoma. Despite a large number of scientific papers in the literature, there is still significant controversy about diagnostics, treatment of the primary tumour, subtypes, and complications. Therefore, consensus on its day-to-day treatment decisions is needed. In January 2024, the Birmingham Orthopaedic Oncology Meeting (BOOM) attempted to gain global consensus from 300 delegates from over 50 countries. The meeting focused on these critical areas and aimed to generate consensus statements based on evidence amalgamation and expert opinion from diverse geographical regions. In parallel, periprosthetic joint infection (PJI) in oncological reconstructions poses unique challenges due to factors such as adjuvant treatments, large exposures, and the complexity of surgery. The meeting debated two-stage revisions, antibiotic prophylaxis, managing acute PJI in patients undergoing chemotherapy, and defining the best strategies for wound management and allograft reconstruction. The objectives of the meeting extended beyond resolving immediate controversies. It sought to foster global collaboration among specialists attending the meeting, and to encourage future research projects to address unsolved dilemmas. By highlighting areas of disagreement and promoting collaborative research endeavours, this initiative aims to enhance treatment standards and potentially improve outcomes for patients globally. This paper sets out some of the controversies and questions that were debated in the meeting.

Challenges and solutions to system-wide use of precision oncology as the standard of care paradigm.

The personalised oncology paradigm remains challenging to deliver despite technological advances in genomics-based identification of actionable variants combined with the increasing focus of drug development on these specific targets. To ensure we continue to build concerted momentum to improve outcomes across all cancer types, financial, technological and operational barriers need to be addressed. For example, complete integration and certification of the 'molecular tumour board' into 'standard of care' ensures a unified clinical decision pathway that both counteracts fragmentation and is the cornerstone of evidence-based delivery inside and outside of a research setting. Generally, integrated delivery has been restricted to specific (common) cancer types either within major cancer centres or small regional networks. Here, we focus on solutions in real-world integration of genomics, pathology, surgery, oncological treatments, data from clinical source systems and analysis of whole-body imaging as digital data that can facilitate cost-effectiveness analysis, clinical trial recruitment, and outcome assessment. This urgent imperative for cancer also extends across the early diagnosis and adjuvant treatment interventions, individualised cancer vaccines, immune cell therapies, personalised synthetic lethal therapeutics and cancer screening and prevention. Oncology care systems worldwide require proactive step-changes in solutions that include inter-operative digital working that can solve patient centred challenges to ensure inclusive, quality, sustainable, fair and cost-effective adoption and efficient delivery. Here we highlight workforce, technical, clinical, regulatory and economic challenges that prevent the implementation of precision oncology at scale, and offer a systematic roadmap of integrated solutions for standard of care based on minimal essential digital tools. These include unified decision support tools, quality control, data flows within an ethical and legal data framework, training and certification, monitoring and feedback. Bridging the technical, operational, regulatory and economic gaps demands the joint actions from public and industry stakeholders across national and global boundaries.

Nomogram Predicting the Risk of Postoperative Major Wound Complication in Soft Tissue Sarcoma of the Trunk and Extremities after Preoperative Radiotherapy.

Preoperative radiotherapy increases the risk of postoperative wound complication in the treatment of soft tissue sarcoma (STS). This study aims to develop a nomogram for predicting major wound complication (MaWC) after surgery. Using the Oxford University Hospital (OUH) database, a total of 126 STS patients treated with preoperative radiotherapy and surgical resection between 2007 and 2021 were retrospectively reviewed. MaWC was defined as a wound complication that required secondary surgical intervention. Univariate and multivariate regression analyses on the association between MaWC and risk factors were performed. A nomogram was formulated and the areas under the Receiver Operating Characteristic Curves (AUC) were adopted to measure the predictive value of MaWC. A decision curve analysis (DCA) determined the model with the best discriminative ability. The incidence of MaWC was 19%. Age, tumour size, diabetes mellitus and metastasis at presentation were associated with MaWC in the univariate analysis. Age, tumour size, and metastasis at presentation were independent risk factors in the multivariate analysis. The sensitivity and specificity of the predictive model is 0.90 and 0.76, respectively. The AUC value was 0.86. The nomogram constructed in the study effectively predicts the risk of MaWC after preoperative radiotherapy and surgery for STS patients.

