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A CT-based radiomics classification model for the prediction of histological type and tumour grade in retroperitoneal sarcoma (RADSARC-R): a retrospective multicohort analysis.
Get PDFBACKGROUND: Retroperitoneal sarcomas are tumours with a poor prognosis. Upfront characterisation of the tumour is difficult, and under-grading is common. Radiomics has the potential to non-invasively characterise the so-called radiological phenotype of tumours. We aimed to develop and independently validate a CT-based radiomics classification model for the prediction of histological type and grade in retroperitoneal leiomyosarcoma and liposarcoma. METHODS: A retrospective discovery cohort was collated at our centre (Royal Marsden Hospital, London, UK) and an independent validation cohort comprising patients recruited in the phase 3 STRASS study of neoadjuvant radiotherapy in retroperitoneal sarcoma. Patients aged older than 18 years with confirmed primary leiomyosarcoma or liposarcoma proceeding to surgical resection with available contrast-enhanced CT scans were included. Using the discovery dataset, a CT-based radiomics workflow was developed, including manual delineation, sub-segmentation, feature extraction, and predictive model building. Separate probabilistic classifiers for the prediction of histological type and low versus intermediate or high grade tumour types were built and tested. Independent validation was then performed. The primary objective of the study was to develop radiomic classification models for the prediction of retroperitoneal leiomyosarcoma and liposarcoma type and histological grade. FINDINGS: 170 patients recruited between Oct 30, 2016, and Dec 23, 2020, were eligible in the discovery cohort and 89 patients recruited between Jan 18, 2012, and April 10, 2017, were eligible in the validation cohort. In the discovery cohort, the median age was 63 years (range 27-89), with 83 (49%) female and 87 (51%) male patients. In the validation cohort, median age was 59 years (range 33-77), with 46 (52%) female and 43 (48%) male patients. The highest performing model for the prediction of histological type had an area under the receiver operator curve (AUROC) of 0·928 on validation, based on a feature set of radiomics and approximate radiomic volume fraction. The highest performing model for the prediction of histological grade had an AUROC of 0·882 on validation, based on a radiomics feature set. INTERPRETATION: Our validated radiomics model can predict the histological type and grade of retroperitoneal sarcomas with excellent performance. This could have important implications for improving diagnosis and risk stratification in retroperitoneal sarcomas. FUNDING: Wellcome Trust, European Organisation for Research and Treatment of Cancer-Soft Tissue and Bone Sarcoma Group, the National Institutes for Health, and the National Institute for Health and Care Research Biomedical Research Centre at The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research
Differences in Lymph Node Metastases Patterns Among Non-pancreatic Periampullary Cancers and Histologic Subtypes: An International Multicenter Retrospective Cohort Study and Systematic Review
No full textAbstract
Background
Standard lymphadenectomy for pancreatoduodenectomy is defined for pancreatic ductal adenocarcinoma and adopted for patients with non-pancreatic periampullary cancer (NPPC), ampullary adenocarcinoma (AAC), distal cholangiocarcinoma (dCCA), or duodenal adenocarcinoma (DAC). This study aimed to compare the patterns of lymph node metastases among the different NPPCs in a large series and in a systematic review to guide the discussion on surgical lymphadenectomy and pathology assessment.
Methods
This retrospective cohort study included patients after pancreatoduodenectomy for NPPC with at least one lymph node metastasis (2010–2021) from 24 centers in nine countries. The primary outcome was identification of lymph node stations affected in case of a lymph node metastasis per NPPC. A separate systematic review included studies on lymph node metastases patterns of AAC, dCCA, and DAC.
