Nomogram predicting response after chemoradiotherapy in rectal cancer using sequential PETCT imaging: a multicentric prospective study with external validation.

Abstract Purpose To develop and externally validate a predictive model for pathologic complete response (pCR) for locally advanced rectal cancer (LARC) based on clinical features and early sequential 18 F-FDG PETCT imaging. Materials and methods Prospective data (i.a. THUNDER trial) were used to train ( N =112, MAASTRO Clinic) and validate ( N =78, Universita Cattolica del S. Cuore) the model for pCR (ypT0N0). All patients received long-course chemoradiotherapy (CRT) and surgery. Clinical parameters were age, gender, clinical tumour (cT) stage and clinical nodal (cN) stage. PET parameters were SUV max , SUV mean , metabolic tumour volume (MTV) and maximal tumour diameter, for which response indices between pre-treatment and intermediate scan were calculated. Using multivariate logistic regression, three probability groups for pCR were defined. Results The pCR rates were 21.4% (training) and 23.1% (validation). The selected predictive features for pCR were cT-stage, cN-stage, response index of SUV mean and maximal tumour diameter during treatment. The models' performances (AUC) were 0.78 (training) and 0.70 (validation). The high probability group for pCR resulted in 100% correct predictions for training and 67% for validation. The model is available on the website www.predictcancer.org. Conclusions The developed predictive model for pCR is accurate and externally validated. This model may assist in treatment decisions during CRT to select complete responders for a wait-and-see policy, good responders for extra RT boost and bad responders for additional chemotherapy

Impact of the COVID-19 Pandemic on Colorectal Cancer Care in the Netherlands: A Population-based Study

Contains fulltext : 283493.pdf (Publisher’s version ) (Open Access)INTRODUCTION: The COVID-19 pandemic disrupted health care services worldwide. In the Netherlands, the first confirmed COVID-19 infection was on February 27, 2020. We aimed to investigate the impact of the pandemic on colorectal cancer care in the Netherlands. METHODS: Colorectal cancer patients who were diagnosed in 25 hospitals in weeks 2 to 26 of the year 2020 were selected from the Netherlands Cancer Registry (NCR) and divided in 4 periods. The average number of patients treated per type of initial treatment was analyzed by the Mantel-Haenszel test adjusted for age. Median time between diagnosis and treatment and between (neo)adjuvant therapy and surgery were analyzed by the Mann Whitney test. Percentages of (acute) resection, stoma and (neo)adjuvant therapy were compared using the Chi-squared test. RESULTS: In total, 1,653 patients were included. The patient population changed during the COVID-19 pandemic regarding higher stage and more clinical presentation with ileus at time of diagnosis. Slight changes were found regarding type of initial treatment. Median time between diagnosis and treatment decreased on average by 4.5 days during the pandemic. The proportion of colon cancer patients receiving a stoma significantly increased with 6.5% during the pandemic. No differences were found in resection rate and treatment with (neo)adjuvant therapy. CONCLUSION: Despite the disruptive impact of the COVID-19 pandemic on global health care, the impact on colorectal cancer care in the Netherlands was limited

'Rapid Learning health care in oncology' – An approach towards decision support systems enabling customised radiotherapy' ☆ ☆☆

AbstractPurposeAn overview of the Rapid Learning methodology, its results, and the potential impact on radiotherapy.Material and resultsRapid Learning methodology is divided into four phases. In the data phase, diverse data are collected about past patients, treatments used, and outcomes. Innovative information technologies that support semantic interoperability enable distributed learning and data sharing without additional burden on health care professionals and without the need for data to leave the hospital. In the knowledge phase, prediction models are developed for new data and treatment outcomes by applying machine learning methods to data. In the application phase, this knowledge is applied in clinical practice via novel decision support systems or via extensions of existing models such as Tumour Control Probability models. In the evaluation phase, the predictability of treatment outcomes allows the new knowledge to be evaluated by comparing predicted and actual outcomes.ConclusionPersonalised or tailored cancer therapy ensures not only that patients receive an optimal treatment, but also that the right resources are being used for the right patients. Rapid Learning approaches combined with evidence based medicine are expected to improve the predictability of outcome and radiotherapy is the ideal field to study the value of Rapid Learning. The next step will be to include patient preferences in the decision making