Design and establishment of a cancer registry: a literature review.

BACKGROUND: Establishment of a cancer registry is a complex process that requires substantial resources and careful planning. There are numerous resources available to provide guidance for this, which include guidelines and frameworks of varying quality. It is the authors' goal to identify evidence-based recommendations within the literature to help guide the process of designing a new registry with optimal efficiency, workability and data use. The objective of this study is to examine the primary literature for evidence-based recommendations on how to design and establish a cancer registry, with a focus on literature which analyses the performance and usefulness of already established registries or guidelines. METHODS: An electronic search was completed in MEDLINE, CINAHL, EMCARE, SCOPUS and the Cochrane Database of Systematic Reviews. Recommendations were extracted from the identified articles and collated as themes. RESULTS: Nine articles of varying quality were included in the review. Recommendations obtained from the literature included broad themes of the importance of clinician involvement, establishment of clear data definitions, number of variables used, inbuilt strategies to improve quality and completeness of data, considerations of costs, an 'opt-out' strategy for ethics and privacy and flexibility of the system. CONCLUSION: This review concluded that there is a large gap in the primary literature for evidence-based recommendations on the design and establishment of cancer registries. The included articles established a small scope of relevant themes, which were largely non-specific. This area of deficiency provides an opportunity for future research, which would further strengthen the quality of current or new guidelines in cancer registry establishment.

An unusual presentation of metastatic melanoma in the shoulder.

A 72-year-old lady underwent a Copeland hemiarthoplasty of the shoulder for rotator cuff arthropathy with a good functional outcome. Her past medical history included previous management of a malignant melanoma. Several years following arthroplasty surgery, she acutely developed signs and symptoms of prosthetic joint infection. The present case report describes the metastatic spread of malignant melanoma mimicking that of prosthetic sepsis.

Quantification of the heterogeneity of prognostic cellular biomarkers in ewing sarcoma using automated image and random survival forest analysis.

Driven by genomic somatic variation, tumour tissues are typically heterogeneous, yet unbiased quantitative methods are rarely used to analyse heterogeneity at the protein level. Motivated by this problem, we developed automated image segmentation of images of multiple biomarkers in Ewing sarcoma to generate distributions of biomarkers between and within tumour cells. We further integrate high dimensional data with patient clinical outcomes utilising random survival forest (RSF) machine learning. Using material from cohorts of genetically diagnosed Ewing sarcoma with EWSR1 chromosomal translocations, confocal images of tissue microarrays were segmented with level sets and watershed algorithms. Each cell nucleus and cytoplasm were identified in relation to DAPI and CD99, respectively, and protein biomarkers (e.g. Ki67, pS6, Foxo3a, EGR1, MAPK) localised relative to nuclear and cytoplasmic regions of each cell in order to generate image feature distributions. The image distribution features were analysed with RSF in relation to known overall patient survival from three separate cohorts (185 informative cases). Variation in pre-analytical processing resulted in elimination of a high number of non-informative images that had poor DAPI localisation or biomarker preservation (67 cases, 36%). The distribution of image features for biomarkers in the remaining high quality material (118 cases, 104 features per case) were analysed by RSF with feature selection, and performance assessed using internal cross-validation, rather than a separate validation cohort. A prognostic classifier for Ewing sarcoma with low cross-validation error rates (0.36) was comprised of multiple features, including the Ki67 proliferative marker and a sub-population of cells with low cytoplasmic/nuclear ratio of CD99. Through elimination of bias, the evaluation of high-dimensionality biomarker distribution within cell populations of a tumour using random forest analysis in quality controlled tumour material could be achieved. Such an automated and integrated methodology has potential application in the identification of prognostic classifiers based on tumour cell heterogeneity.