Dose distribution with Monte Carlo simulation, imaging, and pharmacokinetic studies of radiopharmaceutical for colon cancer

Dose distribution with Monte Carlo simulation, imaging, and pharmacokinetic studies of radiopharmaceutical for colon cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dose distribution with Monte Carlo simulation, imaging, and pharmacokinetic studies of radiopharmaceutical for colon cancer Emre Özgenç, Evren Atlıhan Gundogdu, Yücel Başpınar, Umit Kara, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7177199/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Colon cancer is a widespread form of cancer worldwide. According to FDA-approved radio imaging, nanosystems are the primary carriers of radionuclides. This study examines colon tissue's specific mass attenuation coefficient and its significance in medical imaging and radiation therapy for various conditions, including colorectal cancer. Understanding the mass attenuation coefficient of colon tissue can help optimize nuclear imaging techniques like SPECT/CT and PET/CT and minimize damage to healthy tissue surrounding the affected area during radiation therapy. Studying colon tissue's specific mass attenuation coefficient and its variations in different pathological conditions can significantly contribute to developing more sensitive and targeted medical imaging and treatment strategies for colorectal cancer and other colon-related diseases. It was determined that the Tc-99m-IMT-NSLC formulation was retained in the colon cancer area in higher amounts than the Tc-99m-IMT solution. In addition, using Monte Carlo simulation, MATLAB and WinXCOM calculated how photons are attenuated by the small intestine tissue, which is essential in nuclear medicine, medical imaging, and radiation dosimetry. This targeted research can significantly contribute to an advanced understanding of the unique characteristics and applications of the mass attenuation coefficient in the context of colon tissue, fostering advancements in medical science and technology. Monte Carlo simulation Imatinib Colon Cancer nanocarriers Tc-99m Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Colon cancer is one of the most common human malignancies in the world 1 . Even though clinical treatment for colon cancer has been improved with the development of new drugs and formulations, most colon cancer patients show limited response to targeted imaging 2 . Many current treatments do not provide curative or disease-controlling effects and are associated with clinical side effects, negatively impacting patients' quality of life 3 . Nanosystems represent the premier carriers of radionuclides, playing a crucial role in the efficient transport of these important materials 4 , 5 among FDA-approved radioimaging. Apart from the high production cost of nanosystems, many radioisotopes imaging fail to exert the desired localization. Recurrent tumor imaging is influenced by a multifactorial interplay of underlying mechanisms, including genomic instability, adaptive cellular responses, the bystander effect, intratumoral cellular heterogeneity, and inherent radiation resistance. 4 , 6 . As a result, tumors that are not fully eradicated facilitate the selection and growth of the most resilient tumor cell clones. Simulation techniques are indispensable in radiopharmaceuticals, particularly in dosimetry and treatment planning for radionuclide therapies. These techniques employ advanced computational models to simulate the behavior of radionuclides within the body, assisting in treatment optimization and assessing radiation doses delivered to target tissues and organs 7 . In radiopharmaceutical therapy, simulations determine dose distributions within healthy tissues. These simulations consider various factors, like energy deposition and radiation transport, to ensure accurate and effective treatment planning 8 . Monte Carlo simulations are pivotal in accurately predicting absorbed doses in both tumors and critical organs, underscoring their significance in precise dosimetry calculations for radionuclide therapy 9 . The energy-dependent mass attenuation coefficient (MAC) (µ/ρ/) is a crucial parameter in radiological physics. It represents the probability of interaction per unit path length per unit mass of the material (cm²/g). MAC measures how much a particular material can attenuate a beam of radiation, such as X-rays or gamma rays, as it passes through that material. The small intestine organ is interested in medical physics, particularly diagnostic radiology, nuclear medicine, and radiation therapy. Understanding how photons interact with matter is crucial in various medical contexts. The MAC (µ/ρ) is a critical factor in measuring a material's ability to reduce the intensity of a photon beam as it passes through it. Imatinib (IMT), used to treat … cancer, has been widely studied for its pharmacokinetic and pharmacodynamic properties. A study by Gundogdu et al. used Tc-99m-IMT and Tc-99m radiolabeled nanostructured lipid carrier systems (Tc-99m-NSLC) to image experimental animals with colon cancer and healthy mice 10 . While references provide valuable information about the MAC in various materials and contexts, none directly relate to its interaction with colon tissue. Therefore, this study aimed to understand colon tissue's specific MAC and its importance in medical imaging and radiation therapy for conditions like colorectal cancer. Knowing the MAC of colon tissue can help optimize nuclear imaging techniques such as SPECT/CT and PET/CT and minimize damage to the surrounding healthy tissue during radiation therapy. Investigating colon tissue's specific MAC and its variations in different pathological conditions can contribute significantly to developing more sensitive and targeted medical imaging and treatment strategies for colorectal cancer and other colon-related diseases. 2. Materials and Methods 2.1 Materials IMT was purchased from Roche (Basel, Switzerland), and all solvents and chemicals were obtained from Merck (Darmstadt, Germany). 2.2 Methods 2.2.1 Preparation and characterization of radiolabeled nanostructured lipid carrier system The IMT-NSLC was prepared by using the emulsification–sonication method and characterized as described by Gundogdu et al. 2022. Briefly, oleic acid (14.28%) and Gelucire 48/16 (28.57%), a gelucire derivative, were used as the lipid phase, and Span 80 (28.57%) was used as a surfactant. The lipid phase was melted using a water bath maintained at 70–80°C, to which 1 mg of IMT was subsequently added. Meanwhile, an aqueous solution comprising a water and acetone-ethanol mixture along with Span 80 was prepared and heated to match the temperature of the lipid phase. The lipid and aqueous phases were blended at 10,000 rpm for 10 minutes using an Ultra-Turrax blender (IKA®-Werke GmbH & Co., Staufen, Germany) to create an emulsion. Subsequently, sonication was performed at 500 W and 20 kHz in alternating 20-second cycles for a total of 15 minutes with a Vibracell tip sonicator (IKA®-Werke GmbH & Co., Staufen, Germany), after which the mixture was allowed to cool to room temperature. After this, the IMT-NSLC was labeled with Tc-99m, and the radiolabeling conditions were outlined in our previous study (Gundogdu et al., 2020). The IMT-NSLC was characterized in terms of particle size, polydispersity index (PDI), and zeta potential (ZP) using Malvern Zetasizer (Malvern Nano ZS 90, Malvern, UK) with a particle size range of 3–1000 nm and an angle of 173°. The samples were diluted using distilled water (pH = 7). The zeta potential of IMT-NSLC was measured at 40 V/cm with a DTS 1060C zeta cuvette (Malvern, UK) at a temperature of 25°C, utilizing a conductivity of 5 mS/cm and a dielectric constant of 78.5. Measurements were conducted in triplicate, and the results are presented as mean values ± standard deviation. In vivo studies were carried out using Tc-99m-IMT-NSLC and a Tc-99m-IMT solution. 2.2.2 In Vivo animal studies All studies were performed with 12 healthy and 12 colon cancer nude mice. The male colon cancer model was developed using nude mice (approximately 30 g), which were sourced from the Experimental Animals Laboratory Joint Stock Company. These mice were housed in standard plastic cages lined with white Pinus wood shavings for bedding, topped with stainless steel cover lids. All experiments were conducted following the Guide for the Care and Use of Laboratory Animals (NIH). The study protocol received approval from both the Institutional Review Board and the Animal Ethics Committee (2019–106). Colon cancer induction in mice was performed using CRL-1739 (gastric adenocarcinoma cells). CRL-1739 cell suspension at a density of 1 x 108 in DMEM containing 50 µL fetal bovine serum and 450 µL 4.5 g/L glutamine was injected intraperitoneally into the animals' right or left peritoneal cavity. All cell suspension injection volumes were adjusted to 0.5 mL and administered one day apart. Tumor formation was completed in five weeks. The groups to be used in the in vivo study and their numbers are shown in Table 1 . Table 1 Groups and numbers to be used in vivo study. groups IMT solution radiolabeled Tc-99m-IMT-NSLC control group- healthy Balb-C-Nude mice 6 6 experimental group- Balb-C-Nude mice with colon cancer 6 6 Tc-99m-IMT- NSLC and Tc-99m-IMT solutions underwent in vivo whole-body imaging studies. CRL cells were injected into mice. All mice were anesthetized via intraperitoneal injection of a ketamine-xylazine solution. Following this, 7.4 MBq of Tc-99m-NLTS-IMT was injected into the mice through the tail vein. The anesthetized mice were then positioned horizontally beneath the SPECT/CT imaging system. Approximately one hour after the radiotracer injection, images were captured using a matrix size of 256 × 256, with an energy window set at 20% at 140 keV. The same administration procedure was followed for the Tc-99m-IMT solution. After administering the Tc-99m-IMT-NSLC and Tc-99m-IMT solution, blood samples were collected from healthy, cancerous mice at 0, 30, 60, 90, and 120 minutes. 