Publications by authors named "I M Verpalen"
Objectives: Total tumor volume (TTV) is associated with overall and recurrence-free survival in patients with colorectal cancer liver metastases (CRLM). However, the labor-intensive nature of such manual assessments has hampered the clinical adoption of TTV as an imaging biomarker. This study aimed to develop and externally evaluate a CRLM auto-segmentation model on CT scans, to facilitate the clinical adoption of TTV.
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- The study evaluated the prognostic significance of total tumor volume (TTV) in predicting early recurrence and overall survival in patients with colorectal liver metastases (CRLM) who received systemic therapy followed by local treatment.
- Results showed that both baseline TTV and changes in TTV after treatment were significantly associated with early recurrence and overall survival, while conventional measures like RECIST1.1 did not show predictive value.
- Findings were validated in an external patient cohort, confirming that TTV provides important prognostic information beyond traditional clinical factors for patients with initially unresectable CRLM.
Background: Abdominal computed tomography (CT) is the standard imaging modality for detection and staging in patients with colorectal liver metastases (CRLM). Although liver magnetic resonance imaging (MRI) is superior to CT in detecting small lesions, guidelines are ambiguous regarding the added value of an additional liver MRI in the surgical workup of patients with CRLM. Therefore, this systematic review and meta-analysis aimed to evaluate the clinical added value of liver MRI in patients eligible for resection or ablation of CRLM based on CT.
View Article and Find Full Text PDFObjective: This study aimed to develop and evaluate an automatic model using artificial intelligence (AI) for quantifying vascular involvement and classifying tumor resectability stage in patients with pancreatic ductal adenocarcinoma (PDAC), primarily to support radiologists in referral centers. Resectability of PDAC is determined by the degree of vascular involvement on computed tomography scans (CTs), which is associated with considerable inter-observer variability.
Methods: We developed a semisupervised machine learning segmentation model to segment the PDAC and surrounding vasculature using 613 CTs of 467 patients with pancreatic tumors and 50 control patients.
Background: Accurately predicting the risk of clinically relevant postoperative pancreatic fistula after pancreatoduodenectomy before surgery may assist surgeons in making more informed treatment decisions and improved patient counselling. The aim was to evaluate the predictive accuracy of a radiomics-based preoperative-Fistula Risk Score (RAD-FRS) for clinically relevant postoperative pancreatic fistula.
Methods: Radiomic features were derived from preoperative CT scans from adult patients after pancreatoduodenectomy at a single centre in the Netherlands (Amsterdam, 2013-2018) to develop the radiomics-based preoperative-Fistula Risk Score.