Radiol Oncol 2022; 56(4): 420-429. doi: 10.2478/raon-2022-0051 420 review Imaging perfusion changes in oncological clinical applications by hyperspectral imaging: a literature review Rok Hren1,2, Gregor Sersa3, Urban Simoncic1,4, Matija Milanic1,4 1 Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia 2 Institute of Mathematics, Physics, and Mechanics, Ljubljana, Slovenia 3 Institute of Oncology Ljubljana, Ljubljana, Slovenia 4 Jozef Stefan Institute, Ljubljana, Slovenia Radiol Oncol 2022; 56(4): 420-429. Received 27 October 2022 Accepted 2 November 2022 Correspondence to: Matija Milanic, Ph.D., Faculty of Mathematics and Physics, University of Ljubljana, Jadranska ulica 19, SI-1000 Ljubljana, Slovenia. E-mail matija.milanic@fmf.uni-lj.si Disclosure: No potential conflicts of interest were disclosed. This is an open access article distributed under the terms of the CC-BY license (https://creativecommons.org/licenses/by/4.0/). Background. Hyperspectral imaging (HSI) is a promising imaging modality that uses visible light to obtain information about blood flow. It has the distinct advantage of being noncontact, nonionizing, and noninvasive without the need for a contrast agent. Among the many applications of HSI in the medical field are the detection of various types of tumors and the evaluation of their blood flow, as well as the healing processes of grafts and wounds. Since tumor perfusion is one of the critical factors in oncology, we assessed the value of HSI in quantifying perfusion changes during interventions in clinical oncology through a systematic review of the literature. Materials and methods. The PubMed and Web of Science electronic databases were searched using the terms “hyperspectral imaging perfusion cancer” and “hyperspectral imaging resection cancer”. The inclusion criterion was the use of HSI in clinical oncology, meaning that all animal, phantom, ex vivo, experimental, research and develop- ment, and purely methodological studies were excluded. Results. Twenty articles met the inclusion criteria. The anatomic locations of the neoplasms in the selected articles were as follows: kidneys (1 article), breasts (2 articles), eye (1 article), brain (4 articles), entire gastrointestinal (GI) tract (1 article), upper GI tract (5 articles), and lower GI tract (6 articles). Conclusions. HSI is a potentially attractive imaging modality for clinical application in oncology, with assessment of mastectomy skin flap perfusion after reconstructive breast surgery and anastomotic perfusion during reconstruction of gastrointenstinal conduit as the most promising at present. Key words: hyperspectral imaging; oncology; resection; perfusion; cancer Introduction Cancer is the leading health problem in the world. Only in the EU-27 each year are 2.7 million people diagnosed with cancer, while 1.3 million die from the disease.1 To deal with cancer, knowledge of cancer physiology is essential, where tissue perfu- sion is one of the most important physiological pa- rameters. Perfusion of tumors is critical in their de- velopment and growth. Early studies have shown that tumor growth is dependent on the develop- ment of vasculature that has the capacity to sup- ply oxygen and nutrients to dividing tumor cells.2 However, the vasculature is important not only for the supply of oxygen to tumors but also for the de- livery of drugs into tumors.3 Finally, vasculature is also important for the response of tumors to surgery and other ablative techniques, such as ra- Radiol Oncol 2022; 56(4): 420-429. Hren R et al./Hyperspectral imaging in oncology 421 diotherapy and thermal and nonthermal ablative techniques.4,5 It was demonstrated that information about the tumor and healthy tissue perfusion can im- prove therapy outcome either by guiding tumor resection6,7 or monitoring the reperfusion of the re- sected tissues (e.g., anastomosis or tissue flaps).4,5 Conventional techniques for perfusion imaging in oncology are CT and MR imaging.10 CT per- fusion imaging provides information on tissue hemodynamics by analyzing the first passage of an intravenous contrast bolus through the ves- sels. On the other hand, MR perfusion imaging utilizes either endogenous or exogenous tracers. In the latter case, it is based on following an in- jected bolus of contrast agent over time, which is then used to determine the perfusion character- istics of tissues. While both imaging techniques are promising, radiation exposure (CT), potential adverse events due to contrast (CT/MRI), limited access (MRI), high cost (MRI), and inability to scan at the bedside or in operating theater are disadvan- tages of the conventional techniques.10 To address these shortcomings, various imaging techniques, including optical imaging, have been explored for tissue perfusion imaging.11,12 In optical imaging, the optical contrast of tissues is intrinsically sen- sitive to tissue abnormalities, such as changes in oxygenation, blood concentration or scattering.13,14 These changes are characteristic of many tumors, since they include angiogenesis, hypervasculari- zation, hypermetabolism, and hypoxia, making optical imaging techniques