200 µL blood samples were taken for the pharmacokinetic studies, and the radioactivity amount was determined with a gamma counter (Sesa Uniscaler I/S). Thus, a pharmacokinetic profile was created based on the radioactivity amounts obtained, and pharmacokinetic parameters (area under the curve (AUC), elimination rate constant (ke), and mean residence time (MRT)) were calculated with the Winnonline 5.3 program. Repeated Measure Analyses of Variance (RM-ANOVA) in the SAS program performed statistical evaluations for each pharmacokinetic parameter. The animals were sacrificed at the end of the imaging and pharmacokinetic studies, and biodistribution studies were conducted. After the experimental animals were sacrificed, the large intestine, small intestine, kidney, liver, spleen, stomach, and heart tissues were removed. The gamma counter determined the amount of radioactivity in the weighted tissues, and the amount per gram was calculated. Target/non-target tissue uptake rates were evaluated. 2.2.3 Monte Carlo Simulation The gamma-ray attenuation properties are examined in detail in the literature by study and simulation 11 . The MAC value is one of these crucial parameters for reducing radiation. The MACs for the small intestine were acquired over a range of photon energies from 0.015 MeV to 0.2 MeV. Two distinct sources were utilized to obtain these coefficients: the WinXCOM database and computational Monte Carlo Simulation performed using MATLAB software. Parallel to the WinXCOM data, Monte Carlo Simulation in MATLAB and technical computing was employed to calculate the MACs. The MATLAB-based approach involved numerical methods that potentially included the interpolation or integration of tabulated cross-sections from recognized databases, such as NIST XCOM. The algorithms were designed to ensure high precision and were validated against known benchmarks. The MATLAB simulations were based on a computational model that incorporated the physical principles governing the interaction of photons with matter, like the photoelectric effect, Compton scattering, and pair production. The model parameters were derived from established physical constants and interaction cross-sections provided by the National Institute of Standards and Technology (NIST) XCOM database. The Monte Carlo Simulation was developed to calculate the MACs, µ/ρ, which represent the probability of photon attenuation per unit mass density of the tissue. These algorithms employed numerical integration techniques to solve the relevant equations, ensuring that the energy-dependent coefficients were computed accurately. MATLAB version (R2002b), structured to meet the standards of academic reporting for simulation-based research in medical physics, radiology, nuclear medicine, and radiotherapy, was used, and the Monte Carlo Simulation was employed to calculate and analyze the data. The tissues' theoretical MAC was computed using the WinXcom program and MATLAB code, applying the Beer-Lambert law. 2.2 .4 Radiation Attenuation Parameters The linear attenuation coefficient (LAC) measures the decrease caused by interactions between high-energy photons released by a radioactive source and an absorber. This parameter is calculated using the Beer-Lambert law 12 , 13 . µ = -ln(I/I₀)/x In this equation, "x" represents the thickness of the absorber, whereas "I0" and "I" represent the initial and decreased photon intensities, respectively. The link between the LAC and the material's density is significant. The mixture rule helps validate the photon attenuation of the absorber based on the accuracy of the formulated materials: µₘ = µ/ρ = ∑(w i (µ/ρ) i ) (2) Here, µₘ and ρ signify the MACs (cm² g⁻¹)) and absorber density (g /cm 3 ), respectively. To calculate the adequate atomic number of any composite, one can use the total atomic cross-section (tot) and the electronic cross-section (e) 14 , 15 , 16 : Z_eff=(∑_i▒〖f_i A_i (µ⁄ρ)_i 〗)/(∑_j▒〖f_j A_j/Z_j (µ⁄ρ)_j 〗) This equation, f i , indicates the element's fractional abundance, and Ai and Zi are an element's atomic weight and atomic number, respectively. 3. Results 3.1 Particle characterization of radiolabeled nanostructured lipid carrier system The prepared IMT-NSLC had a particle size of 173,05 ± 9 nm, a PDI of 0.21 ± 0.1, and a ZP of -36.12 ± 0.4 mV on the day of preparation. The IMT-NSLC was stored for 15 days at 5 ± 3°C, 25 ± 2°C (60% RH), and 40 ± 2°C (75% RH), respectively. The particle size increased from 173,05 nm to 203,35 nm after storage of 15 days at 5 ± 3°C, to 220,15 nm after storage of 15 days at 25 ± 2°C (60% RH), and to 202,44 nm after storage of 15 days at 40 ± 2°C (75% RH), respectively. The same tendency was observed for the PDI, namely an increase from 0.21 on the day of preparation to 0.27 after storage of 15 days at 5 ± 3°C, to 0.32 after storage of 15 days at 25 ± 2°C (60% RH) and to 0.28 after storage of 15 days at 40 ± 2°C (75% RH), respectively. 3.2 In vivo animal studies Figures 1 and 2 show centigrams obtained from imaging studies performed with Tc-99m-labeled IMT solution and Tc-99m-IMT-NSLC formulation in healthy and colon cancer-induced mice. To precisely assess the uptake of both formulations in the cancerous region, digital tumor foci were delineated, and the radioactivity per pixel was calculated using the corresponding area values (ROI values). The findings of this analysis are presented in Table 2 . Table 2 ROI values obtained in the colon cancer region for Tc-99m-IMT solution and Tc-99m-IMT-NSLC. Time (minute) ROI value for Tc-99m-IMT solution in the colon cancer area ROI value for Tc-99m-IMT-NLTS in the colon cancer region 0 152 ± 10 736 ± 24 60 821 ± 98 1235 ± 42 120 1192 ± 52 4140 ± 91 Pharmacokinetic profiles of Tc-99m-IMT solution and Tc-99m-IMT-NSLC formulation are shown in Fig. 3 and Fig. 4 . Table 3 shows the pharmacokinetic parameters of Tc-99m-IMT solution and Tc-99m-IMT-NSLC formulation. Biodistribution studies have revealed the amounts of radioactivity per gram of the relevant organs shown in Table 4 and Figs. 5 and 6 . Table 3 Pharmacokinetic parameters and statistical evaluation application (cancerous mice) AUC (cpm*minute) standard deviation (sd) p-value Tc-99m-IMT solution 497 79 0.041 Tc-99m-IMT-NSLC 1194 111 application (healthy mice) AUC (cpm*minute) sd p-value Tc-99m-IMT solution 279 55 0.005 Tc-99m-IMT-NSLC 465 47 application (cancerous mice) ke (hour-1) Sd p-value Tc-99m-IMT solution 0.071 0.0087 0.040 Tc-99m-IMT-NSLC 0.19 0.0046 application (healthy mice) ke (hour-1) Sd p-value Tc-99m-IMT solution 0.0024 0.00019 0.036 Tc-99m-IMT-NSLC 0.055 0.0067 application (cancerous mice) MRT (min) Sd p-value Tc-99m-IMT solution 19.83 7.13 0.041 Tc-99m-IMT-NSLC 65.12 9.23 application (healthy mice) MRT (min) Sd p-value Tc-99m-IMT solution 9.89 4.56 0.037 Tc-99m-IMT-NSLC 25.56 8.77 Table 4 The amount of radioactivity per gram of the organs involved as a result of biodistribution studies Radioactivity quantities (cpm) Tc-99m-IMT solution Tc-99m-IMT-NSLC healthy mice mice with colon cancer healthy mice mice with colon cancer large intestine 80 ± 12.5 408 ± 16.6 73 ± 16.4 1182 ± 14.7 small intestine 57 ± 7.4 47 ± 6.1 24 ± 8.8 35 ± 5 kidney 36 ± 13.1 35 ± 7.9 39 ± 13.1 38 ± 7.2 liver 29 ± 15.9 20 ± 9.5 20 ± 10.9 20 ± 8.4 spleen 37 ± 9 58 ± 9.6 52 ± 8.5 48 ± 9.1 stomach 28 ± 18 30 ± 12.1 57 ± 10.8 106 ± 13.4 heart 262 ± 5.5 26 ± 7.3 10 ± 12.6 13 ± 8.9 Gamma counting was used to determine radioactivity levels per gram in both cancerous and healthy colon areas in mice, and target/non-target uptake ratios were calculated and presented in Table 5 . Table 5 Target/non-target uptake ratios calculated by dividing the radioactivity values by each other (colon cancer area/healthy colon area) Radiopharmaceutical Radioactivity of colon cancer site (cpm) Radioactivity of healthy colon site (cpm) Colon cancer site/Healthy colon site uptake ratio Tc-99m-IMT solution 264 ± 24.4 144 ± 8.2 1.8 ± 1.1 Tc-99m-IMT-NSLC 1003 ± 34 180 ± 5.5 5.6 ± 3 3.3 Monte Carlo Simulation The mean MACs exhibit close values in the small intestine and colon. For the small intestine, the coefficients are approximately 4.95183 cm²/g for WinXCOM and 4.93718 cm²/g for MATLAB. These averages are computed across different energy levels specified in our dataset, ranging from 0.015 MeV to 0.2 MeV, forming a comprehensive spectrum. Similarly, in the colon, mean MACs are approximately 4.00921 cm²/g for WinXCOM and 4.071367 cm²/g for MATLAB. These values, averaged across diverse energy levels, are presented in Table 6 for various photon energies expressed in MeV. This energy range is particularly significant for medical diagnostic applications, notably in nuclear medicine, where low-energy photons find common use. This spectrum includes low-energy photons, typically interacting through the photoelectric effect, and extends to higher-energy photons where Compton scattering is the predominant interaction. The MAC (µ/ρ) is a crucial parameter in radiological physics, representing the probability of interaction per unit path length per unit mass of the material and energy dependent. It is typically measured in cm²/g. The values provided here are for the small intestine organ, which is of interest in medical physics and nuclear medicine. The data presented here are similar to the values obtained from WinXCOM and MATLAB, indicating a high level of detail and accuracy in our calculations. The data presented in Table 1 showcase the percentage difference between the WinXCOM and MATLAB values, offering a valuable metric for assessing the consistency and accuracy of MATLAB calculations compared to the established WinXCOM standard. Most of these percentage differences reveal minimal deviation, strongly suggesting a close alignment between the MATLAB calculations and the data derived from WinXCOM. The values presented in Table 6 are likely based on theoretical calculations or empirical data. This dataset effectively quantifies the relative disparities between the WinXCOM and MATLAB results. Notably, a low percentage deviation indicates a high level of agreement between these two calculation methods. 