## 1 Introduction  
Liver cancer is among the most commonly diagnosed malignancies globally and is the third leading cause of cancer deaths worldwide [1; 2; 3; 4]. Its global incidence is predicted to accelerate, implicating significant mortality and burden of disease [1; 2]. The most common radical treatment for liver cancer in patients with adequate healthy liver reserves is surgical resection [5; 6; 7; 8; 9; 10]. Successful resection relies on highly specific preoperative planning due to the liver's complex anatomy and significant anatomical variability between patients [11; 12; 13]. Hepatic vasculature, for example, may present multiple common variants. The right, middle and left hepatic veins each have three to four independent variants, while the portal veins, hepatic arteries and the biliary tree may also have structural variance [10]. This increases the complexity of the spatial relationships between intrahepatic structures and, therefore, requires adaptive and personalised surgical techniques [10]. During resection, a sufficient future liver remnant must be preserved, while tumours must be resected with an adequate margin to prevent reoccurrence [10; 13; 14]. This requires a comprehensive pre- and intraoperative understanding of the spatial relationships between a patient's cancer and hepatic anatomy [10; 15; 16]. The increasing preference for laparoscopic resection procedures that restrict surgical field overview further emphasises the importance of this understanding [12; 17]. Computed tomography (CT) and magnetic resonance imaging (MRI) are fundamental in preoperative planning of liver cancer resection (LCR) [10; 11; 12; 13; 18]. CT provides three-dimensional (3D) datasets with high spatial resolution; hence, it is traditionally most commonly used in studies for image processing and segmentation of hepatic structures. In recent years, MRI has also increasingly been performed in liver imaging due to its high contrast-to-noise ratio, and the opportunity of making a clinical diagnosis without using contrast medium. These modalities require the surgeon to mentally reconstruct a 3D visualisation of the liver and its structures from a series of two-dimensional (2D) images [13; 15; 19]. This can be particularly challenging due to liver's complex anatomy and the sophisticated spatial relationships between its intrahepatic structures [10; 13]. Three-dimensional volume renderings (3 DVRs) assist in comprehending these relationships; however, they still require the surgeon to mentally reconstruct depth and spatial location information from a 2D image [16; 18; 19]. Advances in 3D printing (3 DP) may alleviate these difficulties through the introduction of patient-specific 3D-printed liver models (3 DPLMs) [20]. 3 DP is a form of additive manufacturing where 3D objects are fabricated layer by layer from a digital file [21]. There are several 3 DP technologies currently utilised in medicine, and at baseline, these involve a liquid, powder, or filament material being selectively deposited and then fused layer by layer via light, thermal energy, or a bonding agent to produce a solid material [21; 22]. Segmentations of patients' individual liver anatomy and pathology from their medical imaging data can be reconstructed in 3D file formats suitable for 3 DP and then used to print representative 3 DPLMs [12; 17]. Previous literature reviews by [PERSON] and [PERSON] [20], [PERSON] et al. [23] and [PERSON] et al. [24] highlight 3 DPLMs' surgical planning value; however, there is limited systematic research describing whether 3 DPLMs enhance LCR surgical planning, or examining whether 3 DPLMs improve LCR patients' intra- and postoperative outcomes. This review, therefore, seeks to build on previous 3 DPLM literature by providing an up-to-date critical examination of the utility of 3 DPLMs for surgical planning and intraoperative guidance of LCR, exploring whether these applications improve patient outcomes.