promising candidates for perfusion imaging in oncology. Hyperspectral imaging (HSI) is an emerging optical imaging technique that uses light to obtain information about perfusion, or more specifically about oxygenation, water content or hemoglobin content of the tissue. The distinct advantage of HSI is that it is a noncontact, nonionizing, and nonin- vasive modality and does not require a contrast agent. HSI integrates conventional imaging and spectroscopy techniques by creating a set of im- ages called a hypercube, which contains the spec- tral signature of the underlying tissue and in turn points to clinically relevant changes, such as angi- ogenesis or hypermetabolism. Figure 1 illustrates the structure and composition of hyperspectral images and physiological parameters derived from these images. HSI was originally employed in remote sens- ing applications16,17 and then expanded into other fields, such as vegetation type and water source detection18,19, wood product control20, drug analy- sis21, food quality control22-25, artwork authenticity and restoration26,27, and security28. HSI is also an attractive modality in the medical field and has been successfully applied for the detection of vari- ous types of tumors, particularly in conjunction with histopathologic diagnosis.29-31 HSI has, inter alia, already proven value in plastic and vascular surgery, where assessing perfusion predicted the outcome of healing processes in transplants and wounds.32,33 How valuable HSI could be in quantifying per- fusion changes during interventions in clinical oncology remains unclear, and to that end, we de- cided to systematically review the literature with FIGURE 1. Structure and composition of hyperspectral images and physiological parameters derived from the images, which are typically displayed in false color. NIR PI = near-infrared perfusion index; OHI = organ hemoglobin index; StO2 = oxygen saturation of tissue; TWI = tissue water index Taken from Pfahl et al.15 and reprinted with permission from the publisher. Radiol Oncol 2022; 56(4): 420-429. Hren R et al./Hyperspectral imaging in oncology422 the intention of exclusively focusing only on stud- ies in which HSI was performed on patients in the clinical oncology setting. Materials and methods Two authors (R.H. and M.M.) conducted jointly – to preclude potential bias – a comprehensive litera- ture search on October 3, 2022 through PubMed and Web of Science electronic databases using the following search terms: »hyperspectral imaging perfusion cancer« and »hyperspectral imaging re- section cancer«. No restrictions in publication date or language were imposed. The inclusion criterion was the application of the hyperspectral imag- ing modality in the oncological clinical setting, meaning that all animal and phantom, ex vivo, ex- perimental, research and development, and purely methodological studies were excluded. Special care was taken that duplications were removed, both across databases and across studies; for ex- ample, if the study was first published in proceed- ings and later in the journal, then proceedings ar- ticle was considered a nonprimary publication and therefore excluded. Studies were categorized with respect to the anatomical location of the tumors. Results A flow diagram of the selection strategy is shown in Figure 2; in total, 101 and 84 articles were found to be of interest in the PubMed and Web of Science databases, respectively. After excluding duplicates and applying the exclusion criteria, first consider- ing the title and abstract, and next, if necessary, reading the entire article, 20 articles were identi- fied for further analysis. The anatomical locations of tumors in the selected articles were as follows: kidneys (1 article), breasts (2 articles), eye (1 arti- cle), brain (4 articles), entire gastrointestinal (GI) tract (1 article), upper GI tract (5 articles) and lower GI tract (6 articles). Kidneys Pioneering effort in assessing perfusion by means of HSI in clinical oncology was the work of Best et al.34 They applied modality to monitor renal oxy- genation during partial nephrectomy using the parameter called the percentage of oxyhemoglobin (HbO2) and categorized 26 patients into the preop- erative groups of high (>75% HbO2) and low (<75% HbO2) oxygenation. Parameter HbO2 has proven useful before, during and after the application of a clamp, with an example of the image presented in Figure 3. The study demonstrated that patients with low oxygenation had a statistically signifi- cant postoperative decline in estimated glomeru- lar filtration rate. While further research is needed, HSI indicates potential for assessing susceptibility to renal ischemic injury in patients undergoing partial nephrectomy. FIGURE 2. Flow diagram of the selection strategy. FIGURE 3. Images of the kidney depicting the percentage of HbO2 as a function of color. A dark red represents high values while the yellows and greens indicate lower values. Taken from Best et al.34 and reprinted with permission from the publisher. Radiol Oncol 2022; 56(4): 420-429. Hren R et al./Hyperspectral imaging in oncology 423 TABLE 1. Included