3.4 Radiation Attenuation Parameters The fluctuation of MAC WinXcom versus energy is seen in Fig. 7 . As shown in Fig. 7 , the MAC values for all samples display distinct characteristics that may be categorized into two energy regions: A substantial drop in MAC values is found at low energies (< 0.05 MeV) due to the prevalence of photoelectric effect at intermediate energies (from 0.05 to 0.2 MeV), there is a modest decrease in MAC values, which may be explained by the dominance of incoherent (Compton) scattering in this energy range. Aside from incident photon energy, the chemical composition of a material affects the change of MAC 17 . Consequently, our findings show that as Ti concentration increases, so do MAC values (all other constituent elements are low-Z materials, Z ≤ 22). Zeff is a parameter used to characterize the radiation response of a multi-element material in numerous technical and medical applications and to determine the substitute for a material. Figure 8 depicts the relationship between adequate atomic number and energy. Table 6 The mass attenuation coefficients (cm2/g) values derived from MATLAB and WinXCOM for selected organs and their relative deviations. Small intestine Colon Stomach MeV MATLAB WinXCOM % Dev MATLAB WinXCOM % Dev MATLAB WinXCOM % Dev 0.015 28.70 28.57 0.005 23.58 23.00 0.025 24.43 24.02 0.017 0.02 12.59 12.64 0.004 10.15 10.18 0.003 10.60 10.63 0.003 0.03 3.85 4.00 0.035 3.30 3.23 0.021 3.44 3.37 0.020 0.04 1.78 1.79 0.006 1.46 1.47 0.006 1.52 1.53 0.006 0.05 1.00 1.00 0.005 0.84 0.83 0.005 0.87 0.86 0.005 0.06 0.65 0.64 0.013 0.55 0.55 0.013 0.57 0.57 0.013 0.08 0.35 0.35 0.008 0.31 0.31 0.007 0.32 0.32 0.007 0.1 0.25 0.25 0.001 0.23 0.23 0.001 0.23 0.23 0.001 0.15 0.16 0.16 0.008 0.15 0.15 0.007 0.15 0.15 0.008 0.2 0.13 0.13 0.00004 0.13 0.13 0.0001 0.13 0.13 0.0007 4. Discussion The images (Figs. 1 and 2) obtained from SPECT scans are color-coded, with shades ranging from dark blue to red, indicating increased radioactivity from the former to the latter. The collected data reveal that the cancerous area exhibits more intense radioactivity compared to the Tc-99m-IMT solution within the first and second hours after the application of IMT-NSLC radiolabeled with Tc-99m. The amount of radioactivity of the Tc-99m-IMT-NSLC formulation in the colon cancer area was between 736.32 ± 24.3 and 4139.71 ± 130.83, while this value was between 151.69 ± 9.67 and 1191.98 ± 52.52 in the Tc-99m-IMT solution. It was determined that the Tc-99m-IMT-NSLC formulation was retained in higher amounts at the colon cancer site than the Tc-99m-IMT solution. According to the study, healthy and colon cancer-induced mice showed higher blood radioactivity values after receiving the Tc-99m-IMT-NSLC formulation than the Tc-99m-IMT solution (p < 0.05). It was noted that there was a significant difference in the blood radioactivity values of Tc-IMT-solution between healthy and cancerous animals (p < 0.05). After administering Tc-99m-IMT, the radioactivity in the blood of cancerous mice was found to be higher for each period as compared to healthy mice. Furthermore, a statistically significant difference was observed in the blood radioactivity values of Tc-99m-IMT-NSLC formulation between healthy and cancerous animals (p < 0.05). When Tc-99m-IMT-NSLC was administered, the radioactivity in the blood of healthy mice was higher at 30 and 60 minutes, while it was lower at 90 and 120 minutes compared to cancerous mice. The present study reports the results of pharmacokinetic and biodistribution studies (Tables 3 , 4 , and Figs. 5 , 6 ) conducted on mice with cancer, aimed at comparing the bioavailability of two different formulations, namely, Tc-99m-IMT-NSLC and Tc-99m-IMT. The study evaluated several bioavailability parameters, including AUC, ke, and OCZ values, and the radioactivity amounts in various organs. The highest AUC and ke values were obtained following the administration of Tc-99m-IMT-NSLC to cancerous mice. Upon terminating the studies, mice were sacrificed, and their organs, namely the kidney, spleen, small intestine, large intestine, heart, liver, and stomach, were removed and weighed. The radioactivity amounts in these organs were subsequently determined using a gamma counter, and the amount of radioactivity per gram was calculated. The results of the biodistribution studies indicated that the Tc-99m-labeled NSLC was more effective than the Tc-99m-IMT formulation in healthy and cancerous mice. The radioactivity amounts per gram of the organs are presented in Table 5 with their standard deviations and in histograms in Figs. 5 and 6 . In summary, the results of the present study suggest that Tc-99m-IMT-NSLC is a more adequate formulation than Tc-99m-IMT in both healthy and cancerous mice, as evidenced by the higher AUC and ke values, and the higher radioactivity amounts in various organs. These findings have significant implications for developing more effective and targeted cancer treatments. Radioactivity levels were higher in the large intestines of healthy and cancerous mice treated with Tc-99m-IMT solution and Tc-99m-IMT-NSLC. In healthy mice treated with Tc-99m-IMT solution, the amounts of radioactivity per gram of organs were highest in the large intestine, followed by the small intestine, spleen, kidney, liver, stomach, and heart. Similarly, in cancerous mice treated with Tc-99m-IMT solution, the large intestine showed the highest radioactivity levels per gram, followed by the spleen, small intestine, kidney, stomach, liver, and heart. When healthy mice were administered Tc-99m-IMT-NSLC, the large intestine had the highest levels of radioactivity per gram, followed by the stomach, spleen, kidney, small intestine, liver, and heart. In cancerous mice administered Tc-99m-IMT-NSLC, the large intestine showed the highest radioactivity levels per gram, followed by the stomach, spleen, kidney, small intestine, liver, and heart. The radioactivity levels were measured in both the colon cancer and healthy colon regions during the Tc-99m-IMT-NSLC and Tc-99m-IMT solution applications. The results indicated a higher radioactivity in the colon cancerous region for both formulations. Specifically, Tc-99m-IMT-NSLC had an activity of 1002.61 ± 33.98 cpm in the colon cancer region and 179.66 ± 5.50 cpm in the healthy colon region. In contrast, the Tc-99m-IMT solution had an activity of 263.79 ± 24.35 cpm in the colon cancer region and 144.02 ± 8.23 cpm in the healthy colon region. Furthermore, Tc-99m-IMT-NSLC had a higher uptake ratio of the colon cancerous region to the healthy colon region than Tc-99m-IMT solution, with an uptake ratio of 5.58 ± 3.02. Lastly, the percentage uptake of the formulations administered to mice with colon cancer was calculated by analyzing the amount of radioactivity in their organs. The percentage uptake was 69.53 ± 2.31% for the Tc-99m-labeled F9-IMT formulation and 42.28 ± 2.31% for the Tc-99m-IMT solution. Table 1 compares MACs (µ/ρ/) for the small intestine at various photon energy levels, measured in Mega-electron Volts (MeV). These coefficients are essential for how photons are attenuated by the small intestine tissue, which is significant in nuclear medicine, medical imaging, and radiation dosimetry. These values are calculated with MATLAB and WinXCOM, which provide photon cross-sections for scattering, photoelectric absorption, and pair production in various materials. Zeff appears to depend highly on photon energy on all the samples investigated. Because of the prominence of the photoelectric effect (interaction cross-section, σpe ~ E-7/2), the highest values of Zeff have been obtained at lower energies. The lowest values of Zeff are seen in the energy range where Compton scattering is important (interaction cross-section, σcomp ~ E-1). Zeff values are nearly constant in this energy area because they are independent of energy 18 . Furthermore, adequate atomic numbers vary depending on the range of constituent atomic numbers and their proportions in the material. This indicates that the higher the samples' Ti (highest-Z constituent) ratio, the higher the effective atomic number values. 5. Conclusions Animal studies were conducted to evaluate the effects of administering Tc-99m-IMT solution and Tc-99m-IMT-NSLC on colon cancer. Pharmacokinetic parameters such as AUC, MRT, and ke were calculated from blood samples collected from healthy and cancerous mice treated with these formulations. Additionally, biodistribution studies were conducted at the end of the imaging and pharmacokinetic studies. Radioactivity values from blood samples were determined using the Phoenix Winnonline 5.3 program in pharmacokinetic studies. The pharmacokinetic parameters AUC, ke, and MRT (Mean Residence Time ) values were analyzed using SAS's Repeated Measures Analysis of Variance (RM-ANOVA) program. The results indicated that the Tc-99m-IMT-NSLC had higher pharmacokinetic parameters than the Tc-99m-IMT formulation in healthy and cancerous mice. Furthermore, imaging studies revealed that the Tc-99m-IMT-NSLC formulation had the highest uptake ratio of colon cancerous region to healthy colon region, exceeding (Figs. 1 and 2). Biodistribution studies conducted after two-hour imaging showed that the Tc-99m-IMT-NSLC formulation was more retained in the colon cancerous region than the Tc-99m-IMT solution. While the references provided valuable insights into the MAC in various materials and contexts, they did not directly address the specific interaction of the MAC with colon tissue. Therefore, there is a compelling need for further research, explicitly incorporating advanced techniques such as Monte Carlo simulation, to delve into the MAC of colon tissue. Investigating this aspect with sophisticated simulation methods will provide crucial information regarding its implications in medical imaging and radiation therapy. This targeted research can significantly contribute to advancing our understanding of the unique characteristics and applications of the mass attenuation coefficient in the context of colon tissue, fostering advancements in medical science and technology. Declarations Funding This research received no external funding. Institutional Review Board Statement: The Institutional Review Board and the Animal Ethics Committee approved the study protocol (2019–106). Conflicts of Interest The authors declare no conflicts of interest. Data availability Data will be made available on request. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021;71(3):209-49. doi:10.3322/caac.21660 Tolba MF. Revolutionizing the landscape of colorectal cancer treatment: the potential role of immune checkpoint inhibitors. Int J Cancer 2020;147(11):2996-3006. doi:10.1002/ijc.33056 Sgouros G, Bodei L, McDevitt MR, Nedrow JR. Radiopharmaceutical therapy in cancer: clinical advances and challenges. Nat Rev Drug Discov 2020;19(9):589-608. doi:10.1038/s41573-020-0073-9 Maeda H. 