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Preclinical In Vivo-Models to Investigate HIPEC; Current Methodologies and Challenges.

ABSTRACT: Hyperthermic intraperitoneal chemotherapy (HIPEC) is a treatment modality for patients with peritoneal metastasis (PM) of various origins which aims for cure in combination with cytoreductive surgery (CRS). Efficacy of CRS-HIPEC depends on patient selection, tumor type, delivery technique, and treatment parameters such as temperature, carrier solution, type of drug, dosage, volume, and treatment duration. Preclinical research offers a powerful tool to investigate the impact of these parameters and to assist in designing potentially more effective treatment protocols and clinical trials. The different methodologies for peritoneal disease and HIPEC are variable. This study aims to review the objectives, methods, and clinical relevance of in vivo preclinical HIPEC studies found in the literature. In this review, recommendations are provided and possible pitfalls are discussed on the choice of type of animal and tumor model per stratified parameters and study goal. The guidelines presented in this paper can improve the clinical relevance and impact of future in vivo HIPEC experiments.

SUBMITTER: Helderman RFCPA

PROVIDER: S-EPMC8303745 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Preclinical In Vivo-Models to Investigate HIPEC; Current Methodologies and Challenges.

Helderman Roxan F C P A RFCPA Löke Daan R DR Tanis Pieter J PJ Tuynman Jurriaan B JB Ceelen Wim W de Hingh Ignace H IH van der Speeten Kurt K Franken Nicolaas A P NAP Oei Arlene L AL Kok H Petra HP Crezee Johannes J

Cancers 20210708 14

Hyperthermic intraperitoneal chemotherapy (HIPEC) is a treatment modality for patients with peritoneal metastasis (PM) of various origins which aims for cure in combination with cytoreductive surgery (CRS). Efficacy of CRS-HIPEC depends on patient selection, tumor type, delivery technique, and treatment parameters such as temperature, carrier solution, type of drug, dosage, volume, and treatment duration. Preclinical research offers a powerful tool to investigate the impact of these parameters a ...[more]

PMID: 34298644

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Project description:ObjectiveUp to a third of patients with epilepsy fail to achieve satisfactory seizure control. A reliable method of predicting seizures would alleviate psychological and physical impact. Dysregulation in heart rate variability (HRV) has been found to precede epileptic seizures and may serve as an extracerebral predictive biomarker. This study aims to identify the preictal HRV dynamics and unveil the factors impeding the clinical application of ECG-based seizure prediction.MethodsThirty-nine adult patients (eight women; median age: 38, [IQR = 31, 56.5]) with 252 seizures were included. Each patient had more than three recorded epileptic seizures, each at least 2 hours apart. For each seizure, one hour of ECG prior to seizure onset was analyzed and 97 HRV features were extracted from overlapping three-minute windows with 10s stride. Two separate patient-specific experiments were performed using a support vector machine (SVM). Firstly, the separability of training data was examined in a non-causal trial. Secondly, the prediction was attempted in pseudo-prospective conditions. Finally, visualized HRV data, clinical metadata, and results were correlated.ResultsThe mean receiver operating characteristic (ROC) area under the curve (AUC) for the non-causal experiment was 0.823 (±0.12), with 208 (82.5%) seizures achieving an improvement over chance (IoC) classification score (p < 0.05, Hanley & McNeil test). In pseudo-prospective classification, the ROC-AUC was 0.569 (±0.17), and 86 (49.4%) seizures were classified with IoC. Off-sample optimized SVMs failed to improve performance. Major limiting factors identified include non-stationarity, variable preictal duration and dynamics. The latter is expressed as both inter-seizure onset zone (SOZ) and intra-SOZ variability.SignificanceThe pseudo-prospective preictal classification achieving IoC in approximately half of tested seizures suggests the presence of genuine preictal HRV dynamics, but the overall performance does not warrant clinical application at present. The limiting factors identified are often overlooked in non-causal study designs. While current deterministic prediction methods prove inadequate, probabilistic approaches may offer a promising alternative.Plain language summaryMany patients with epilepsy suffer from uncontrollable seizures and would greatly benefit from a reliable seizure prediction method. Currently, no such system is available to meet this need. Previous studies suggest that changes in the electrocardiogram (ECG) precede seizures by several minutes. In our work, we evaluated whether variations in heart rate could be used to predict epileptic seizures. Our findings indicate that we are still far from achieving results suitable for clinical application and highlight several limiting factors of present seizure prediction approaches.

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Preclinical In Vivo-Models to Investigate HIPEC; Current Methodologies and Challenges.

Publications

Preclinical In Vivo-Models to Investigate HIPEC; Current Methodologies and Challenges.

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