articles reporting the use of hyperspectral imaging (HSI) to quantify perfusion changes in clinical applications in oncology Reference Year of publication Number of patients Oncologic intervention System Algorithm Kidneys Best34 2013 26 Partial nephrectomy DLP HSI, 520–645 nm Supervised multivariate least squares regression Eye Rose35 2018 8 Radiation retinopathy Tunable laser, 520–620 nm with 5 nm steps PHYSPEC software (Photon etc., Montreal, QC, Canada) Breasts Chin36 2017 43 Skin response to radiation OxyVu-2TM (Hypermed, Inc., Waltham, MA), 500–600 nm The OxyVu-2TM software (Hypermed, Inc., Waltham, MA) Pruimboom8 2022 10 Mastectomy skin flap necrosis TIVITA™ (Diaspective Vision GmbH, Am Salzhaff, Germany), 500– 1000 nm with 5 nm step TIVITA™ (Diaspective Vision GmbH, Am Salzhaff, Germany) Brain Fabelo37 2018 22 Craniotomy for resection of intraaxial brain tumor Hyperspec VNIR A-Series (HeadWall Photonics, Massachusetts, USA), 400–1000 nm Spectral angle mapper Fabelo38 2018 5 Craniotomy for resection of intraaxial brain tumor; all 5 patients with grade IV glioblastoma As in Fabelo37 As in Fabelo37 Fabelo39 2019 6 Craniotomy for resection of intra-axial brain tumor; all 6 patients with grade IV glioblastoma As in Fabelo37 As in Fabelo37 Fabelo40 2019 22 Craniotomy for resection of intraaxial brain tumor As in Fabelo37 As in Fabelo37 Entire GI tract Jansen-Winkeln41 [Article in German] 2018 47 Gastrointestinal surgery with esophageal, gastric, pancreatic, small bowel or colorectal anastomoses As in Pruimboom8 As in Pruimboom8 Upper GI tract Kohler9 2019 22 Hybrid or open esophagectomy followed by reconstruction of gastric conduit As in Pruimboom8 As in Pruimboom8 Moulla42 [Article in German] 2020 Video presentation of hybrid esophagectomy As in Pruimboom8 As in Pruimboom8 Schwandner43 2020 4 Hybrid esophagectomy followed by reconstructing gastric conduit As in Pruimboom8 As in Pruimboom8 Hennig44 2021 13 Hybrid esophagectomy followed by reconstructing gastric conduit As in Pruimboom8 As in Pruimboom8 Moulla45 2021 20 Pancreatoduodenectomy As in Pruimboom8 As in Pruimboom8 Lower GI tract Jansen-Winkeln46 2019 24 Colorectal resection As in Pruimboom8 As in Pruimboom8 Jansen-Winkeln47 2020 32 Colorectal resection As in Pruimboom8 As in Pruimboom8 Pfahl48 2022 128 Colorectal resection As in Pruimboom8 As in Pruimboom8 Jansen-Winkeln49 2021 54 Colorectal resection As in Pruimboom8 As in Pruimboom8 Jansen-Winkeln50 2022 115 Colorectal resection As in Pruimboom8 As in Pruimboom8 Barberio51 2022 52 Colorectal resection As in Pruimboom8 As in Pruimboom8 GI = gastrointestinal Radiol Oncol 2022; 56(4): 420-429. Hren R et al./Hyperspectral imaging in oncology424 Eye In the study of Rose et al.35, clinicians used Doppler spectral domain optical coherence tomography (SD-OCT) in 8 patients diagnosed with radiation retinopathy to measure total retinal blood flow, while retinal blood oxygen saturation was quan- tified by a specially designed HSI retinal camera. They found that blood flow in the retinopathy eye was significantly lower than that in the fellow eye, while arteriolar oxygen saturation and venular ox- ygen saturation were higher in the retinopathy eye than in the fellow eye. Unfortunately, researchers conducted no follow-up studies, in which they would further evaluate microvascular changes due to radiation-induced retinopathy. Breasts Chin et al.36 studied a dose‒response relationship between radiation exposure and oxygenated hemo- globin in 43 women undergoing breast-conserving therapy radiation. The authors concluded that HSI may prove useful as an objective measure of pa- tients’ skin response to radiation dose. However, they also noted that interpatient variability remains a challenge, as approximately 40% of the variability in change in oxygenated hemoglobin is accounted for by dose, 25% by individual woman, and 35% by causes that they could not identify. Pruimboom et al.8 used HSI in a prospective clinical pilot study enrolling women with breast reconstruction and detected mastectomy skin flap necrosis in 3 out of 10 patients. Somewhat analo- gously to the study of Best et al.34, they found that tissue oxygenation was statistically significantly lower in the group of patients who developed flap necrosis than in the group of patients who did not. It appears that HSI is specifically suited for the early detection of flap necrosis, which could in turn aid in the timely and accurate debridement of necrotic tissue. Future work should confirm the modality’s potential also in identifying partial deep inferior epigastric artery perforator (DIEP) flap necrosis. Brain Fabelo et al.37-40 developed an intraoperative HSI acquisition system and were able to assemble an in vivo hyperspectral human brain image database with the overall goal of accurately delineating tu- mor tissue from normal brain tissue. As the brain tumor typically infiltrates the surrounding tissue, it is extremely difficult to identify the border; in ad- dition, both overresection of adjacent normal brain tissue and leaving tumor tissue behind have det- rimental impacts on the results of the surgery and