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Heterogeneity of dose distribution in normal tissues in case of radiopharmaceutical therapy with alpha-emitting radionuclides. Radiat Environ Biophys 2022;61(4):579-96. doi:10.1007/s00411-022-01000-5 Ranjbar H, Ghannadi-Maragheh M, Bahrami-Samani A, Beiki D. Dosimetric evaluation of 153Sm-EDTMP, 177Lu-EDTMP and 166Ho-EDTMP for systemic radiation therapy: influence of type and energy of radiation and half-life of radionuclides. Radiat Phys Chem 2015;108:60-4. doi:10.1016/j.radphyschem.2014.11.015 Gundogdu E, Demir E-S, Ekinci M, Ozgenc E, Ilem-Ozdemir D, Senyigit Z, et al. An innovative formulation based on nanostructured lipid carriers for imatinib delivery: pre-formulation, cellular uptake and cytotoxicity studies. Nanomaterials 2022;12(2):250. doi:10.3390/nano12020250 Kilicoglu O, Mehmetcik H. Science mapping for radiation shielding research. Radiat Phys Chem 2021;189:109721. doi:10.1016/j.radphyschem.2021.109721 Abuzaid MM, Susoy G, Issa SAM, Elshami W, Kilicoglu O, Tekin HO. Relationship between melting-conditions and gamma shielding performance of fluoro-sulfo-phosphate (FPS) glass systems: a comparative investigation. Ceram Int 2020;46(10 Pt A):15255-69. doi:10.1016/j.ceramint.2020.03.065 Kara U, Kilicoglu O, Ersoy S. Structural and gamma-ray attenuation coefficients of different OAD films for nuclear medicine applications. Radiat Phys Chem 2020;172:108785. doi:10.1016/j.radphyschem.2020.108785 Alothman MA, Alrowaili ZA, Al-Baradi AM, Kilicoglu O, Mutuwong C, Al-Buriahi MS. Elastic properties and radiation shielding ability of ZnO–P2O5/B2O3 glass system. J Mater Sci Mater Electron 2021;32(14):19203-17. doi:10.1007/s10854-021-06442-z Kilicoglu O. Characterization of copper oxide and cobalt oxide substituted bioactive glasses for gamma and neutron shielding applications. Ceram Int 2019;45(17 Pt B):23619-31. doi:10.1016/j.ceramint.2019.08.073 Tekin HO, Akman F, Issa SAM, Kaçal MR, Kilicoglu O, Polat H. Two-step investigation on fabrication and characterization of iron-reinforced novel composite materials for nuclear-radiation shielding applications. J Phys Chem Solids 2020;146:109604. doi:10.1016/j.jpcs.2020.109604 Kilicoglu O, Kara U, Ozgenc E, Gundogdu E. Pre-clinic study of radiopharmaceutical for Covid-19 inactivation: dose distribution with Monte Carlo simulation. Appl Radiat Isot 2022;188:110364. doi:10.1016/j.apradiso.2022.110364 Kilicoglu O, Sepay N, Ozgenc E, Gundogdu E, Kara U, Alomairy S, et al. Evaluation of F-18 FDG radiopharmaceuticals through molecular docking and radiation effects. Appl Radiat Isot 2023;191:110553. doi:10.1016/j.apradiso.2022.110553 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Özgenç","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYJADxgcQOoF4LcwGcC0HiNTCJkGUFn6xsw8fMFTU2fVLNz+r+PDLhoGfPceA+eMe3FokZ6cbGzCcYUueOeeY2c2ZfWkMkj1vDBgOPMOtxeB2GpsEYxtPssGNHLbbvD2HGYAMoBY8LrO/ncb+g7FNItkeqKX4b89/BntCWgyk09gYGNsM7AwkctiYGX4cYAAy8GuRuJ3GLJFwJiFB4kaasWRvQzKPxJlnBQfO4NHCPzuN8cOHijp7/hnJDz/8+GMnx9+evPFBBR4tYJDAwJDYAGIwtjHwgGhCGsDAHkL9IUbtKBgFo2AUjDQAAA91UHt2rin2AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-7586-8520","institution":"Ege university","correspondingAuthor":true,"prefix":"","firstName":"Emre","middleName":"","lastName":"Özgenç","suffix":""},{"id":489530412,"identity":"e6dcb25c-9e51-4614-a8c5-ad38850182f1","order_by":1,"name":"Evren Atlıhan Gundogdu","email":"","orcid":"","institution":"Ege university","correspondingAuthor":false,"prefix":"","firstName":"Evren","middleName":"Atlıhan","lastName":"Gundogdu","suffix":""},{"id":489530413,"identity":"0a20820f-7399-46bf-9af5-2117ba66b261","order_by":2,"name":"Yücel Başpınar","email":"","orcid":"","institution":"Ege university","correspondingAuthor":false,"prefix":"","firstName":"Yücel","middleName":"","lastName":"Başpınar","suffix":""},{"id":489530414,"identity":"a53bb90b-5fdb-408e-a6b8-26f1ccdd687c","order_by":3,"name":"Umit Kara","email":"","orcid":"","institution":"Suleyman Demirel University","correspondingAuthor":false,"prefix":"","firstName":"Umit","middleName":"","lastName":"Kara","suffix":""},{"id":489530415,"identity":"d95d8ee3-253f-41e9-b06a-b806e97db675","order_by":4,"name":"Ozge Kilicoglu","email":"","orcid":"","institution":"Marmara University","correspondingAuthor":false,"prefix":"","firstName":"Ozge","middleName":"","lastName":"Kilicoglu","suffix":""}],"badges":[],"createdAt":"2025-07-21 12:06:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7177199/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7177199/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87895560,"identity":"6706c26a-a431-420c-8f3e-3f066d5aa08c","added_by":"auto","created_at":"2025-07-30 07:29:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":464516,"visible":true,"origin":"","legend":"\u003cp\u003eWhole body images from Tc-99m-IMT (A) and Tc-99m- IMT-NSLC (B) administered to healthy mice at 0, 1, and 2 hours.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7177199/v1/3f1b45c4810f9e421887b0c4.png"},{"id":87895561,"identity":"2e88175c-88f6-4249-8c4e-f79ca8fecf6f","added_by":"auto","created_at":"2025-07-30 07:29:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":489452,"visible":true,"origin":"","legend":"\u003cp\u003eWhole body images from Tc-99m-IMT (A) and Tc-99m- IMT-NSLC (B) administered to colon cancer-induced mice at 0, 1, and 2 hours.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7177199/v1/e6a306be55796f5aca0b3ed8.png"},{"id":87895957,"identity":"58eb5b5d-cae9-4588-a858-7fdff8f2cf58","added_by":"auto","created_at":"2025-07-30 07:37:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":22564,"visible":true,"origin":"","legend":"\u003cp\u003ePharmacokinetic profile of Tc-99m-IMT solution\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7177199/v1/b278704eb2c06afb1e09bd65.png"},{"id":87895562,"identity":"dc1a9f24-7680-4edc-9c7b-e0d26817bc1f","added_by":"auto","created_at":"2025-07-30 07:29:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":25754,"visible":true,"origin":"","legend":"\u003cp\u003ePharmacokinetic profile of Tc-99m-IMT-NSLC\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7177199/v1/06e11500adf2d3b249d9c515.png"},{"id":87895565,"identity":"ab8aa047-19a6-4eaa-8179-fc1896b71fee","added_by":"auto","created_at":"2025-07-30 07:29:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":31950,"visible":true,"origin":"","legend":"\u003cp\u003eAmounts of radioactivity per gram of relevant organs of mice treated with Tc-99m-IMT solution.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7177199/v1/385f27d2fb996cc463661279.png"},{"id":87897182,"identity":"31db7da5-1838-4eca-8031-24b0d0ab4b34","added_by":"auto","created_at":"2025-07-30 07:45:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":23802,"visible":true,"origin":"","legend":"\u003cp\u003eAmounts of radioactivity per gram of relevant organs of mice treated with Tc-99m-IMT-NSLC.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7177199/v1/26f03f733f0feb7cdb961cf3.png"},{"id":87895958,"identity":"06784d08-879a-4367-b9b1-92c7699cc92a","added_by":"auto","created_at":"2025-07-30 07:37:27","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":47279,"visible":true,"origin":"","legend":"\u003cp\u003eMass attenuation coefficient for selected samples\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7177199/v1/ee1cbc1d0c103e17cce24bc3.png"},{"id":87895963,"identity":"54c52f32-55ab-487c-a503-c7a2a9351381","added_by":"auto","created_at":"2025-07-30 07:37:27","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":49100,"visible":true,"origin":"","legend":"\u003cp\u003eEffective atomic numbers for selected samples\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7177199/v1/a555bbebabf5733139838b10.png"},{"id":90077983,"identity":"c2b05d2a-230e-4958-b28e-15630fd369d5","added_by":"auto","created_at":"2025-08-28 08:21:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2243706,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7177199/v1/9b32e116-24b9-4bd7-8322-d85f5aa9f5df.pdf"}],"financialInterests":"","formattedTitle":"Dose distribution with Monte Carlo simulation, imaging, and pharmacokinetic studies of radiopharmaceutical for colon cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eColon cancer is one of the most common human malignancies in the world \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Even though clinical treatment for colon cancer has been improved with the development of new drugs and formulations, most colon cancer patients show limited response to targeted imaging \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Many current treatments do not provide curative or disease-controlling effects and are associated with clinical side effects, negatively impacting patients' quality of life \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Nanosystems represent the premier carriers of radionuclides, playing a crucial role in the efficient transport of these important materials \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e among FDA-approved radioimaging. Apart from the high production cost of nanosystems, many radioisotopes imaging fail to exert the desired localization. Recurrent tumor imaging is influenced by a multifactorial interplay of underlying mechanisms, including genomic instability, adaptive cellular responses, the bystander effect, intratumoral cellular heterogeneity, and inherent radiation resistance.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. As a result, tumors that are not fully eradicated facilitate the selection and growth of the most resilient tumor cell clones.\u003c/p\u003e\u003cp\u003eSimulation techniques are indispensable in radiopharmaceuticals, particularly in dosimetry and treatment planning for radionuclide therapies. These techniques employ advanced computational models to simulate the behavior of radionuclides within the body, assisting in treatment optimization and assessing radiation doses delivered to target tissues and organs \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In radiopharmaceutical therapy, simulations determine dose distributions within healthy tissues. These simulations consider various factors, like energy deposition and radiation transport, to ensure accurate and effective treatment planning \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Monte Carlo simulations are pivotal in accurately predicting absorbed doses in both tumors and critical organs, underscoring their significance in precise dosimetry calculations for radionuclide therapy \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe energy-dependent mass attenuation coefficient (MAC) (\u0026micro;/ρ/) is a crucial parameter in radiological physics. It represents the probability of interaction per unit path length per unit mass of the material (cm\u0026sup2;/g). MAC measures how much a particular material can attenuate a beam of radiation, such as X-rays or gamma rays, as it passes through that material.