## 1 Introduction
### ROI Concept
The authors described the use of ROIs for the representative statistical analysis of quantitative results. Five ROIs (250 \(\upmu\)m \(\times\) 250 \(\upmu\)m) were designated over all tissue of the same type (mainly in the kidney and cerebellum), avoiding special structures such as vessels. Small special structures were analyzed separately, with adapted ROI sizes. The elemental content was calculated by averaging the content quantified in all ROIs of the same type. The main deposition of both elements was found in the middle coat of blood vessels in a ring-like shape, which was clearly visible in the published LA-ICP-MS images [55]. [PERSON] et al. [56] predefined ROIs in atherosclerotic plaque using an SR-\(\upmu\)XRF method. Then, the authors investigated the selected region by LA-ICP-MS toward Gd from contrast agents. The method of LA-ICP-MS achieved a better resolution and found small areas of high Gd content. [PERSON] et al. [57] also analyzed the atherosclerotic lesions and divided the obtained material into four ROIs: (1) atherosclerotic plaque; (2) connection between the atherosclerotic plaque and the arterial wall; (3) outer wall; and (4) the inner wall or fibrous cap on the atherosclerotic lesion. A photograph of the sample taken prior to ablation enabled drawing the ROIs with the imaging software tools. The boundaries of each ROI were delineated so that they did not touch each other and were placed in the central part of the sample. This approach for ROI extraction ensured the covering of only one type of region of the sample and avoiding overlapping with the plastic base. The connection between the atherosclerotic plaque and the arterial wall was described by the authors as a smooth transition between the arterial wall (representing soft tissue) and the atherosclerotic plaque. In some cases, two areas were treated as a single ROI, when it was possible to find them in two different localizations (e.g., the connection between the atherosclerotic plaque and the arterial wall). ROIs could be well distinguished due to the sharp boundaries of high signal intensities for selected elements in plaque and for other elements in the arterial walls [57]. The term \"ROI\" in LA-ICP-MS measurements can be found also in [PERSON] et al. 's two studies published in 2013 [58; 59]. The authors described ROIs as \"freely drawn\" and calculated the average content of trace metals separately for each region; in this case, of a human liver. One of the directions of LA-ICP-MS development is designing software which would simplify handling LA-ICP-MS data and, at the same time, allow for easy ROI extraction. [PERSON] et al. [60] proposed dedicated software based on Python programming language that uses more advanced segmentation algorithms for ROI classifications. In 2011, [PERSON] et al. [61], while presenting their new software (IMAGENA), proposed the identification of free-hand-drawn ROIs, including the glass or other blank background outside the sample and areas based on anatomical criteria inside. [PERSON] et al. [62] proposed dedicated software, FishImager, to assign a biological feature as an ROI and overlay it with a quantitative LA-ICP-MS image. Phenotypic features were identified with the corresponding software, FishInspector, based on a microscopic image and automatically defined as biological ROIs. For each ROI, descriptive statistics such as the sum, mean, standard deviation, median, variance, minimum, and maximum were calculated. Different cluster algorithms could also be applied to the LA-ICP-MS data; then, cluster data could be correlated with the ROIs. Combination of biological ROIs with the cluster results was very important, as it enabled the move from visual observations and interpretations towards validation and statistical proof.