patient outcomes, either adversely affecting the pa- tient’s quality of life or causing tumor progression. A B C FIGURE 4. (A) Red-Green-Blue (RGB) representation of the imaged brain, including normal and tumor tissue. (B) Extraction of blood vessels from hyperspectral images using the spectral angle mapper algorithm (SAM). (C) Tissue classification map generated from hyperspectral images: tumor tissue is red, normal tissue is green, blood vessels are blue, and background is black. Taken from Fabelo et al.38 and reprinted with permission from the publisher. Radiol Oncol 2022; 56(4): 420-429. Hren R et al./Hyperspectral imaging in oncology 425 The work of Fabelo et al. was performed as a part of the European Future and Emerging Technologies (FET) project HELICoiD (HypErspectraL Imaging Cancer Detection). In their first methodological paper, they de- signed a special cancer detection algorithm utiliz- ing spatial and spectral features of hyperspectral images from 5 patients with grade IV glioblas- toma.38 They demonstrated that it was possible to accurately discriminate between normal tissue, tumor tissue, blood vessels and background by generating classification and segmentation maps in surgical time during neurosurgical operations, as shown in Figure 4. In their second methodological paper39, they used data from 6 patients with grade IV glioblas- toma and applied improved algorithms to create maps, in which the parenchymal area of the brain could be delineated; an overall average accuracy of 80% was achieved. Their HSI system was systematically assessed at two clinical institutions enrolling 22 patients, and researchers found that results relevant for surgeons were obtained within 15 to 70 seconds.40 They also made available to the public this first in vivo hyperspectral human brain image database specifically designed for cancer detection. While authors were hopeful in their conclusion that HSI could facilitate brain tumor surgeries, no further studies beyond 2019 were published. HSI files from the studies by Fabelo and co- workers are available from http://hsibraindata- base.iuma.ulpgc.es database. Entire gastrointestinal tract During the past 3 years, the main focus of applying HSI in clinical oncology has been in the domain of the gastrointestinal tract, or more specifically, ad- dressing anastomotic insufficiency, which is one of the most serious postsurgery complications of reconstructing the gastrointestinal conduit. As anastomotic healing fundamentally depends on adequate perfusion, HSI could be a suitable mo- dality in assessing anastomotic perfusion in clini- cal practice. In a pilot study, Jansen-Winkeln et al.41 collected hyperspectral images in 47 patients who underwent gastrointestinal oncologic resection followed by esophageal, gastric, pancreatic, small bowel or colorectal anastomoses. The recorded hyperspectral images were analyzed to extract the following specific physiological tissue parameters, which were deemed characteristic for perfusion changes at the sites of anastomoses: oxygen satu- ration of the tissue (StO2), organ hemoglobin index (OHI), near-infrared perfusion index (NIR-PI), and tissue water index (TWI); the most clinically rel- evant appeared to be StO2. They concluded that intraoperative HSI provided a noncontact, nonin- vasive modality, which enabled real-time analysis of potential anastomotic leakage without the use of a contrast medium. Their group followed their initial work with several studies focusing on the upper and lower gastrointestinal tract, respective- ly, described in more detail below. Upper gastrointestinal tract Köhler et al.9 applied intraoperative HSI in 22 pa- tients during esophagectomy to the tip of the gastric tube, which later became esophagogastric anastomosis; they compared physiological HSI parameters (StO2, OHI, NIR PI and TWI) in 14 pa- tients who underwent laparoscopic gastrolysis and ischemic conditioning of the stomach with those in 8 patients without pretreatment. They noted that the values of physiological HSI parameters were higher in patients with ischemic preconditioning than in patients without ischemic precondition- ing; however, only StO2 exhibited weak statistical significance. In a single patient who developed anastomotic insufficiency of the intrathoracic es- ophagogastric anastomosis, all physiological HSI parameters were substantially lower than those in A B C D FIGURE 5. Comparison of Red-Green-Blue (RGB) images and near-infrared perfusion index (NIR PI) images recorded in a patient with (A, B) and without postoperative anastomotic insufficiency (C, D). Taken from Köhler et al.9 and reprinted with permission from the publisher. Radiol Oncol 2022; 56(4): 420-429. Hren R et al./Hyperspectral imaging in oncology426 other patients. Figure 5 compares the NIR PI im- age recorded in this patient with the correspond- ing image taken in the patient without postopera- tive anastomotic leakage. Hybrid esophagectomy along with intraoperative HSI used in the paper of Köhler et al.9 was presented as a video article by Moulla et al.42, while another clinical group43 cor- roborated the findings of Köhler