\u003c/p\u003e\u003cp\u003eThe small intestine organ is interested in medical physics, particularly diagnostic radiology, nuclear medicine, and radiation therapy. Understanding how photons interact with matter is crucial in various medical contexts. The MAC (\u0026micro;/ρ) is a critical factor in measuring a material's ability to reduce the intensity of a photon beam as it passes through it. Imatinib (IMT), used to treat \u0026hellip; cancer, has been widely studied for its pharmacokinetic and pharmacodynamic properties. A study by Gundogdu et al. used Tc-99m-IMT and Tc-99m radiolabeled nanostructured lipid carrier systems (Tc-99m-NSLC) to image experimental animals with colon cancer and healthy mice \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. While references provide valuable information about the MAC in various materials and contexts, none directly relate to its interaction with colon tissue. Therefore, this study aimed to understand colon tissue's specific MAC and its importance in medical imaging and radiation therapy for conditions like colorectal cancer. Knowing the MAC of colon tissue can help optimize nuclear imaging techniques such as SPECT/CT and PET/CT and minimize damage to the surrounding healthy tissue during radiation therapy. Investigating colon tissue's specific MAC and its variations in different pathological conditions can contribute significantly to developing more sensitive and targeted medical imaging and treatment strategies for colorectal cancer and other colon-related diseases.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Materials\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIMT was purchased from Roche (Basel, Switzerland), and all solvents and chemicals were obtained from Merck (Darmstadt, Germany).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Methods\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Preparation and characterization of radiolabeled nanostructured lipid carrier system\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe IMT-NSLC was prepared by using the emulsification\u0026ndash;sonication method and characterized as described by Gundogdu et al. 2022. Briefly, oleic acid (14.28%) and Gelucire 48/16 (28.57%), a gelucire derivative, were used as the lipid phase, and Span 80 (28.57%) was used as a surfactant. The lipid phase was melted using a water bath maintained at 70\u0026ndash;80\u0026deg;C, to which 1 mg of IMT was subsequently added. Meanwhile, an aqueous solution comprising a water and acetone-ethanol mixture along with Span 80 was prepared and heated to match the temperature of the lipid phase. The lipid and aqueous phases were blended at 10,000 rpm for 10 minutes using an Ultra-Turrax blender (IKA\u0026reg;-Werke GmbH \u0026amp; Co., Staufen, Germany) to create an emulsion. Subsequently, sonication was performed at 500 W and 20 kHz in alternating 20-second cycles for a total of 15 minutes with a Vibracell tip sonicator (IKA\u0026reg;-Werke GmbH \u0026amp; Co., Staufen, Germany), after which the mixture was allowed to cool to room temperature. After this, the IMT-NSLC was labeled with Tc-99m, and the radiolabeling conditions were outlined in our previous study (Gundogdu et al., 2020). The IMT-NSLC was characterized in terms of particle size, polydispersity index (PDI), and zeta potential (ZP) using Malvern Zetasizer (Malvern Nano ZS 90, Malvern, UK) with a particle size range of 3\u0026ndash;1000 nm and an angle of 173\u0026deg;. The samples were diluted using distilled water (pH\u0026thinsp;=\u0026thinsp;7). The zeta potential of IMT-NSLC was measured at 40 V/cm with a DTS 1060C zeta cuvette (Malvern, UK) at a temperature of 25\u0026deg;C, utilizing a conductivity of 5 mS/cm and a dielectric constant of 78.5. Measurements were conducted in triplicate, and the results are presented as mean values\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. In vivo studies were carried out using Tc-99m-IMT-NSLC and a Tc-99m-IMT solution.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 In Vivo animal studies\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAll studies were performed with 12 healthy and 12 colon cancer nude mice. The male colon cancer model was developed using nude mice (approximately 30 g), which were sourced from the Experimental Animals Laboratory Joint Stock Company. These mice were housed in standard plastic cages lined with white Pinus wood shavings for bedding, topped with stainless steel cover lids. All experiments were conducted following the Guide for the Care and Use of Laboratory Animals (NIH). The study protocol received approval from both the Institutional Review Board and the Animal Ethics Committee (2019\u0026ndash;106).\u003c/p\u003e\u003cp\u003eColon cancer induction in mice was performed using CRL-1739 (gastric adenocarcinoma cells). CRL-1739 cell suspension at a density of 1 x 108 in DMEM containing 50 \u0026micro;L fetal bovine serum and 450 \u0026micro;L 4.5 g/L glutamine was injected intraperitoneally into the animals' right or left peritoneal cavity. All cell suspension injection volumes were adjusted to 0.5 mL and administered one day apart. Tumor formation was completed in five weeks. The groups to be used in the in vivo study and their numbers are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGroups and numbers to be used in vivo study.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003egroups\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIMT solution radiolabeled\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTc-99m-IMT-NSLC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003econtrol group-\u003c/p\u003e\u003cp\u003ehealthy Balb-C-Nude mice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eexperimental group-\u003c/p\u003e\u003cp\u003eBalb-C-Nude mice with colon cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTc-99m-IMT- NSLC and Tc-99m-IMT solutions underwent in vivo whole-body imaging studies. CRL cells were injected into mice. All mice were anesthetized via intraperitoneal injection of a ketamine-xylazine solution. Following this, 7.4 MBq of Tc-99m-NLTS-IMT was injected into the mice through the tail vein. The anesthetized mice were then positioned horizontally beneath the SPECT/CT imaging system. Approximately one hour after the radiotracer injection, images were captured using a matrix size of 256 \u0026times; 256, with an energy window set at 20% at 140 keV. The same administration procedure was followed for the Tc-99m-IMT solution.\u003c/p\u003e\u003cp\u003eAfter administering the Tc-99m-IMT-NSLC and Tc-99m-IMT solution, blood samples were collected from healthy, cancerous mice at 0, 30, 60, 90, and 120 minutes. 200 \u0026micro;L blood samples were taken for the pharmacokinetic studies, and the radioactivity amount was determined with a gamma counter (Sesa Uniscaler I/S). Thus, a pharmacokinetic profile was created based on the radioactivity amounts obtained, and pharmacokinetic parameters (area under the curve (AUC), elimination rate constant (ke), and mean residence time (MRT)) were calculated with the Winnonline 5.3 program. Repeated Measure Analyses of Variance (RM-ANOVA) in the SAS program performed statistical evaluations for each pharmacokinetic parameter. The animals were sacrificed at the end of the imaging and pharmacokinetic studies, and biodistribution studies were conducted.\u003c/p\u003e\u003cp\u003eAfter the experimental animals were sacrificed, the large intestine, small intestine, kidney, liver, spleen, stomach, and heart tissues were removed. The gamma counter determined the amount of radioactivity in the weighted tissues, and the amount per gram was calculated. Target/non-target tissue uptake rates were evaluated.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 Monte Carlo Simulation\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe gamma-ray attenuation properties are examined in detail in the literature by study and simulation \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The MAC value is one of these crucial parameters for reducing radiation. The MACs for the small intestine were acquired over a range of photon energies from 0.015 MeV to 0.2 MeV. Two distinct sources were utilized to obtain these coefficients: the WinXCOM database and computational Monte Carlo Simulation performed using MATLAB software. Parallel to the WinXCOM data, Monte Carlo Simulation in MATLAB and technical computing was employed to calculate the MACs. The MATLAB-based approach involved numerical methods that potentially included the interpolation or integration of tabulated cross-sections from recognized databases, such as NIST XCOM. The algorithms were designed to ensure high precision and were validated against known benchmarks. The MATLAB simulations were based on a computational model that incorporated the physical principles governing the interaction of photons with matter, like the photoelectric effect, Compton scattering, and pair production. The model parameters were derived from established physical constants and interaction cross-sections provided by the National Institute of Standards and Technology (NIST) XCOM database.\u003c/p\u003e\u003cp\u003eThe Monte Carlo Simulation was developed to calculate the MACs, \u0026micro;/ρ, which represent the probability of photon attenuation per unit mass density of the tissue. These algorithms employed numerical integration techniques to solve the relevant equations, ensuring that the energy-dependent coefficients were computed accurately. MATLAB version (R2002b), structured to meet the standards of academic reporting for simulation-based research in medical physics, radiology, nuclear medicine, and radiotherapy, was used, and the Monte Carlo Simulation was employed to calculate and analyze the data. The tissues' theoretical MAC was computed using the WinXcom program and MATLAB code, applying the Beer-Lambert law.