Treatment and overall survival of four types of non-metastatic periampullary cancer:nationwide population-based cohort study

Background: Periampullary adenocarcinoma consists of pancreatic adenocarcinoma (PDAC), distal cholangiocarcinoma (DC), ampullary cancer (AC), and duodenal adenocarcinoma (DA). The aim of this study was to assess treatment modalities and overall survival by tumor origin. Methods: Patients diagnosed with non-metastatic periampullary cancer in 2012–2018 were identified from the Netherlands Cancer Registry. OS was studied with Kaplan–Meier analysis and multivariable Cox regression analyses, stratified by origin. Results: Among the 8758 patients included, 68% had PDAC, 13% DC, 12% AC, and 7% DA. Resection was performed in 35% of PDAC, 56% of DC, 70% of AC, and 59% of DA. Neoadjuvant and/or adjuvant therapy was administered in 22% of PDAC, 7% of DC, 7% of AC, and 12% of DA. Three-year OS was highest for AC (37%) and DA (34%), followed by DC (21%) and PDAC (11%). Adjuvant therapy was associated with improved OS among PDAC (HR = 0.62; 95% CI 0.55–0.69) and DC (HR = 0.69; 95% CI 0.48–0.98), but not AC (HR = 0.87; 95% CI 0.62–1.22) and DA (HR = 0.85; 95% CI 0.48–1.50). Conclusion: This retrospective study identified considerable differences in treatment modalities and OS between the four periampullary cancer origins in daily clinical practice. An improved OS after adjuvant chemotherapy could not be demonstrated in patients with AC and DA

Підручник зі слов’янського фольклору в Канаді

Рецензія на підручник: Kononenko N. Slavic Folklore: a Handbook. - Westport (Connecticut); London: Greenwood Press, 2007. - 209 pp

Neoadjuvant chemoradiotherapy plus surgery versus active surveillance for oesophageal cancer: A stepped-wedge cluster randomised trial

Background: Neoadjuvant chemoradiotherapy (nCRT) plus surgery is a standard treatment for locally advanced oesophageal cancer. With this treatment, 29% of patients have a pathologically complete response in the resection specimen. This provides the rationale for investigating an active surveillance approach. The aim of this study is to assess the (cost-)effectiveness of active surveillance vs. standard oesophagectomy after nCRT for oesophageal cancer. Methods: This is a phase-III multi-centre, stepped-wedge cluster randomised controlled trial. A total of 300 patients with clinically complete response (cCR, i.e. no local or disseminated disease proven by histology) after nCRT will be randomised to show non-inferiority of active surveillance to standard oesophagectomy (non-inferiority margin 15%, intra-correlation coefficient 0.02, power 80%, 2-sided α 0.05, 12% drop-out). Patients will undergo a first clinical response evaluation (CRE-I) 4-6 weeks after nCRT, consisting of endoscopy with bite-on-bite biopsies of the primary tumour site and other suspected lesions. Clinically complete responders will undergo a second CRE (CRE-II), 6-8 weeks after CRE-I. CRE-II will include 18F-FDG-PET-CT, followed by endoscopy with bite-on-bite biopsies and ultra-endosonography plus fine needle aspiration of suspected lymph nodes and/or PET- positive lesions. Patients with cCR at CRE-II will be assigned to oesophagectomy (first phase) or active surveillance (second phase of the study). The duration of the first phase is determined randomly over the 12 centres, i.e., stepped-wedge cluster design. Patients in the active surveillance arm will undergo diagnostic evaluations similar to CRE-II at 6/9/12/16/20/24/30/36/48 and 60 months after nCRT. In this arm, oesophagectomy will be offered only to patients in whom locoregional regrowth is highly suspected or proven, without distant dissemination. The main study parameter is overall survival; secondary endpoints include percentage of patients who do not undergo surgery, quality of life, clinical irresectability (cT4b) rate, radical resection rate, postoperative complications, progression-free survival, distant dissemination rate, and cost-effectiveness. We hypothesise that active surveillance leads to non-inferior survival, improved quality of life and a reduction in costs, compared to standard oesophagectomy. Discussion: If active surveillance and surgery as needed after nCRT leads to non-inferior survival compared to standard oesophagectomy, this organ-sparing approach can be implemented as a standard of care