et al.9 by reporting a case study including four patients. Hennig et al.44 continued the systematic evalu- ation of the capabilities of intraoperative HSI in 13 consecutive patients who underwent hybrid esophagectomy and reconstruction of the gastric conduit. Researchers also decided to use both in- traoperative HSI and fluorescence imaging with indocyanine green (FI-ICG) to define the optimal position of anastomosis. While there are no thresh- old values yet established to define adequately and insufficiently perfused tissues, they decided that HSI physiological parameter StO2 at >75% deter- mined the well-perfused area. It was noteworthy that imaging modalities recorded simultaneously in 10 out of 13 patients identified the perfusion border zone more peripherally than the one desig- nated subjectively by the surgeon. While HSI and FI-ICG may complement each other as intraopera- tive modalities, Hennig et al.44 were of the opinion that HSI may be advantageous due to “the lower costs, noninvasiveness, and lack of contraindica- tions”. Moulla et al.45 expanded oncological clinical applications in the domain of pancreatic surgery. Hyperspectral images were recorded during pan- creatoduodenectomy in 20 consecutive patients before and after gastroduodenal artery clamping. In this pilot study, they were able to detect by the means of physiologic HSI parameter StO2 improve- ment in liver perfusion after median acute liga- ment division in one patient with celiac artery ste- nosis. The HSI acquisition system in the operating room is shown in Figure 6. Lower gastrointestinal tract Jansen-Winkeln et al.9 applied intraoperative HSI in 24 patients to define the transection line during colorectal surgery. They found that the transec- tion line subjectively delineated by the surgeon FIGURE 6. Hyperspectral imaging (HSI) acquisition system in the operating room. Hyperspectral images were acquired within a few seconds with physiologic HSI parameters displayed in false colors. Taken from Moulla et al.45 and reprinted with permission from the publisher. Radiol Oncol 2022; 56(4): 420-429. Hren R et al./Hyperspectral imaging in oncology 427 deviated from the border line determined by HSI; in 13 patients subjectively, planned resection was up to 13 mm too distal in the poorly perfused area, while in 11 patients, it was too far in the well-per- fused area. Similar to esophagectomy44, intraop- erative HSI has shown potential in determining the optimal anastomotic area during colorectal surgery. Jansen-Winkeln et al.47 applied further intraop- erative HSI along with FI-ICG in 32 consecutive patients undergoing colorectal resection and con- cluded that both modalities provided similar in- formation in specifying the perfusion border zone and could complement each other. To optimize the performance of both modalities, Pfahl et al.48 constructed the combined FI-ICG and HSI system, which was tested in 128 patients. In another study49, Jansen-Winkeln et al. imaged colorectal tumors in 54 consecutive patients dur- ing colorectal resections and found that HSI used in combination with a neural-network algorithm was able to classify cancer or adenomatous mar- gins around the central tumor with a sensitivity of 86% and a specificity of 95%. Recently, they pub- lished a large study50 enrolling 115 patients who underwent colorectal resection to systematically assess the feasibility of HSI in quantifying tis- sue perfusion, and in accordance with a smaller patient series, they found that “well-perfused areas were clearly distinguishable from the less perfused ones only after one minute”.46,47 Similar conclusions were reached in a group of 52 patients undergoing colorectal surgery by Barberio et al.51, who also found that the physiological HSI param- eter StO2 was significantly lower in patients receiv- ing neoadjuvant radio/chemotherapy than in other oncological patients. Figure 7 illustrates the use- fulness of HSI in establishing the transection line during colorectal surgery. Discussion Based on this literature review, the following in- ferences could be made: HSI is still finding its place in oncological clinical applications with the assessment of (i) mastectomy skin flap perfusion after breast reconstructive surgery8 and (ii) anas- tomotic perfusion during reconstruction of gastro- intenstinal conduit9,44,45,48-50 as the most promising. However, caution needs to be advised because re- cently much research has been done in the arena of using HSI during brain surgery for glioblastoma, yet this clinical effort has not been sustained. In addition, the need for an obvious expansion of the study of Pruimboom et al.8 to a larger patient group, which would also include cases of DIEP flap necrosis, a meaningful and robust establishment of cutoff values for physiological HSI parameters is mandatory if HSI is to retain its clinical appeal. In their study, oxygen saturation of tissue StO2 ap- peared to be the most useful HSI index, and the cut-off value of 36.3% predicting tissue necrosis was found; this value was close to that defined by a pilot study52 enrolling mostly nononcological pa- tients (19 out of 