Results
The study included 2367 patients, of whom 1535 had AAC, 616 had dCCA, and 216 had DAC. More patients with pancreatobiliary type AAC had one or more lymph node metastasis (67.2% vs 44.8%; P < 0.001) compared with intestinal-type, but no differences in metastasis pattern were observed. Stations 13 and 17 were most frequently involved (95%, 94%, and 90%). Whereas dCCA metastasized more frequently to station 12 (13.0% vs 6.4% and 7.0%, P = 0.005), DAC metastasized more frequently to stations 6 (5.0% vs 0% and 2.7%; P < 0.001) and 14 (17.0% vs 8.4% and 11.7%, P = 0.015).
Conclusion
This study is the first to comprehensively demonstrate the differences and similarities in lymph node metastases spread among NPPCs, to identify the existing research gaps, and to underscore the importance of standardized lymphadenectomy and pathologic assessment for AAC, dCCA, and DAC.
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Advanced practice in radiotherapy across Europe: stakeholders' perceptions of implementation and evolution.
No full textINTRODUCTION: Adapting radiotherapy services with workforce innovation using skills-mix or task-shifting optimises resources, supporting current and future demands. Advanced practitioners (APs) work at a different level of practice (beyond initial registration) across four pillars: clinical practice, leadership and management, education, and research. There is limited cross-country research on the advanced therapeutic radiographers/radiation therapists (TR/RTTs), particularly in Europe. This study aimed to investigate European radiotherapy stakeholders' perceptions regarding current and future advanced practice (AP). METHODS: From June to September 2022, one-to-one online semi-structured interviews were conducted in English, and audio and video were recorded. Full verbatim audio files were independently transcribed and checked by interviewer and interviewees. Braun and Clarke's seven steps guided the thematic analysis (using NVivo). RESULTS: Thirty-three interviewees working or studying in 16 European countries represented practitioners (n=14), managers (n=6), educators (n=4), professional bodies (n=4), students (n=3), and regulators (n=2). Four overarching themes emerged: "AP drivers and outcomes", "AP challenges vs enablers", "Current vs future AP", "Becoming and being advanced practitioner". Participants identified research as the neglected AP pillar due to a lack of protected time, limited staff skills, no research culture, no funding, workload, and clinical priorities. Interviewees highlighted the importance of consistency in job titles, harmonisation of education models and curricula, definition of AP requirements, and support for all AP pillars through job plans and workforce planning. CONCLUSION: Neither the profession nor education of TR/RTTs are harmonised across Europe, which is highly reflected in advanced-level practice. Advanced TR/RTTs should work across all pillars, including research, and these should be embedded in master's programmes, including leadership. IMPLICATIONS FOR PRACTICE: This study highlights a policy gap in the education and practice of APs in radiotherapy
The PS4-likelihood ratio calculator: flexible allocation of evidence weighting for case-control data in variant classification.
No full textBACKGROUND: The 2015 American College of Medical Genetics/Association of Molecular Pathology (ACMG/AMP) variant classification framework specifies that case-control observations can be scored as 'strong' evidence (PS4) towards pathogenicity. METHODS: We developed the PS4-likelihood ratio calculator (PS4-LRCalc) for quantitative evidence assignment based on the observed variant frequencies in cases and controls. Binomial likelihoods are computed for two models, each defined by prespecified OR thresholds. Model 1 represents the hypothesis of association between variant and phenotype (eg, OR≥5) and model 2 represents the hypothesis of non-association (eg, OR≤1). RESULTS: PS4-LRCalc enables continuous quantitation of evidence for variant classification expressed as a likelihood ratio (LR), which can be log-converted into log LR (evidence points). Using PS4-LRCalc, observed data can be used to quantify evidence towards either pathogenicity or benignity. Variants can also be evaluated against models of different penetrance. The approach is applicable to balanced data sets generated for more common phenotypes and smaller data sets more typical in very rare disease variant evaluation. CONCLUSION: PS4-LRCalc enables flexible evidence quantitation on a continuous scale for observed case-control data. The converted LR is amenable to incorporation into the now widely used 2018 updated Bayesian ACMG/AMP framework
Deciphering the genetics and mechanisms of predisposition to multiple myeloma.
