Treatment strategies and clinical outcomes in consecutive patients with locally advanced pancreatic cancer:A multicenter prospective cohort

Introduction: Since current studies on locally advanced pancreatic cancer (LAPC) mainly report from single, high-volume centers, it is unclear if outcomes can be translated to daily clinical practice. This study provides treatment strategies and clinical outcomes within a multicenter cohort of unselected patients with LAPC. Materials and methods: Consecutive patients with LAPC according to Dutch Pancreatic Cancer Group criteria, were prospectively included in 14 centers from April 2015 until December 2017. A centralized expert panel reviewed response according to RECIST v1.1 and potential surgical resectability. Primary outcome was median overall survival (mOS), stratified for primary treatment strategy. Results: Overall, 422 patients were included, of whom 77% (n = 326) received chemotherapy. The majority started with FOLFIRINOX (77%, 252/326) with a median of six cycles (IQR 4-10). Gemcitabine monotherapy was given to 13% (41/326) of patients and nab-paclitaxel/gemcitabine to 10% (33/326), with a median of two (IQR 3-5) and three (IQR 3-5) cycles respectively. The mOS of the entire cohort was 10 months (95%CI 9-11). In patients treated with FOLFIRINOX, gemcitabine monotherapy, or nab-paclitaxel/gemcitabine, mOS was 14 (95%CI 13-15), 9 (95%CI 8-10), and 9 months (95%CI 8-10), respectively. A resection was performed in 13% (32/252) of patients after FOLFIRINOX, resulting in a mOS of 23 months (95%CI 12-34). Conclusion: This multicenter unselected cohort of patients with LAPC resulted in a 14 month mOS and a 13% resection rate after FOLFIRINOX. These data put previous results in perspective, enable us to inform patients with more accurate survival numbers and will support decision-making in clinical practice. (C) 2020 The Authors. Published by Elsevier Ltd

Resectability and Ablatability Criteria for the Treatment of Liver Only Colorectal Metastases:Multidisciplinary Consensus Document from the COLLISION Trial Group

The guidelines for metastatic colorectal cancer crudely state that the best local treatment should be selected from a 'toolbox' of techniques according to patient- and treatment-related factors. We created an interdisciplinary, consensus-based algorithm with specific resectability and ablatability criteria for the treatment of colorectal liver metastases (CRLM). To pursue consensus, members of the multidisciplinary COLLISION and COLDFIRE trial expert panel employed the RAND appropriateness method (RAM). Statements regarding patient, disease, tumor and treatment characteristics were categorized as appropriate, equipoise or inappropriate. Patients with ECOG≤2, ASA≤3 and Charlson comorbidity index ≤8 should be considered fit for curative-intent local therapy. When easily resectable and/or ablatable (stage IVa), (neo)adjuvant systemic therapy is not indicated. When requiring major hepatectomy (stage IVb), neo-adjuvant systemic therapy is appropriate for early metachronous disease and to reduce procedural risk. To downstage patients (stage IVc), downsizing induction systemic therapy and/or future remnant augmentation is advised. Disease can only be deemed permanently unsuitable for local therapy if downstaging failed (stage IVd). Liver resection remains the gold standard. Thermal ablation is reserved for unresectable CRLM, deep-seated resectable CRLM and can be considered when patients are in poor health. Irreversible electroporation and stereotactic body radiotherapy can be considered for unresectable perihilar and perivascular CRLM 0-5cm. This consensus document provides per-patient and per-tumor resectability and ablatability criteria for the treatment of CRLM. These criteria are intended to aid tumor board discussions, improve consistency when designing prospective trials and advance intersociety communications. Areas where consensus is lacking warrant future comparative studies.</p

A theoretical framework to describe communication processes during medical disability assessment interviews

BACKGROUND: Research in different fields of medicine suggests that communication is important in physician-patient encounters and influences satisfaction with these encounters. It is argued that this also applies to the non-curative tasks that physicians perform, such as sickness certification and medical disability assessments. However, there is no conceptualised theoretical framework that can be used to describe intentions with regard to communication behaviour, communication behaviour itself, and satisfaction with communication behaviour in a medical disability assessment context. OBJECTIVE: The objective of this paper is to describe the conceptualization of a model for the communication behaviour of physicians performing medical disability assessments in a social insurance context and of their claimants, in face-to-face encounters during medical disability assessment interviews and the preparation thereof. CONCEPTUALIzATION: The behavioural model, based on the Theory of Planned Behaviour (TPB), is conceptualised for the communication behaviour of social insurance physicians and claimants separately, but also combined during the assessment interview. Other important concepts in the model are the evaluation of communication behaviour (satisfaction), intentions, attitudes, skills, and barriers for communication. CONCLUSION: The conceptualization of the TPB-based behavioural model will help to provide insight into the communication behaviour of social insurance physicians and claimants during disability assessment interviews. After empirical testing of the relationships in the model, it can be used in other studies to obtain more insight into communication behaviour in non-curative medicine, and it could help social insurance physicians to adapt their communication behaviour to their task when performing disability assessment

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

The Prospective Dutch Colorectal Cancer (PLCRC) cohort: real-world data facilitating research and clinical care

Real-world data (RWD) sources are important to advance clinical oncology research and evaluate treatments in daily practice. Since 2013, the Prospective Dutch Colorectal Cancer (PLCRC) cohort, linked to the Netherlands Cancer Registry, serves as an infrastructure for scientific research collecting additional patient-reported outcomes (PRO) and biospecimens. Here we report on cohort developments and investigate to what extent PLCRC reflects the “real-world”. Clinical and demographic characteristics of PLCRC participants were compared with the general Dutch CRC population (n = 74,692, Dutch-ref). To study representativeness, standardized differences between PLCRC and Dutch-ref were calculated, and logistic regression models were evaluated on their ability to distinguish cohort participants from the Dutch-ref (AU-ROC 0.5 = preferred, implying participation independent of patient characteristics). Stratified analyses by stage and time-period (2013–2016 and 2017–Aug 2019) were performed to study the evolution towards RWD. In August 2019, 5744 patients were enrolled. Enrollment increased steeply, from 129 participants (1 hospital) in 2013 to 2136 (50 of 75 Dutch hospitals) in 2018. Low AU-ROC (0.65, 95% CI: 0.64–0.65) indicates limited ability to distinguish cohort participants from the Dutch-ref. Characteristics that remained imbalanced in the period 2017–Aug’19 compared with the Dutch-ref were age (65.0 years in PLCRC, 69.3 in the Dutch-ref) and tumor stage (40% stage-III in PLCRC, 30% in the Dutch-ref). PLCRC approaches to represent the Dutch CRC population and will ultimately meet the current demand for high-quality RWD. Efforts are ongoing to improve multidisciplinary recruitment which will further enhance PLCRC’s representativeness and its contribution to a learning healthcare system

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing