ArticlePloS one2019
Neoadjuvant therapy for locally advanced gastric cancer patients. A population pharmacodynamic modeling.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Neoadjuvant chemoradiotherapy plus postoperative adjuvant XELOX chemotherapy versus postoperative adjuvant chemotherapy with XELOX regimen for local advanced gastric cancer-A randomized, controlled study.The British journal of radiology · 2021Trial
- Neoadjuvant Bevacizumab Plus Docetaxel/Cisplatin/Capecitabine Chemotherapy in Locally Advanced Gastric Cancer Patients: A Pilot Study.Frontiers in surgery · 2022Article
- Interpretation of the development of neoadjuvant therapy for gastric cancer based on the vicissitudes of the NCCN guidelines.World journal of gastrointestinal oncology · 2020Review
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPerioperative chemotherapy (CT) or neoadjuvant chemoradiotherapy (CRT) in patients with locally advanced gastric (GC) or gastroesophageal junction cancer (GEJC) has been shown to improve survival compared to an exclusive surgical approach. However, most patients retain a poor prognosis due to important relapse rates. Population pharmacokinetic-pharmacodynamic (PK/PD) modeling may allow identifying at risk-patients. We aimed to develop a mechanistic PK/PD model to characterize the relationship between the type of neoadjuvant therapy, histopathologic response and survival times in locally advanced GC and GEJC patients.
methodsPatients with locally advanced GC and GEJC treated with neoadjuvant CT with or without preoperative CRT were analyzed. Clinical response was assessed by CT-scan and EUS. Pathologic response was defined as a reduction on pTNM stage compared to baseline cTNM. Metastasis development risk and overall survival (OS) were described using the population approach with NONMEM 7.3. Model evaluation was performed through predictive checks.
resultsA low correlation was observed between clinical and pathologic TNM stage for both T (R = 0.32) and N (R = 0.19) categories. A low correlation between clinical and pathologic response was noticed (R = -0.29). The OS model adequately described the observed survival rates. Disease recurrence, cTNM stage ≥3 and linitis plastica absence, were correlated to a higher risk of death.
conclusionOur model adequately described clinical response profiles, though pathologic response could not be predicted. Although the risk of disease recurrence and survival were linked, the identification of alternative approaches aimed to tailor therapeutic strategies to the individual patient risk warrants further research.
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