22), in which the values of both StO2 and NIR PI above 40% indicated regular heal- ing without any revision surgery; furthermore, op- erators in that study noted that HSI was superior to assessments based on clinical and Doppler ultra- sound monitoring both in accuracy and speed. It is worthwhile to emphasize that HSI parameters are in general easy to follow by the operator as they are visualized as false-colour images (Figure 1). When evaluating applications of HSI in assess- ing anastomotic perfusion during reconstructing gastrointestinal conduits, two main challenges be- come apparent: (i) the first challenge is, as in the case of breast reconstructive surgery, related to the establishment of a clear cutoff value indicating FIGURE 7. Usefulness of hyperspectral imaging (HSI) in establishing transection line during colorectal surgery. The Red-Green-Blue (RGB) image (A) and StO2 map (B) show a patient in whom the clinical transection line (continuous line in black) and HSI transection line (dotted line in blue) were aligned; (C) and (D) show the RGB image and StO2 map, respectively, of a patient in whom the clinical transection line deviated from the HSI transection line. Taken from Barberio et al.51 and reprinted with permission from the publisher. A B C D Radiol Oncol 2022; 56(4): 420-429. Hren R et al./Hyperspectral imaging in oncology428 adequate tissue perfusion so that the operator can convincingly identify the optimal anastomosis ar- ea; (ii) the second challenge is related to HSI being limited to open surgery due to the large size of the HSI camera. The first challenge will need to be ap- proached by enrolling progressively larger patient groups undergoing various oncological surgical interventions. It appears that the group of Jansen- Winkeln et al.48,50 is already moving in this direc- tion by conducting progressively larger clinical studies. However, with the application of neural networks, requirements for cohort sizes become far higher but could also be partially satisfied with the data augmentation. The second challenge has been recently addressed by the same group15, with ex vivo testing of laparoscopic HSI camera and a highlight that the clinical trial with minimally in- vasive HSI has commenced already. Comparison of HSI and FI-ICG44,47,48 revealed similar results in defining the perfusion border of anastomosis, while both modalities were docu- mented to be reliable, fast, and intuitive. Even if HSI is completely noninvasive, injection of ICG rarely provokes allergic reactions. Since there is a potential for each of the two modalities to contrib- ute complementary information, it is not surpris- ing that Pfahl et al.48 constructed a combined HSI and FI-ICG recording system. In conclusion, HSI is at this stage emerging as an attractive imaging modality to quantify perfu- sion in oncological patients. Hopefully, a larger number of clinical sites will initiate clinical trials to address the challenges, which still preclude the final acceptance of this promising imaging tech- nique in the oncological clinical setting. Acknowledgment This work was financially supported by the state budget by the Slovenian Research Agency, re- search grant no. J3-3083 and research program no. P3-0003, P3-0307, and P1-0389. We would like to thank Dr. Ivan Stajduhar from University of Rijeka, Faculty of Engineering for his technical support in preparing Figures for pub- lishing. References 1. European Commission. ECIS - European cancer information system [Internet]. 2022. [cited 2022 Oct 15]. Available from: https://ecis.jrc. ec.europa.eu/ 2. Folkman J. Role of angiogenesis in tumor growth and metastasis. Semin Oncol 2002; 29: 15-8. doi: 10.1053/sonc.2002.37263 3. Stylianopoulos T, Munn LL, Jain RK. Reengineering the tumor vasculature: improving drug delivery and efficacy. Trends Cancer 2018; 4: 258-9. doi: 10.1016/j.trecan.2018.02.010 4. Sersa G, Ursic K, Cemazar M, Heller R, Bosnjak M, Campana LG. Biological factors of the tumour response to electrochemotherapy: review of the evidence and a research roadmap. Eur J Surg Oncol 2021; 47: 1836-46. doi: 10.1016/j.ejso.2021.03.229 5. Kanthou C, Tozer G. Targeting the vasculature of tumours: combining VEGF pathway inhibitors with radiotherapy. Brit J Radiol 2019; 92: 20180405. doi: 10.1259/bjr.20180405 6. Popiel B, Gupta D, Misra S. Value of an intraoperative real time tissue perfu- sion assessment system following a nipple-sparing radical mastectomy for advanced breast cancer. Int J Surg Case Rep 2014; 5: 30-3. doi: 10.1016/j. ijscr.2013.11.007 7. Crawford T, Moshnikova A, Roles S, Weerakkody D, DuPont M, Carter LM, et al. pHLIP ICG for delineation of tumors and blood flow during fluorescence- guided surgery. Sci Rep 2022; 10: 18356. doi: 10.1038/s41598-020-75443-5 8. Pruimboom T, Lindelauf AAMA, Felli E, Sawor JH, Deliaert AEK, van der Hulst RRWJ, et al. Perioperative hyperspectral imaging to assess mastectomy skin flap and DIEP flap perfusion in immediate autologous breast reconstruction: a pilot study. Diagnostics 2022; 12: 184. doi: 10.3390/diagnostics12010184 9. Köhler H, Jansen-Winkeln B, Maktabi M, Barberio M, Takoh J, Holfert