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.2\u003cem\u003e.4 Radiation Attenuation Parameters\u003c/em\u003e\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe linear attenuation coefficient (LAC) measures the decrease caused by interactions between high-energy photons released by a radioactive source and an absorber. This parameter is calculated using the Beer-Lambert law \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u0026micro; = -ln(I/I₀)/x\u003c/p\u003e\u003cp\u003eIn this equation, \"x\" represents the thickness of the absorber, whereas \"I0\" and \"I\" represent the initial and decreased photon intensities, respectively.\u003c/p\u003e\u003cp\u003eThe link between the LAC and the material's density is significant. The mixture rule helps validate the photon attenuation of the absorber based on the accuracy of the formulated materials:\u003c/p\u003e\u003cp\u003e\u0026micro;ₘ = \u0026micro;/ρ = \u0026sum;(w\u003csub\u003ei\u003c/sub\u003e(\u0026micro;/ρ)\u003csub\u003ei\u003c/sub\u003e) (2)\u003c/p\u003e\u003cp\u003eHere, \u0026micro;ₘ and ρ signify the MACs (cm\u0026sup2; g⁻\u0026sup1;)) and absorber density (g /cm\u003csup\u003e3\u003c/sup\u003e), respectively.\u003c/p\u003e\u003cp\u003eTo calculate the adequate atomic number of any composite, one can use the total atomic cross-section (tot) and the electronic cross-section (e) \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e:\u003c/p\u003e\u003cp\u003eZ_eff=(\u0026sum;_i▒〖f_i A_i (\u0026micro;\u0026frasl;ρ)_i 〗)/(\u0026sum;_j▒〖f_j A_j/Z_j (\u0026micro;\u0026frasl;ρ)_j 〗)\u003c/p\u003e\u003cp\u003eThis equation, f\u003csub\u003ei\u003c/sub\u003e, indicates the element's fractional abundance, and Ai and Zi are an element's atomic weight and atomic number, respectively.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Particle characterization of radiolabeled nanostructured lipid carrier system\u003c/h2\u003e\n \u003cp\u003eThe prepared IMT-NSLC had a particle size of 173,05\u0026thinsp;\u0026plusmn;\u0026thinsp;9 nm, a PDI of 0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1, and a ZP of -36.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 mV on the day of preparation. The IMT-NSLC was stored for 15 days at 5\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u0026deg;C, 25\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C (60% RH), and 40\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C (75% RH), respectively. The particle size increased from 173,05 nm to 203,35 nm after storage of 15 days at 5\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u0026deg;C, to 220,15 nm after storage of 15 days at 25\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C (60% RH), and to 202,44 nm after storage of 15 days at 40\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C (75% RH), respectively. The same tendency was observed for the PDI, namely an increase from 0.21 on the day of preparation to 0.27 after storage of 15 days at 5\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u0026deg;C, to 0.32 after storage of 15 days at 25\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C (60% RH) and to 0.28 after storage of 15 days at 40\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C (75% RH), respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 In vivo animal studies\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cstrong\u003eFigures 1 and 2\u003c/strong\u003e show centigrams obtained from imaging studies performed with Tc-99m-labeled IMT solution and Tc-99m-IMT-NSLC formulation in healthy and colon cancer-induced mice.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eTo precisely assess the uptake of both formulations in the cancerous region, digital tumor foci were delineated, and the radioactivity per pixel was calculated using the corresponding area values (ROI values). The findings of this analysis are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eROI values obtained in the colon cancer region for Tc-99m-IMT solution and Tc-99m-IMT-NSLC.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003cp\u003e(minute)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eROI value for Tc-99m-IMT solution in the colon cancer area\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eROI value for Tc-99m-IMT-NLTS in the colon cancer region\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e736\u0026thinsp;\u0026plusmn;\u0026thinsp;24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e821\u0026thinsp;\u0026plusmn;\u0026thinsp;98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1235\u0026thinsp;\u0026plusmn;\u0026thinsp;42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1192\u0026thinsp;\u0026plusmn;\u0026thinsp;52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4140\u0026thinsp;\u0026plusmn;\u0026thinsp;91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003ePharmacokinetic profiles of Tc-99m-IMT solution and Tc-99m-IMT-NSLC formulation are shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the pharmacokinetic parameters of Tc-99m-IMT solution and Tc-99m-IMT-NSLC formulation. Biodistribution studies have revealed the amounts of radioactivity per gram of the relevant organs shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePharmacokinetic parameters and statistical evaluation\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eapplication (cancerous mice)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003cp\u003e(cpm*minute)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003estandard deviation (sd)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTc-99m-IMT solution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTc-99m-IMT-NSLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eapplication (healthy mice)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(cpm*minute)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003esd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTc-99m-IMT solution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTc-99m-IMT-NSLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eapplication (cancerous mice)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eke (hour-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTc-99m-IMT solution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTc-99m-IMT-NSLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eapplication (healthy mice)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eke (hour-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTc-99m-IMT solution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTc-99m-IMT-NSLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eapplication (cancerous mice)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMRT (min)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTc-99m-IMT solution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTc-99m-IMT-NSLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eapplication (healthy mice)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMRT (min)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTc-99m-IMT solution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTc-99m-IMT-NSLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe amount of radioactivity per gram of the organs involved as a result of biodistribution studies\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRadioactivity quantities (cpm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTc-99m-IMT solution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTc-99m-IMT-NSLC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ehealthy mice\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emice with colon cancer\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ehealthy mice\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emice with colon cancer\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elarge intestine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e408\u0026thinsp;\u0026plusmn;\u0026thinsp;16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1182\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esmall intestine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekidney\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eliver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003espleen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003estomach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eheart\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e262\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eGamma counting was used to determine radioactivity levels per gram in both cancerous and healthy colon areas in mice, and target/non-target uptake ratios were calculated and presented in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTarget/non-target uptake ratios calculated by dividing the radioactivity values by each other (colon cancer area/healthy colon area)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRadiopharmaceutical\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRadioactivity of colon cancer site (cpm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRadioactivity of healthy colon site (cpm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eColon cancer site/Healthy colon site uptake ratio\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTc-99m-IMT solution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e264\u0026thinsp;\u0026plusmn;\u0026thinsp;24.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTc-99m-IMT-NSLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1003\u0026thinsp;\u0026plusmn;\u0026thinsp;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Monte Carlo Simulation\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe mean MACs exhibit close values in the small intestine and colon. For the small intestine, the coefficients are approximately 4.95183 cm\u0026sup2;/g for WinXCOM and 4.93718 cm\u0026sup2;/g for MATLAB. These averages are computed across different energy levels specified in our dataset, ranging from 0.015 MeV to 0.2 MeV, forming a comprehensive spectrum. Similarly, in the colon, mean MACs are approximately 4.00921 cm\u0026sup2;/g for WinXCOM and 4.071367 cm\u0026sup2;/g for MATLAB. These values, averaged across diverse energy levels, are presented in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e for various photon energies expressed in MeV. This energy range is particularly significant for medical diagnostic applications, notably in nuclear medicine, where low-energy photons find common use.