Effect of Bone Metastasis Cancer Board on Spinal Surgery Outcomes: A Retrospective Study.

Background and Objectives: Bone metastasis cancer boards (BMCBs) focusing on the management of bone metastases have been gathering much attention. However, the association of BMCBs with spinal surgery in patients with spinal metastases remains unclear. In this retrospective single-center observational study, we aimed to clarify the effect of a BMCB on spinal metastasis treatment. Materials and Methods: We reviewed consecutive cases of posterior decompression and/or instrumentation surgery for metastatic spinal tumors from 2008 to 2019. The BMCB involved a team of specialists in orthopedics, rehabilitation medicine, radiation oncology, radiology, palliative supportive care, oncology, and hematology. We compared demographics, eastern cooperative oncology group performance status (ECOGPS), Barthel index (BI), number of overall versus emergency surgeries, and primary tumors between patients before (2008-2012) and after (2013-2019) BMCB establishment. Results: A total of 226 patients including 33 patients before BMCB started were enrolled; lung cancer was the most common primary tumor. After BMCB establishment, the mean patient age was 5 years older (p = 0.028), the mean operating time was 34 min shorter (p = 0.025), the mean hospital stay was 34.5 days shorter (p < 0.001), and the mean BI before surgery was 12 points higher (p = 0.049) than before. Moreover, the mean number of surgeries per year increased more than fourfold to 27.6 per year (p < 0.01) and emergency surgery rates decreased from 48.5% to 29.0% (p = 0.041). Patients with an unknown primary tumor before surgery decreased from 24.2% to 9.3% (p = 0.033). Postoperative deterioration rates from 1 to 6 months after surgery of ECOGPS and BI after BMCB started were lower than before (p = 0.045 and p = 0.027, respectively). Conclusion: The BMCB decreased the emergency surgery and unknown primary tumor rate despite an increase in the overall number of spinal surgeries. The BMCB also contributed to shorter operation times, shorter hospital stays, and lower postoperative deterioration rates of ECOGPS and BI.

Use of the KT-MCC strategy to improve the quality of decision making for multidisciplinary cancer conferences: a pilot study.

BACKGROUND: Multidisciplinary Cancer Conferences (MCCs) are increasingly used to guide treatment decisions for patients with cancer, though numerous barriers to optimal MCC decision-making quality have been identified. We aimed to improve the quality of MCC decision making through the use of an implementation bundle titled the KT-MCC Strategy. The Strategy included use of discussion tools (standard case intake tool and a synoptic discussion tool), workshops, MCC team and chair training, and audit and feedback. Implementation strategies were selected using a theoretically-rooted and integrated KT approach, meaning members of the target population (MCC participants) assisted with the design and implementation of the intervention and strategies. We evaluated implementation quality of the KT-MCC Strategy and initial signals of impact on decision making quality. METHODS: This was a before-and-after study design among 4 MCC teams. Baseline data (before-phase) were collected for a period of 2 months to assess the quality of MCC decision making. Study teams selected the intervention strategies they wished to engage with. Post-intervention data (after-phase) were collected for 4 months. Implementation quality outcomes included reach, adherence/fidelity and adaptation. We also evaluated feasibility of data management. Decision making quality was evaluated on a per-case and per-round level using the MTB-MODe and MDT-OARS tools, respectively. RESULTS: There were a total of 149 cases and 23 MCCs observed in the before phase and 260 cases and 35 MCCs observed in the after phase. Teams implemented 3/5 strategies; adherence to selected strategies varied by MCC team. The per-round quality of MCCs improved by 11% (41.0 to 47.3, p = < 0.0001). The quality of per-case decision-making did not improve significantly (32.3 to 32.6, p = 0.781). CONCLUSION: While per round MCC decision making quality improved significantly, per-case decision-making quality did not. We posit that the limited improvements on decision making quality may be attributed to implementation quality gaps, including a lack of uptake of and adherence to theoretically-identified implementation strategies. Our findings highlight the importance of evaluating implementation quality and processes, iterative testing, and engagement of key gatekeepers in the implementation process.

Use of the theoretical domains framework and behaviour change wheel to develop a novel intervention to improve the quality of multidisciplinary cancer conference decision-making.

