Nadeem R. Abu-Rustum
Andrea Maurichi, Francesco Barretta, Roberto Patuzzo, Rosalba Miceli, Gianfranco Gallino, Ilaria Mattavelli, Consuelo Barbieri, Andrea Leva, Martina Angi, Francesco Baldo Lanza, Giuseppe Spadola, Mara Cossa, Francesco Nesa, Umberto Cortinovis, Laura Sala, Lorenza Di Guardo, Carolina Cimminiello, Michele Del Vecchio, Barbara Valeri, and Mario Santinami
Background: Prognostic parameters in sentinel node (SN)–positive melanoma are important indicators to identify patients at high risk of recurrence who should be candidates for adjuvant therapy. We aimed to evaluate the presence of melanoma cells beyond the SN capsule—extranodal extension (ENE)—as a prognostic factor in patients with positive SNs. Methods: Data from 1,047 patients with melanoma and positive SNs treated from 2001 to 2020 at the Istituto Nazionale dei Tumori in Milano, Italy, were retrospectively investigated. Kaplan-Meier survival and crude cumulative incidence of recurrence curves were estimated. A multivariable logistic model was used to investigate the association between ENE and selected predictive factors. Cox models estimated the effect of the selected predictors on survival endpoints. Results: Median follow-up was 69 months. The 5-year overall survival rate was 62.5% and 71.7% for patients with positive SNs with and without ENE, respectively. The 5-year disease-free survival rate was 54.0% and 64.0% for patients with positive SNs with and without ENE, respectively. The multivariable logistic model showed that age, size of the main metastatic focus in the SN, and number
s of metastatic non-SNs were associated with ENE (all P<.0001). The multivariable Cox regression models showed the estimated prognostic effects of ENE associated with age, ulceration, size of the main metastatic focus in the SN, and number of metastatic non-SNs (all P<.0001) on disease-free survival and overall survival. Conclusions: ENE was a significant prognostic factor in patients with positive-SN melanoma. This parameter may be useful in clinical practice as a selection criterion for adjuvant treatment in patients with stage IIIA disease with a tumor burden <1 mm in the SN. We recommend its inclusion as an independent prognostic determinant in future updates of melanoma guidelines.
Héctor G. van den Boorn, Willemieke P.M. Dijksterhuis, Lydia G.M. van der Geest, Judith de Vos-Geelen, Marc G. Besselink, Johanna W. Wilmink, Martijn G.H. van Oijen, and Hanneke W.M. van Laarhoven
Background: A prediction model for overall survival (OS) in metastatic pancreatic ductal adenocarcinoma (PDAC) including patient and treatment characteristics is currently not available, but it could be valuable for supporting clinicians in patient communication about expectations and prognosis. We aimed to develop a prediction model for OS in metastatic PDAC, called SOURCE-PANC, based on nationwide population-based data. Materials and Methods: Data on patients diagnosed with synchronous metastatic PDAC in 2015 through 2018 were retrieved from the Netherlands Cancer Registry. A multivariate Cox regression model was created to predict OS for various treatment strategies. Available patient, tumor, and treatment characteristics were used to compose the model. Treatment strategies were categorized as systemic treatment (subdivided into FOLFIRINOX, gemcitabine/nab-paclitaxel, and gemcitabine monotherapy), biliary drainage, and best supportive care only. Validation was performed according to a temporal internal–external cross-validation scheme. The predictive quality was assessed with the C-index and calibration. Results: Data for 4,739 patients were included in the model. Sixteen predictors were included: age, sex, performance status, laboratory values (albumin, bilirubin, CA19-9, lactate dehydrogenase), clinical tumor and nodal stage, tumor sublocation, presence of distant lymph node metastases, liver or peritoneal metastases, number of metastatic sites, and treatment strategy. The model demonstrated a C-index of 0.72 in the internal–external cross-validation and showed good calibration, with the intercept and slope 95% confidence intervals including the ideal values of 0 and 1, respectively. Conclusions: A population-based prediction model for OS was developed for patients with metastatic PDAC and showed good performance. The predictors that were included in the model comprised both baseline patient and tumor characteristics and type of treatment. SOURCE-PANC will be incorporated in an electronic decision support tool to support shared decision-making in clinical practice.