Augmenting Surgery via Multi-scale Modeling and Translational Systems Biology in the Era of Precision Medicine: A Multidisciplinary Perspective

Abstract

In this era of tremendous technological capabilities and increased focus on improving clinical outcomes, decreasing costs, and increasing precision, there is a need for a more quantitative approach to the field of surgery. Multiscale computational modeling has the potential to bridge the gap to the emerging paradigms of Precision Medicine and Translational Systems Biology, in which quantitative metrics and data guide patient care through improved stratification, diagnosis, and therapy. Achievements by multiple groups have demonstrated the potential for (1) multiscale computational modeling, at a biological level, of diseases treated with surgery and the surgical procedure process at the level of the individual and the population; along with (2) patient-specific, computationally-enabled surgical planning, delivery, and guidance and robotically-augmented manipulation. In this perspective article, we discuss these concepts, and cite emerging examples from the fields of trauma, wound healing, and cardiac surgery.

Keywords: Computer aided surgery; Computer-aided interventions; Heart; Inflammation; Mathematical model; Model-guided surgery; Multiscale model; Wound healing.

Figure 1. A synthetic view of Computational Surgery

Multiscale modeling is envisioned as a binding framework by which to synthesize the currently disparate fields associated with Computational Surgery, leading ultimately to a Precision Medicine framework based on multiscale computational modeling.

Figure 2

Computational modeling of (a): Surgical Ventricular Restoration, (b): Algisyl-LVR injection therapy (c): Cardiokinetix Parachute Device. EDP, P1 and OP represent end diastolic pressure, patient 1 and operation, respectively.

Figure 3

Using methods from Rucker et al., (a) preoperative liver model and registered swabbed point cloud acquired from intraoperative liver surface are shown with ultrasound image of segment III portal pedicle in (b), and (c) showing manual segmentation of structure. In (d) we see the planned model cloud with ultrasound slice (white arrow) showing alignment of pedicle based on rigid registration, and (e) is the close-up. Notice how inferior vessel region does not align with corresponding vascular structure. In (f), a deformation correction driven by the closest point mismatch in data shown in (a) has been performed, and (g) shows the new location of the structure as well as modified shapes to vasculature. It should be noted that in this example ultrasound data was used for validation only, i.e. no localized ultrasound structures were used within the alignment algorithm, only data shown in (a) was used. We should further note that the alignment error of this feature after rigid registration was estimated at 5.5 +/− 2.6 mm (10.9 mm maximum error). The error was reduced to 2.4 +/− 1.5 mm (5.4 mm maximum error) after model-based deformation correction.

Figure 4

Time Scales Involved in the Wound Healing Process. (A) Relative number of primary cell types involved in the three passes of wound healing. The inflammatory phase initiates within minutes of wounding and continues for days as first neutrophils and then macrophages move into the provisional fibrin matrix established during hemostasis. Over the next several days, fibroblasts and vascular endothelial cells (not shown) rapidly expand in number and then begin to remodel the wound site with newly synthesized ECM that continues for many months. (B) The corresponding temporal pattern of ECM production in the wound. Fibronectin and type III collagen is synthesized initially. Later, ECM production by fibroblasts transitions predominantly to type I collagen and results in an increase in the wound breaking strength. Adapted from Witte et al.

Figure 5. Simulations of the PUABM Match Key Features of Clinical Images

(A) Simulations achieved visual appearances with characteristics similar to each stage of PU development. The first row of clinical images come from the National Pressure Ulcer Advisory Panel (copyright npuap.org, used with permission) and are of different subjects. Images in the second row are from people with SCI enrolled in a prospective study of PU at each stage. Irregular shapes and increasing nearby damage are observed in both sets of clinical data. (B) Numbers indicate days post-injury. Simulated ulcers evolve with visual characteristics that match PU progression observed in people with SCI. Two simulation time courses are matched against one patient from our study. We match key features: irregular shapes, nearby satellite ulcers (open arrows), jagged edges (solid arrows), and decreasing tissue health across the field. Reprinted from.