\u003c/p\u003e\n \u003cp\u003eThis spectrum includes low-energy photons, typically interacting through the photoelectric effect, and extends to higher-energy photons where Compton scattering is the predominant interaction. The MAC (\u0026micro;/\u0026rho;) is a crucial parameter in radiological physics, representing the probability of interaction per unit path length per unit mass of the material and energy dependent. It is typically measured in cm\u0026sup2;/g. The values provided here are for the small intestine organ, which is of interest in medical physics and nuclear medicine. The data presented here are similar to the values obtained from WinXCOM and MATLAB, indicating a high level of detail and accuracy in our calculations. The data presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e showcase the percentage difference between the WinXCOM and MATLAB values, offering a valuable metric for assessing the consistency and accuracy of MATLAB calculations compared to the established WinXCOM standard. Most of these percentage differences reveal minimal deviation, strongly suggesting a close alignment between the MATLAB calculations and the data derived from WinXCOM. The values presented in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e are likely based on theoretical calculations or empirical data. This dataset effectively quantifies the relative disparities between the WinXCOM and MATLAB results. Notably, a low percentage deviation indicates a high level of agreement between these two calculation methods.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Radiation Attenuation Parameters\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe fluctuation of MAC WinXcom versus energy is seen in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, the MAC values for all samples display distinct characteristics that may be categorized into two energy regions: A substantial drop in MAC values is found at low energies (\u0026lt;\u0026thinsp;0.05 MeV) due to the prevalence of photoelectric effect at intermediate energies (from 0.05 to 0.2 MeV), there is a modest decrease in MAC values, which may be explained by the dominance of incoherent (Compton) scattering in this energy range. Aside from incident photon energy, the chemical composition of a material affects the change of MAC \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Consequently, our findings show that as Ti concentration increases, so do MAC values (all other constituent elements are low-Z materials, Z\u0026thinsp;\u0026le;\u0026thinsp;22).\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eZeff is a parameter used to characterize the radiation response of a multi-element material in numerous technical and medical applications and to determine the substitute for a material. Figure \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e depicts the relationship between adequate atomic number and energy.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe mass attenuation coefficients (cm2/g) values derived from MATLAB and WinXCOM for selected organs and their relative deviations.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eSmall intestine\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eColon\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eStomach\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMATLAB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWinXCOM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Dev\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMATLAB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWinXCOM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Dev\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMATLAB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWinXCOM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Dev\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe images (Figs.\u0026nbsp;1 and 2) obtained from SPECT scans are color-coded, with shades ranging from dark blue to red, indicating increased radioactivity from the former to the latter. The collected data reveal that the cancerous area exhibits more intense radioactivity compared to the Tc-99m-IMT solution within the first and second hours after the application of IMT-NSLC radiolabeled with Tc-99m.\u003c/p\u003e\u003cp\u003eThe amount of radioactivity of the Tc-99m-IMT-NSLC formulation in the colon cancer area was between 736.32\u0026thinsp;\u0026plusmn;\u0026thinsp;24.3 and 4139.71\u0026thinsp;\u0026plusmn;\u0026thinsp;130.83, while this value was between 151.69\u0026thinsp;\u0026plusmn;\u0026thinsp;9.67 and 1191.98\u0026thinsp;\u0026plusmn;\u0026thinsp;52.52 in the Tc-99m-IMT solution. It was determined that the Tc-99m-IMT-NSLC formulation was retained in higher amounts at the colon cancer site than the Tc-99m-IMT solution.\u003c/p\u003e\u003cp\u003eAccording to the study, healthy and colon cancer-induced mice showed higher blood radioactivity values after receiving the Tc-99m-IMT-NSLC formulation than the Tc-99m-IMT solution (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). It was noted that there was a significant difference in the blood radioactivity values of Tc-IMT-solution between healthy and cancerous animals (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). After administering Tc-99m-IMT, the radioactivity in the blood of cancerous mice was found to be higher for each period as compared to healthy mice. Furthermore, a statistically significant difference was observed in the blood radioactivity values of Tc-99m-IMT-NSLC formulation between healthy and cancerous animals (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). When Tc-99m-IMT-NSLC was administered, the radioactivity in the blood of healthy mice was higher at 30 and 60 minutes, while it was lower at 90 and 120 minutes compared to cancerous mice.\u003c/p\u003e\u003cp\u003eThe present study reports the results of pharmacokinetic and biodistribution studies (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003e) conducted on mice with cancer, aimed at comparing the bioavailability of two different formulations, namely, Tc-99m-IMT-NSLC and Tc-99m-IMT. The study evaluated several bioavailability parameters, including AUC, ke, and OCZ values, and the radioactivity amounts in various organs. The highest AUC and ke values were obtained following the administration of Tc-99m-IMT-NSLC to cancerous mice. Upon terminating the studies, mice were sacrificed, and their organs, namely the kidney, spleen, small intestine, large intestine, heart, liver, and stomach, were removed and weighed. The radioactivity amounts in these organs were subsequently determined using a gamma counter, and the amount of radioactivity per gram was calculated. The results of the biodistribution studies indicated that the Tc-99m-labeled NSLC was more effective than the Tc-99m-IMT formulation in healthy and cancerous mice. The radioactivity amounts per gram of the organs are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e with their standard deviations and in histograms in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003e. In summary, the results of the present study suggest that Tc-99m-IMT-NSLC is a more adequate formulation than Tc-99m-IMT in both healthy and cancerous mice, as evidenced by the higher AUC and ke values, and the higher radioactivity amounts in various organs. These findings have significant implications for developing more effective and targeted cancer treatments.\u003c/p\u003e\u003cp\u003eRadioactivity levels were higher in the large intestines of healthy and cancerous mice treated with Tc-99m-IMT solution and Tc-99m-IMT-NSLC. In healthy mice treated with Tc-99m-IMT solution, the amounts of radioactivity per gram of organs were highest in the large intestine, followed by the small intestine, spleen, kidney, liver, stomach, and heart. Similarly, in cancerous mice treated with Tc-99m-IMT solution, the large intestine showed the highest radioactivity levels per gram, followed by the spleen, small intestine, kidney, stomach, liver, and heart. When healthy mice were administered Tc-99m-IMT-NSLC, the large intestine had the highest levels of radioactivity per gram, followed by the stomach, spleen, kidney, small intestine, liver, and heart. In cancerous mice administered Tc-99m-IMT-NSLC, the large intestine showed the highest radioactivity levels per gram, followed by the stomach, spleen, kidney, small intestine, liver, and heart.\u003c/p\u003e\u003cp\u003eThe radioactivity levels were measured in both the colon cancer and healthy colon regions during the Tc-99m-IMT-NSLC and Tc-99m-IMT solution applications. The results indicated a higher radioactivity in the colon cancerous region for both formulations. Specifically, Tc-99m-IMT-NSLC had an activity of 1002.61\u0026thinsp;\u0026plusmn;\u0026thinsp;33.98 cpm in the colon cancer region and 179.66\u0026thinsp;\u0026plusmn;\u0026thinsp;5.50 cpm in the healthy colon region. In contrast, the Tc-99m-IMT solution had an activity of 263.79\u0026thinsp;\u0026plusmn;\u0026thinsp;24.35 cpm in the colon cancer region and 144.02\u0026thinsp;\u0026plusmn;\u0026thinsp;8.23 cpm in the healthy colon region. Furthermore, Tc-99m-IMT-NSLC had a higher uptake ratio of the colon cancerous region to the healthy colon region than Tc-99m-IMT solution, with an uptake ratio of 5.58\u0026thinsp;\u0026plusmn;\u0026thinsp;3.02. Lastly, the percentage uptake of the formulations administered to mice with colon cancer was calculated by analyzing the amount of radioactivity in their organs. The percentage uptake was 69.53\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31% for the Tc-99m-labeled F9-IMT formulation and 42.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31% for the Tc-99m-IMT solution.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e compares MACs (\u0026micro;/ρ/) for the small intestine at various photon energy levels, measured in Mega-electron Volts (MeV). These coefficients are essential for how photons are attenuated by the small intestine tissue, which is significant in nuclear medicine, medical imaging, and radiation dosimetry. These values are calculated with MATLAB and WinXCOM, which provide photon cross-sections for scattering, photoelectric absorption, and pair production in various materials.