Minimally Invasive Model
Minimally Invasive Model - Related Videos
06:51
A Minimally Invasive Model of Aortic Stenosis in Swine
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Cited by 1 •
2023
This protocol describes a minimally invasive surgical procedure for ascending aortic banding in swine.
07:46
A Minimally Invasive Method for Generating a Syngeneic Orthotopic Mouse Model of Lung Cancer
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2025
A major challenge of developing new therapies for lung cancer includes modeling the tumor microenvironment as regional disease progresses to metastatic disease. Here, we describe a simple method of establishing an orthotopic syngeneic model of lung cancer that allows for in vivo tracking of disease over time.
07:34
Minimally-invasive Technique for Injection into Rat Optic Nerve
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Cited by 4 •
2015
Direct injection into the rat optic nerve is useful for regenerative research. We demonstrate a minimally-invasive technique for direct injection into a rat optic nerve that does not involve opening the skull. Using this method, surgical complications are minimized and recovery is more rapid.
15:04
An Anatomical Study of Nerves at Risk During Minimally Invasive Hallux Valgus Surgery
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Cited by 9 •
2018
Minimally invasive surgical (MIS) procedures rely on anatomical references to localize structures not directly visible to the surgeon. This manuscript describes a combined method of plane-by-plane dissection and sectional anatomy of fresh-frozen specimens to locate the structures at risk during MIS procedures.
08:15
Minimally Invasive Establishment of Murine Orthotopic Bladder Xenografts
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Cited by 16 •
2014
The established technique to inoculate primary invasive orthotopic bladder cancer xenografts requires laparotomy and mobilization of the bladder. This procedure inflicts significant morbidity on the mice, is technically challenging and time-consuming. We therefore developed a high-precision, percutaneous approach utilizing ultrasound guidance.
FAQs
Repeated measurements are the central mechanistic advantage of a Minimally Invasive Model. Sampling or imaging the same subject at multiple time points links changes in tumor status to an individual baseline, rather than relying only on comparisons between different subjects. This longitudinal design can reveal patterns in tumor growth, progression, or treatment response that single time-point assessments may miss.
Limiting tissue disruption helps preserve normal physiology during observation. Extensive procedures can alter the biological state being studied, whereas small-volume sampling, accessible tissue collection, imaging, or localized procedures reduce that disturbance. This matters because measurements are intended to reflect tumor biology and therapy response, not primarily the effects of repeated, burdensome intervention.
Compared with models requiring extensive intervention, the minimally invasive approach can reduce procedural burden and variability associated with major tissue disruption. Its value is therefore not simply convenience: repeated observations in the same subject can make changes easier to interpret while reducing differences introduced by separate interventions. The resulting data can strengthen longitudinal assessment of experimental outcomes.
The choice among small-volume sampling, accessible tissue collection, imaging, and localized procedures depends on what must be monitored. A design centered on biomarkers may prioritize collection, whereas assessment of tumor growth or progression may rely on imaging or another accessible measurement. Matching the measurement approach to the biological outcome helps the model answer a focused cancer research question.
A typical study workflow begins by selecting the cancer process and outcome to follow, then pairing it with an appropriate minimally invasive measurement. Investigators can collect small samples, obtain accessible tissue, perform imaging, or use a localized procedure, and repeat the selected assessment over time. The resulting series of observations supports evaluation of tumor status and response.
Minimally Invasive Model approaches can be applied to studies of tumor growth, metastasis, biomarkers, and therapeutic efficacy. Their repeated-measurement capability allows investigators to follow how a tumor or biomarker changes and whether treatment response evolves in the same subject. This supports more informative time-course analysis than an assessment limited to a single observation.
These models are relevant to cancer research because they connect experimental measurements with questions important for clinical translation. By reducing procedural burden and preserving normal physiology as observations accumulate, they can help researchers refine experimental designs and judge therapeutic effects over time. The goal is not merely to collect more measurements, but to improve how findings may inform clinical research.