No full textMultiple myeloma (MM) is an incurable malignancy of plasma cells. Epidemiological studies indicate a substantial heritable component, but the underlying mechanisms remain unclear. Here, in a genome-wide association study totaling 10,906 cases and 366,221 controls, we identify 35 MM risk loci, 12 of which are novel. Through functional fine-mapping and Mendelian randomization, we uncover two causal mechanisms for inherited MM risk: longer telomeres; and elevated levels of B-cell maturation antigen (BCMA) and interleukin-5 receptor alpha (IL5RA) in plasma. The largest increase in BCMA and IL5RA levels is mediated by the risk variant rs34562254-A at TNFRSF13B. While individuals with loss-of-function variants in TNFRSF13B develop B-cell immunodeficiency, rs34562254-A exerts a gain-of-function effect, increasing MM risk through amplified B-cell responses. Our results represent an analysis of genetic MM predisposition, highlighting causal mechanisms contributing to MM development
Quantitative diffusion-weighted MRI response assessment in rhabdomyosarcoma: an international retrospective study on behalf of the European paediatric Soft tissue sarcoma Study Group Imaging Committee.
No full textOBJECTIVE: To investigate the feasibility of diffusion-weighted magnetic resonance imaging (DW-MRI) as a predictive imaging marker after neoadjuvant chemotherapy in patients with rhabdomyosarcoma. MATERIAL AND METHODS: We performed a multicenter retrospective study including pediatric, adolescent and young adult patients with rhabdomyosarcoma, Intergroup Rhabdomyosarcoma Study group III/IV, treated according to the European paediatric Soft tissue sarcoma Study Group (EpSSG) RMS2005 or MTS2008 studies. DW-MRI was performed according to institutional protocols. We performed two-dimensional single-slice tumor delineation. Areas of necrosis or hemorrhage were delineated to be excluded in the primary analysis. Mean, median and 5th and 95th apparent diffusion coefficient (ADC) were extracted. RESULTS: Of 134 included patients, 82 had measurable tumor at diagnosis and response and DW-MRI scans of adequate quality and were included in the analysis. Technical heterogeneity in scan acquisition protocols and scanners was observed. Mean ADC at diagnosis was 1.1 (95% confidence interval [CI]: 1.1-1.2) (all ADC expressed in * 10-3 mm2/s), versus 1.6 (1.5-1.6) at response assessment. The 5th percentile ADC was 0.8 (0.7-0.9) at diagnosis and 1.1 (1.0-1.2) at response. Absolute change in mean ADC after neoadjuvant chemotherapy was 0.4 (0.3-0.5). Exploratory analyses for association between ADC and clinical parameters showed a significant difference in mean ADC at diagnosis for alveolar versus embryonal histology. Landmark analysis at nine weeks after the date of diagnosis showed no significant association (hazard ratio 1.3 [0.6-3.2]) between the mean ADC change and event-free survival. CONCLUSION: A significant change in the 5th percentile and the mean ADC after chemotherapy was observed. Strong heterogeneity was identified in DW-MRI acquisition protocols between centers and in individual patients
Effect of cross-platform gene-expression, computational methods on breast cancer subtyping in PALOMA-2 and PALLET studies.