\u003c/p\u003e\u003cp\u003eZeff appears to depend highly on photon energy on all the samples investigated. Because of the prominence of the photoelectric effect (interaction cross-section, σpe\u0026thinsp;~\u0026thinsp;E-7/2), the highest values of Zeff have been obtained at lower energies. The lowest values of Zeff are seen in the energy range where Compton scattering is important (interaction cross-section, σcomp\u0026thinsp;~\u0026thinsp;E-1). Zeff values are nearly constant in this energy area because they are independent of energy \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFurthermore, adequate atomic numbers vary depending on the range of constituent atomic numbers and their proportions in the material. This indicates that the higher the samples' Ti (highest-Z constituent) ratio, the higher the effective atomic number values.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAnimal studies were conducted to evaluate the effects of administering Tc-99m-IMT solution and Tc-99m-IMT-NSLC on colon cancer. Pharmacokinetic parameters such as AUC, MRT, and ke were calculated from blood samples collected from healthy and cancerous mice treated with these formulations. Additionally, biodistribution studies were conducted at the end of the imaging and pharmacokinetic studies. Radioactivity values from blood samples were determined using the Phoenix Winnonline 5.3 program in pharmacokinetic studies. The pharmacokinetic parameters AUC, ke, and MRT (Mean Residence Time\u003cb\u003e)\u003c/b\u003e values were analyzed using SAS's Repeated Measures Analysis of Variance (RM-ANOVA) program. The results indicated that the Tc-99m-IMT-NSLC had higher pharmacokinetic parameters than the Tc-99m-IMT formulation in healthy and cancerous mice. Furthermore, imaging studies revealed that the Tc-99m-IMT-NSLC formulation had the highest uptake ratio of colon cancerous region to healthy colon region, exceeding (Figs.\u0026nbsp;1 and 2). Biodistribution studies conducted after two-hour imaging showed that the Tc-99m-IMT-NSLC formulation was more retained in the colon cancerous region than the Tc-99m-IMT solution.\u003c/p\u003e\u003cp\u003eWhile the references provided valuable insights into the MAC in various materials and contexts, they did not directly address the specific interaction of the MAC with colon tissue. Therefore, there is a compelling need for further research, explicitly incorporating advanced techniques such as Monte Carlo simulation, to delve into the MAC of colon tissue. Investigating this aspect with sophisticated simulation methods will provide crucial information regarding its implications in medical imaging and radiation therapy. This targeted research can significantly contribute to advancing our understanding of the unique characteristics and applications of the mass attenuation coefficient in the context of colon tissue, fostering advancements in medical science and technology.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003eInstitutional Review Board Statement: The Institutional Review Board and the Animal Ethics Committee approved the study protocol (2019–106).\u003c/p\u003e\n\u003cp\u003eConflicts of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021;71(3):209-49. doi:10.3322/caac.21660\u003c/li\u003e\n \u003cli\u003eTolba MF. Revolutionizing the landscape of colorectal cancer treatment: the potential role of immune checkpoint inhibitors. Int J Cancer 2020;147(11):2996-3006. doi:10.1002/ijc.33056\u003c/li\u003e\n \u003cli\u003eSgouros G, Bodei L, McDevitt MR, Nedrow JR. Radiopharmaceutical therapy in cancer: clinical advances and challenges. Nat Rev Drug Discov 2020;19(9):589-608. doi:10.1038/s41573-020-0073-9\u003c/li\u003e\n \u003cli\u003eMaeda H. The 35th anniversary of the discovery of EPR effect: a new wave of nanomedicines for tumor-targeted drug delivery-personal remarks and future prospects. J Pers Med 2021;11(3):229. doi:10.3390/jpm11030229\u003c/li\u003e\n \u003cli\u003eSchirrmacher V. From chemotherapy to biological therapy: a review of novel concepts to reduce the side effects of systemic cancer treatment (Review). Int J Oncol 2019;54(2):407-19. doi:10.3892/ijo.2018.4661\u003c/li\u003e\n \u003cli\u003eMitchell MJ, Billingsley MM, Haley RM, Wechsler ME, Peppas NA, Langer R. Engineering precision nanoparticles for drug delivery. Nat Rev Drug Discov 2021;20(2):101-24. doi:10.1038/s41573-020-0090-8\u003c/li\u003e\n \u003cli\u003eLjungberg M, Celler A, Konijnenberg MW, Eckerman KF, Dewaraja YK, Sj\u0026ouml;green-Gleisner K. MIRD pamphlet No. 26: joint EANM/MIRD guidelines for quantitative 177Lu SPECT applied for dosimetry of radiopharmaceutical therapy. J Nucl Med 2016;57(1):151-62. doi:10.2967/jnumed.115.159012\u003c/li\u003e\n \u003cli\u003eLi WB, Bouvier-Capely C, Saldarriaga Vargas C, Andersson M, Madas B. Heterogeneity of dose distribution in normal tissues in case of radiopharmaceutical therapy with alpha-emitting radionuclides. Radiat Environ Biophys 2022;61(4):579-96. doi:10.1007/s00411-022-01000-5\u003c/li\u003e\n \u003cli\u003eRanjbar H, Ghannadi-Maragheh M, Bahrami-Samani A, Beiki D. Dosimetric evaluation of 153Sm-EDTMP, 177Lu-EDTMP and 166Ho-EDTMP for systemic radiation therapy: influence of type and energy of radiation and half-life of radionuclides. Radiat Phys Chem 2015;108:60-4. doi:10.1016/j.radphyschem.2014.11.015\u003c/li\u003e\n \u003cli\u003eGundogdu E, Demir E-S, Ekinci M, Ozgenc E, Ilem-Ozdemir D, Senyigit Z, et al. An innovative formulation based on nanostructured lipid carriers for imatinib delivery: pre-formulation, cellular uptake and cytotoxicity studies. Nanomaterials 2022;12(2):250. doi:10.3390/nano12020250\u003c/li\u003e\n \u003cli\u003eKilicoglu O, Mehmetcik H. Science mapping for radiation shielding research. Radiat Phys Chem 2021;189:109721. doi:10.1016/j.radphyschem.2021.109721\u003c/li\u003e\n \u003cli\u003eAbuzaid MM, Susoy G, Issa SAM, Elshami W, Kilicoglu O, Tekin HO. Relationship between melting-conditions and gamma shielding performance of fluoro-sulfo-phosphate (FPS) glass systems: a comparative investigation. Ceram Int 2020;46(10 Pt A):15255-69. doi:10.1016/j.ceramint.2020.03.065\u003c/li\u003e\n \u003cli\u003eKara U, Kilicoglu O, Ersoy S. Structural and gamma-ray attenuation coefficients of different OAD films for nuclear medicine applications. Radiat Phys Chem 2020;172:108785. doi:10.1016/j.radphyschem.2020.108785\u003c/li\u003e\n \u003cli\u003eAlothman MA, Alrowaili ZA, Al-Baradi AM, Kilicoglu O, Mutuwong C, Al-Buriahi MS. Elastic properties and radiation shielding ability of ZnO\u0026ndash;P2O5/B2O3 glass system. J Mater Sci Mater Electron 2021;32(14):19203-17. doi:10.1007/s10854-021-06442-z\u003c/li\u003e\n \u003cli\u003eKilicoglu O. Characterization of copper oxide and cobalt oxide substituted bioactive glasses for gamma and neutron shielding applications. Ceram Int 2019;45(17 Pt B):23619-31. doi:10.1016/j.ceramint.2019.08.073\u003c/li\u003e\n \u003cli\u003eTekin HO, Akman F, Issa SAM, Ka\u0026ccedil;al MR, Kilicoglu O, Polat H. Two-step investigation on fabrication and characterization of iron-reinforced novel composite materials for nuclear-radiation shielding applications. J Phys Chem Solids 2020;146:109604. doi:10.1016/j.jpcs.2020.109604\u003c/li\u003e\n \u003cli\u003eKilicoglu O, Kara U, Ozgenc E, Gundogdu E. Pre-clinic study of radiopharmaceutical for Covid-19 inactivation: dose distribution with Monte Carlo simulation. Appl Radiat Isot 2022;188:110364. doi:10.1016/j.apradiso.2022.110364\u003c/li\u003e\n \u003cli\u003eKilicoglu O, Sepay N, Ozgenc E, Gundogdu E, Kara U, Alomairy S, et al. Evaluation of F-18 FDG radiopharmaceuticals through molecular docking and radiation effects. Appl Radiat Isot 2023;191:110553. doi:10.1016/j.apradiso.2022.110553\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Monte Carlo simulation, Imatinib, Colon Cancer, nanocarriers, Tc-99m ","lastPublishedDoi":"10.21203/rs.3.rs-7177199/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7177199/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eColon cancer is a widespread form of cancer worldwide. According to FDA-approved radio imaging, nanosystems are the primary carriers of radionuclides. This study examines colon tissue's specific mass attenuation coefficient and its significance in medical imaging and radiation therapy for various conditions, including colorectal cancer.\u003c/p\u003e\u003cp\u003eUnderstanding the mass attenuation coefficient of colon tissue can help optimize nuclear imaging techniques like SPECT/CT and PET/CT and minimize damage to healthy tissue surrounding the affected area during radiation therapy. Studying colon tissue's specific mass attenuation coefficient and its variations in different pathological conditions can significantly contribute to developing more sensitive and targeted medical imaging and treatment strategies for colorectal cancer and other colon-related diseases.\u003c/p\u003e\u003cp\u003eIt was determined that the Tc-99m-IMT-NSLC formulation was retained in the colon cancer area in higher amounts than the Tc-99m-IMT solution. In addition, using Monte Carlo simulation, MATLAB and WinXCOM calculated how photons are attenuated by the small intestine tissue, which is essential in nuclear medicine, medical imaging, and radiation dosimetry.\u003c/p\u003e\u003cp\u003eThis targeted research can significantly contribute to an advanced understanding of the unique characteristics and applications of the mass attenuation coefficient in the context of colon tissue, fostering advancements in medical science and technology.\u003c/p\u003e","manuscriptTitle":"Dose distribution with Monte Carlo simulation, imaging, and pharmacokinetic studies of radiopharmaceutical for colon cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 07:29:22","doi":"10.21203/rs.3.rs-7177199/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4c62e076-2537-41e9-a03b-ef8ef18ec28e","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-28T08:13:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-30 07:29:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7177199","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7177199","identity":"rs-7177199","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}