Nomogram predicting overall survival after surgical resection for retroperitoneal leiomyosarcoma patients.

Background: Surgery is the best way to cure the retroperitoneal leiomyosarcoma (RLMS), and there is currently no prediction model on RLMS after surgical resection. The objective of this study was to develop a nomogram to predict the overall survival (OS) of patients with RLMS after surgical resection.

Methods: Patients who underwent surgical resection from September 2010 to December 2020 were included.

Proteomic characteristics reveal the signatures and the risks of T1 colorectal cancer metastasis to lymph nodes.

The presence of lymph node metastasis (LNM) affects treatment strategy decisions in T1NxM0 colorectal cancer (CRC), but the currently used clinicopathological-based risk stratification cannot predict LNM accurately. In this study, we detected proteins in formalin-fixed paraffin-embedded (FFPE) tumor samples from 143 LNM-negative and 78 LNM-positive patients with T1 CRC and revealed changes in molecular and biological pathways by label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) and established classifiers for predicting LNM in T1 CRC. An effective 55-proteins prediction model was built by machine learning and validated in a training cohort (N=132) and two validation cohorts (VC1, N=42; VC2, N=47), achieved an impressive AUC of 1.

Prognostic Factor Analysis and Nomogram Construction of Primary Retroperitoneal Liposarcoma: A Review of 10 Years of Treatment Experience in a Single Asian Cohort of 211 Cases.

Objective: This study intended to retrospectively analyze the data of patients with primary retroperitoneal liposarcoma in a single Asian large-volume sarcoma center and to establish nomograms focused on PRLPS for predicting progression-free survival (PFS) and overall survival (OS).

Methods: A total of 211 patients treated surgically for primary, non-metastatic retroperitoneal liposarcoma during 2009-2021 were identified, and clinicopathologic variables were analyzed. PFS and OS nomograms were built based on variables selected by multivariable analysis.