No full textIntrinsic breast cancer molecular subtyping (IBCMS) provides significant prognostic information for patients with breast cancer and helps determine treatment. This study compared IBCMS methods on various gene-expression platforms in PALOMA-2 and PALLET trials. PALOMA-2 tumor samples were profiled using EdgeSeq and nanostring and subtyped with AIMS, PAM50, and research-use-only (ruo)Prosigna. PALLET tumor biopsies were profiled using mRNA sequencing and subtyped with AIMS and PAM50. In PALOMA-2 (n = 222), a 54% agreement was observed between results from AIMS and gold-standard ruoProsigna, with AIMS assigning 67% basal-like to HER2-enriched. In PALLET (n = 224), a 69% agreement was observed between results from PAM50 and AIMS. Different IBCMS methods may lead to different results and could misguide treatment selection; hence, a standardized clinical PAM50 assay and computational approach should be used.Trial number: NCT01740427
Deep Learning for Automatic Segmentation, Treatment Planning and MRI Quantitative Analysis of Cervical Cancer
No full textCervical cancer is a threatening global health concern for women, typically treated with radiation therapy (RT) in advanced stages, traditionally planned using computed tomography (CT) scans. However, advancements in medical imaging, particularly the incorporation of magnetic resonance imaging (MRI), have offered enhanced clarity in RT treatments due to superior soft-tissue contrast, exemplified by MRI-based linear accelerators (MR-Linac or MRL). Despite such advancements, manual delineation of regions of interest (ROIs) in treatment plans remains a limitation due to its time-consuming nature and susceptibility to variabilities. Addressing this, this thesis explores the synthesis of MRL images from CT scans through a patch-based cycle-consistent generative adversarial network (Cycle-GAN), aimed at enhancing the segmentation of organs-at-risk (OARs) for MRgRT. By leveraging an existing CT-based repository (n=212) at the Royal Marsden Hospital (RMH), this methodology broadens the training dataset significantly, achieving improved segmentation outcomes with acceptance rates of up to 100% and 98%, as score by two radiation oncologists, for treatment planning on MRL data. Furthermore, segmentation challenges due to the intricacies in the tumour's appearance and its unclear boundaries necessitate robust models and extensive datasets for efficient learning of the representation of disease patterns. This research, therefore, employs a transfer learning approach to enhance the segmentation of cervical cancer by applying semantic knowledge from a substantial external dataset (n=206) to a smaller, in-house dataset (n=21), demonstrating the viability and efficacy of this technique in boosting segmentation accuracy. A novel multi-head architecture, utilising b1000 diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) maps, was introduced, indicating improved performance in segmentation tasks and yielding a Dice similarity coefficient (DSC) of 0.82, compared to 0.79 from conventional multi-channel training. Moreover, a service evaluation employing magnetic resonance fingerprinting (MRF) was conducted to discern the changes in T1 and T2 relaxation times before and after treatment in 7 patients, revealing no significant shifts in values across the general population but significant changes in 4 individual cases. The contours generated by the segmentation algorithm were in strong agreement with the expert-defined contours. Although these findings are preliminary, they lay the groundwork for integrating quantitative MRI with AI for enhanced characterisation of pelvic malignancies. In conclusion, this thesis makes pivotal contributions towards refining medical image segmentation technologies and methodologies, offering advanced representation learning to bridge semantic differences between varying diseases and acquisition protocols. These developments pave the way for future research, expanding the prospects of integrating more refined and accurate AI-driven technologies in clinical decision-making processes and applications beyond pelvic malignancies
Advancing patient-centric care: integrating patient reported outcomes for tolerability assessment in early phase clinical trials - insights from an expert virtual roundtable.
No full textEarly phase clinical trials provide an initial evaluation of therapies' risks and benefits to patients, including safety and tolerability, which typically relies on reporting outcomes by investigator and laboratory assessments. Use of patient-reported outcomes (PROs) to inform risks (tolerability) and benefits (improvement in disease symptoms) is more common in later than early phase trials. We convened a two-day expert roundtable covering: (1) the necessity and feasibility of a universal PRO core conceptual model for early phase trials; (2) the practical integration of PROs in early phase trials to inform tolerability assessment, guide dose decisions, or as real-time safety alerts to enhance investigator-reported adverse events. Participants (n = 22) included: patient advocates, regulators, clinicians, statisticians, pharmaceutical representatives, and PRO methodologists working across diverse clinical areas. In this manuscript, we report major recommendations resulting from the roundtable discussions corresponding to each theme. Additionally, we highlight priority areas necessitating further investigation