N, et al. Evaluation of hyperspectral imaging (HSI) for the measurement of ischemic conditioning effects of the gastric conduit during esophagectomy. Surg Endosc 2019; 33: 3775-82. doi: 10.1007/s00464-019-06675-4 10. Trinh A, Wintermark M, Iv M. Clinical review of computed tomography and MR perfusion imaging in neuro-oncology. Radiol Clin North Am 2021; 59: 323-34. doi: 10.1016/j.rcl.2021.01.002 11. van Manen L, Handgraaf HJM, Diana M, Dijkstra J, Ishizawa T, Vahrmeijer AL, et al. A practical guide for the use of indocyanine green and methylene blue in fluorescence-guided abdominal surgery. J Surg Oncol 2018; 118: 283-300. doi: 10.1002/jso.25105 12. Wiesinger I, Jung F, Jung EM. Contrast-enhanced ultrasound (CEUS) and perfusion imaging using VueBox®. Clin Hemorheol Microcirc 2021; 78: 29- 40. doi: 10.3233/CH-201040 13. Jacques SL. Optical properties of biological tissues: a review. Phys Med Biol 2013; 58: R37-61. doi: 10.1088/0031-9155/58/11/R37 14. Bashkatov AN, Genina EA, Tuchin VV. Optical properties of skin, subcutane- ous, and muscle tissues: a review. J Innov Opt Health Sci 2011; 04: 9-38. doi: 10.1142/S1793545811001319 15. Pfahl A, Köhler H, Thomaßen MT, Maktabi M, Bloße AM, Mehdorn M, et al. Clinical evaluation of a laparoscopic hyperspectral imaging system. Surg Endosc 2022; 36: 7794-9. doi: 10.1007/s00464-022-09282-y 16. Goetz AFH, Vane G, Solomon JE, Rock BN. Imaging spectrometry for earth remote sensing. Science 1985; 228: 1147-53. doi: 10.1126/sci- ence.228.4704.1147 17. Selci S. The future of hyperspectral imaging. J Imaging 2019; 5: 84. doi: 10.3390/jimaging5110084 18. Govender M, Chetty K, Bulcock H. A review of hyperspectral remote sens- ing and its application in vegetation and water resource studies. Water SA [Internet]. 2007; 33: 145-51. [cited 2022 Oct 8]. Available from: http://www. ajol.info/index.php/wsa/article/view/49049 19. Castro-Esau K. Discrimination of lianas and trees with leaf-level hyper- spectral data. Remote Sens Environ 2004; 90: 353-72. doi: 10.1016/j. rse.2004.01.013 20. Schimleck L, Ma T, Inagaki T, Tsuchikawa S. Review of near infrared hyper- spectral imaging applications related to wood and wood products. Appl Spectrosc Rev 2022; 1-25. doi: 10.1080/05704928.2022.2098759 21. Puchert T, Lochmann D, Menezes JC, Reich G. Near-infrared chemical imag- ing (NIR-CI) for counterfeit drug identification—A four-stage concept with a novel approach of data processing (Linear Image Signature). J Pharm Biomed Anal 2010; 51: 138-45. doi: 10.1016/j.jpba.2009.08.0221 22. Feng YZ, Sun DW. Application of hyperspectral imaging in food safety inspec- tion and control: a review. Crit Rev Food Sci Nutr 2012; 52: 1039-58. doi: 10.1080/10408398.2011.651542 Radiol Oncol 2022; 56(4): 420-429. Hren R et al./Hyperspectral imaging in oncology 429 23. Huang H, Liu L, Ngadi M. Recent developments in hyperspectral imaging for assessment of food quality and safety. Sensors 2014; 14: 7248-76. doi: 10.3390/s140407248 24. Gowen A, Odonnell C, Cullen P, Downey G, Frias J. Hyperspectral imaging – an emerging process analytical tool for food quality and safety control. Trends Food Sci Technol 2007; 18: 590-8. doi: 10.1016/j.tifs.2007.06.001 25. Soni A, Dixit Y, Reis MM, Brightwell G. Hyperspectral imaging and machine learning in food microbiology: Developments and challenges in detection of bacterial, fungal, and viral contaminants. Comp Rev Food Sc Food Safe 2022; 21: 3717-45. doi: 10.1111/1541-4337.12983 26. Balas C, Epitropou G, Tsapras A, Hadjinicolaou N. Hyperspectral imaging and spectral classification for pigment identification and mapping in paintings by El Greco and his workshop. Multimed Tools Appl 2018; 77: 9737-51. doi: 10.1007/s11042-017-5564-2 27. Sandak J, Sandak A, Legan L, Retko K, Kavčič M, Kosel J, et al. Nondestructive evaluation of heritage object coatings with four hyperspectral imaging sys- tems. Coatings 2021; 11: 244. doi: 10.3390/coatings11020244 28. Yuen PW, Richardson M. An introduction to hyperspectral imaging and its application for security, surveillance and target acquisition. Imaging Sci J 2010; 58: 241-53. doi: 10.1179/174313110X12771950995716 29. Ortega S, Fabelo H, Camacho R, de la Luz Plaza M, Callicó GM, Sarmiento R. Detecting brain tumor in pathological slides using hyperspectral imaging. Biomed Opt Express 2018; 9: 818. doi: 10.1364/BOE.9.000818 30. Ortega S, Fabelo H, Iakovidis D, Koulaouzidis A, Callico G. Use of hyperspec- tral/multispectral imaging in gastroenterology. Shedding some – different – light into the dark. J Clin Med 2019; 8: 36. doi: 10.3390/jcm8010036 31. Ma L, Halicek M, Zhou X, Dormer JD, Fei B. Hyperspectral microscopic imag- ing for automatic detection of head and neck squamous cell carcinoma us- ing histologic image and machine learning. In: Tomaszewski JE, Ward AD, ed- itors. Medical Imaging 2020: Digital Pathology [Internet]. Houston, United States: SPIE; 2020. p. 31. [cited 2022 Oct 8]. Available from: https://www. spiedigitallibrary.org/conference-proceedings-of-spie/11320/2549369/ Hyperspectral-microscopic-imaging-for-automatic-detection-of-head-and- neck/10.1117/12.2549369.full 32. Keller A. A new