BACKGROUND: Multidisciplinary Cancer Conferences (MCCs) are prospective meetings involving cancer specialists to discuss treatment plans for patients with cancer. Despite reported gaps in MCC quality, there have been few efforts to improve its functioning. The purpose of this study was to use theoretically-rooted knowledge translation (KT) theories and frameworks to inform the development of a strategy to improve MCC decision-making quality. METHODS: A multi-phased approach was used to design an intervention titled the KT-MCC Strategy. First, key informant interviews framed using the Theoretical Domains Framework (TDF) were conducted with MCC participants to identify barriers and facilitators to optimal MCC decision-making. Second, identified TDF domains were mapped to corresponding strategies using the COM-B Behavior Change Wheel to develop the KT-MCC Strategy. Finally, focus groups with MCC participants were held to confirm acceptability of the proposed KT-MCC Strategy. RESULTS: Data saturation was reached at n = 21 interviews. Twenty-seven barrier themes and 13 facilitator themes were ascribed to 11 and 10 TDF domains, respectively. Differences in reported barriers by physician specialty were observed. The resulting KT-MCC Strategy included workshops, chair training, team training, standardized intake forms and a synoptic discussion checklist, and, audit and feedback. Focus groups (n = 3, participants 18) confirmed the acceptability of the identified interventions. CONCLUSION: Myriad factors were found to influence MCC decision making. We present a novel application of the TDF and COM-B to the context of MCCs. We comprehensively describe the barriers and facilitators that impact MCC decision making and propose strategies that may positively impact the quality of MCC decision making.

How to Design and Foster Thoracic Oncology Multidisciplinary Cancer Conferences.

In this chapter, the authors review and discuss the literature on multidisciplinary cancer conferences (MCCs, aka tumor boards), clarifying the terminology, showing the evolution of the field, and providing an evidence-based perspective on positive outcomes, best practices, factors influencing the quality of MCCs, evaluation tools to assess the quality of MCCs, and quality improvement interventions for MCCs. The authors then discuss some perspectives from their MCC and initiatives that they undertook to improve the work of their team and the care that they provide to patients in the area of thoracic oncology.

Automatized Hepatic Tumor Volume Analysis of Neuroendocrine Liver Metastases by Gd-EOB MRI-A Deep-Learning Model to Support Multidisciplinary Cancer Conference Decision-Making.

BACKGROUND: Rapid quantification of liver metastasis for diagnosis and follow-up is an unmet medical need in patients with secondary liver malignancies. We present a 3D-quantification model of neuroendocrine liver metastases (NELM) using gadoxetic-acid (Gd-EOB)-enhanced MRI as a useful tool for multidisciplinary cancer conferences (MCC). METHODS: Manual 3D-segmentations of NELM and livers (149 patients in 278 Gd-EOB MRI scans) were used to train a neural network (U-Net architecture). Clinical usefulness was evaluated in another 33 patients who were discussed in our MCC and received a Gd-EOB MRI both at baseline and follow-up examination (n = 66) over 12 months. Model measurements (NELM volume; hepatic tumor load (HTL)) with corresponding absolute (ΔabsNELM; ΔabsHTL) and relative changes (ΔrelNELM; ΔrelHTL) between baseline and follow-up were compared to MCC decisions (therapy success/failure). RESULTS: Internal validation of the model's accuracy showed a high overlap for NELM and livers (Matthew's correlation coefficient (φ): 0.76/0.95, respectively) with higher φ in larger NELM volume (φ = 0.80 vs. 0.71; p = 0.003). External validation confirmed the high accuracy for NELM (φ = 0.86) and livers (φ = 0.96). MCC decisions were significantly differentiated by all response variables (ΔabsNELM; ΔabsHTL; ΔrelNELM; ΔrelHTL) (p < 0.001). ΔrelNELM and ΔrelHTL showed optimal discrimination between therapy success or failure (AUC: 1.000; p < 0.001). CONCLUSION: The model shows high accuracy in 3D-quantification of NELM and HTL in Gd-EOB-MRI. The model's measurements correlated well with MCC's evaluation of therapeutic response.