diagnostic algorithm for early prediction of vascular compromise in 208 microsurgical flaps using tissue oxygen satura- tion measurements. Ann Plast Surg 2009; 62: 538-43. doi: 10.1097/ SAP.0b013e3181a47ce8 33. Jafari-Saraf L, Wilson SE, Gordon IL. Hyperspectral image measurements of skin hemoglobin compared with transcutaneous PO2 measurements. Ann Vasc Surg 2012; 26: 537-48. doi: 10.1016/j.avsg.2011.12.002 34. Best SL, Thapa A, Jackson N, Olweny E, Holzer M, Park S, et al. Renal oxygenation measurement during partial nephrectomy using hyperspectral imaging may predict acute postoperative renal function. J Endourol 2013; 27: 1037-40. doi: 10.1089/end.2012.0683 35. Rose K, Krema H, Durairaj P, Dangboon W, Chavez Y, Kulasekara SI, et al. Retinal perfusion changes in radiation retinopathy. Acta Ophthalmol 2018; 96: e727-31. doi: 10.1111/aos.13797 36. Chin MS, Siegel-Reamer L, FitzGerald GA, Wyman A, Connor NM, Lo YC, et al. Association between cumulative radiation dose, adverse skin reactions, and changes in surface hemoglobin among women undergoing breast conserving therapy. Clin Transl Radiat Oncol 2017; 4: 15-23. doi: 10.1016/j. ctro.2017.03.003 37. Fabelo H, Ortega S, Lazcano R, Madroñal D, M. Callicó G, Juárez E, et al. An intraoperative visualization system using hyperspectral imaging to aid in brain tumor delineation. Sensors 2018; 18: 430. doi: 10.3390/s18020430 38. Fabelo H, Ortega S, Ravi D, Kiran BR, Sosa C, Bulters D, et al. Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations. Fred AL, editor. PLoS ONE 2018; 13: e0193721. 39. Fabelo H, Halicek M, Ortega S, Shahedi M, Szolna A, Piñeiro J, et al. Deep learning-based framework for in vivo identification of glioblastoma tumor using hyperspectral images of human brain. Sensors 2019; 19: 920. doi: 10.3390/s19040920 40. Fabelo H, Ortega S, Szolna A, Bulters D, Pineiro JF, Kabwama S, et al. In-vivo hyperspectral human brain image database for brain cancer detection. IEEE Access 2019; 7: 39098-116. doi: 10.1109/ACCESS.2019.2904788 41. Jansen-Winkeln B, Maktabi M, Takoh JP, Rabe SM, Barberio M, Köhler H, et al. [Hyperspectral imaging in gastrointestinal anastomoses]. [German]. Chirurg 2018; 89: 717-25. 42. Moulla Y, Reifenrath M, Rehmet K, Niebisch S, Jansen-Winkeln B, Sucher R, et al. [Hybrid esophagectomy with intraoperative hyperspectral imaging: video contribution]. [German]. Chirurg 2020; 91(S1): 1-12. 43. Schwandner F, Hinz S, Witte M, Philipp M, Schafmayer C, Grambow E. Intraoperative assessment of gastric sleeve oxygenation using hyperspectral imaging in esophageal resection: a feasibility study. Visc Med 2021; 37: 165- 70. doi: 10.1159/000509304 44. Hennig S, Jansen-Winkeln B, Köhler H, Knospe L, Chalopin C, Maktabi M, et al. Novel intraoperative imaging of gastric tube perfusion during oncologic esophagectomy – a pilot study comparing hyperspectral imaging (HSI) and fluorescence imaging (FI) with indocyanine green (ICG). Cancers 2021; 14: 97. doi: 10.3390/cancers14010097 45. Moulla Y, Buchloh DC, Köhler H, Rademacher S, Denecke T, Meyer HJ, et al. Hyperspectral Imaging (HSI) – A new tool to estimate the perfusion of up- per abdominal organs during pancreatoduodenectomy. Cancers 2021; 13: 2846. doi: 10.3390/cancers13112846 46. Jansen-Winkeln B, Holfert N, Köhler H, Moulla Y, Takoh JP, Rabe SM, et al. Determination of the transection margin during colorectal resection with hyperspectral imaging (HSI). Int J Colorectal Dis 2019; 34: 731-9. doi: 10.1007/s00384-019-03250-0 47. Jansen-Winkeln B, Germann I, Köhler H, Mehdorn M, Maktabi M, Sucher R, et al. Comparison of hyperspectral imaging and fluorescence angiography for the determination of the transection margin in colorectal resections – a comparative study. Int J Colorectal Dis 2021; 36: 283-91. doi: 10.1007/ s00384-020-03755-z 48. Pfahl A, Radmacher GK, Köhler H, Maktabi M, Neumuth T, Melzer A, et al. Combined indocyanine green and quantitative perfusion assessment with hyperspectral imaging during colorectal resections. Biomed Opt Express 2022; 13: 3145. doi: 10.1364/BOE.452076 49. Jansen-Winkeln B, Barberio M, Chalopin C, Schierle K, Diana M, Köhler H, et al. Feedforward artificial neural network-based colorectal cancer detec- tion using hyperspectral imaging: a step towards automatic optical biopsy. Cancers 2021; 13: 967. doi: 10.3390/cancers13050967 50. Jansen-Winkeln B, Dvorak M, Köhler H, Maktabi M, Mehdorn M, Chalopin C, et al. Border line definition using hyperspectral imaging in colorectal resec- tions. Cancers 2022; 14: 1188. doi: 10.3390/cancers14051188 51. Barberio M, Lapergola A, Benedicenti S, Mita M, Barbieri V, Rubichi F, et al. Intraoperative bowel perfusion quantification with hyperspectral imaging: a guidance tool for precision colorectal surgery. Surg Endosc [Internet]. 14 July 2022. [cited 2022 Oct 8]. Available from: https://link.springer.com/10.1007/ s00464-022-09407-3 52. Kohler LH, Köhler H, Kohler S, Langer S, Nuwayhid R, Gockel I, et al. Hyperspectral imaging (HSI) as a new diagnostic tool in free flap monitoring for soft tissue reconstruction: a proof of concept study. BMC Surg 2021; 21: 222. doi: 10.1186/s12893-021-01232-0