Computed tomographic pneumogastrography in determining the types of gastric cancer according to the Lauren classification

Objective. To assess the capabilities of computed tomographic pneumogastrography in determining the types of gastric cancer according to the Lauren classification at the stage of clinical staging.Materials and methods. This study is a single-center retrospective study with 202 patients with gastric cancer included who was treated at the National Medical Research Center of Oncology named after N. N. Petrov from 2015 to 2018. All patients underwent subtotal gastric resection or gastrectomy and computed tomographic pneumogastrography at the stage of clinical staging. For patients undergoing neoadjuvant chemotherapy, CT was performed twice: before chemotherapy and after, immediately before surgery. We studied quantitative and qualitative imaging biomarkers, measured densitometric indices of stomach tumor density in the arterial, portal and delayed phases of scanning at five different points. For patients receiving NACT, all density indices were recorded twice — both before the start of therapeutic treatment, and after, immediately before the surgery.Results. The distribution of gastric cancer types according to Lauren»s classification was as follows: in 59 (29,2 %) intestinal type, 69 (34,2 %) — diffuse, 16 (7,9 %) — mixed, 58 (28,7 %) — indeterminate type. Based on visual characteristics, taking into account the characteristics of tumor growth, 3 main CT-PGG of the gastric cancer type were identified: 1 — tuberous (n = 68, 34,0 %), 2 — intramural (n = 57,3 %) and 3 — mixed (n = 77,4 %). CT-PGG tumor type is associated with Lauren type (χ2 = 185,19, p <0,001). With a tuberous CT-PGG type, it is possible to assume that the tumor is of an intestinal or indeterminate Lauren type; sensitivity 0,58 (95% CI: 0,49-0,67), specificity 0,1 (95% CI: 0,96-0,1). With an intramural CT-PGG type, the diffuse type of tumor according to Lauren is most likely; sensitivity 0,75 (95% CI: 0,64-0,85), specificity 0,96 (95% CI: 0,91-0,99). With a mixed CT-PGG type, the definition of the type according to Lauren is difficult. In the definition of mixed tumor type according to Lauren, the sensitivity and specificity of mixed CT-PGG tumor type are 0,94 (95% CI: 0,70% -0,1) and 0,67 (95% CI: 0,59-0,73) respectively.Conclusion. The shape of the stomach tumor, determined by CT-PGG, has a high diagnostic efficiency in determining the types of gastric cancer according to Lauren. The tuberous CT-PGG type is typical for tumors of the intestinal type according to Lauren, and the intramural CT-PGG type is typical for tumors of the diffuse type according to Lauren. Tumors of indeterminate Lauren type have any CT-PGG type and contrast pattern. For tumors of a mixed type according to Lauren, a mixed type according to CT-PGG is characteristic, but differential diagnosis with tumors of a tuberous and diffuse type according to Lauren of an atypical form for them is impossible. Tumors of the intestinal and diffuse Lauren type of the CT-PGG type, which is not typical for them, have an atypical contrast pattern.

Computational assessment of stomach tumor volume from multi-slice computerized tomography images in presence of type 2 cancer

Background: The multi–slice computerized tomography (MSCT) is a medical imaging modality that has been used to determine the size and location of the stomach cancer. Additionally, MSCT is considered the best modality for the staging of gastric cancer. One way to assess the type 2 cancer of stomach is by detecting the pathological structure with an image segmentation approach. The tumor segmentation of MSCT gastric cancer images enables the diagnosis of the disease condition, for a given patient, without using an invasive method as surgical intervention. Methods: This approach consists of three stages. The initial stage, an image enhancement, consists of a method for correcting non homogeneities present in the background of MSCT images. Then, a segmentation stage using a clustering method allows to obtain the adenocarcinoma morphology. In the third stage, the pathology region is reconstructed and then visualized with a three–dimensional (3–D) computer graphics procedure based on marching cubes algorithm. In order to validate the segmentations, the Dice score is used as a metric function useful for comparing the segmentations obtained using the proposed method with respect to ground truth volumes traced by a clinician. Results: A total of 8 datasets available for patients diagnosed, from the cancer data collection of the project, Cancer Genome Atlas Stomach Adenocarcinoma (TCGASTAD) is considered in this research. The volume of the type 2 stomach tumor is estimated from the 3–D shape computationally segmented from the each dataset. These 3–D shapes are computationally reconstructed and then used to assess the morphopathology macroscopic features of this cancer. Conclusions: The segmentations obtained are useful for assessing qualitatively and quantitatively the stomach type 2 cancer. In addition, this type of segmentation allows the development of computational models that allow the planning of virtual surgical processes related to type 2 cancer.

Computational assessment of stomach tumor volume from multi-slice computerized tomography images in presence of type 2 cancer

Background: The multi–slice computerized tomography (MSCT) is a medical imaging modality that has been used to determine the size and location of the stomach cancer. Additionally, MSCT is considered the best modality for the staging of gastric cancer. One way to assess the type 2 cancer of stomach is by detecting the pathological structure with an image segmentation approach. The tumor segmentation of MSCT gastric cancer images enables the diagnosis of the disease condition, for a given patient, without using an invasive method as surgical intervention. Methods: This approach consists of three stages. The initial stage, an image enhancement, consists of a method for correcting non homogeneities present in the background of MSCT images. Then, a segmentation stage using a clustering method allows to obtain the adenocarcinoma morphology. In the third stage, the pathology region is reconstructed and then visualized with a three–dimensional (3–D) computer graphics procedure based on marching cubes algorithm. In order to validate the segmentations, the Dice score is used as a metric function useful for comparing the segmentations obtained using the proposed method with respect to ground truth volumes traced by a clinician. Results: A total of 8 datasets available for patients diagnosed, from the cancer data collection of the project, Cancer Genome Atlas Stomach Adenocarcinoma (TCGASTAD) is considered in this research. The volume of the type 2 stomach tumor is estimated from the 3–D shape computationally segmented from the each dataset. These 3–D shapes are computationally reconstructed and then used to assess the morphopathology macroscopic features of this cancer. Conclusions: The segmentations obtained are useful for assessing qualitatively and quantitatively the stomach type 2 cancer. In addition, this type of segmentation allows the development of computational models that allow the planning of virtual surgical processes related to type 2 cancer.

Disability and Genetic Modification

This chapter examines the theme of disability, including attitudes toward disability and ideas about deviations from the bodily norm, that Bujold explores in her fictional work. While disability is rarely treated in science fiction and fantasy, it is ubiquitous in Bujold's work. Most visible is Miles Vorkosigan himself, whose fetus was damaged by an insurgent's attack and who struggles with his brittle bones and other problems throughout the early decades of his life. But to Miles can be added many other characters whose physical or mental disabilities are a crucial part of the narrative, from the brain-damaged Dubauer in Shards of Honor to the one-handed Dag in the Sharing Knife sequence, and Cazaril, with a mutilated hand and a demonic stomach tumor, in the first Chalion book. Bujold has declared that she was never writing books about issues: they are about character. The disabilities with which her characters have to cope “do not comprise the sums of their characters nor the reasons for their existences, but are just plot-things that happen to them and with which they must deal, daily or otherwise,” and she adds that the letters she gets from disabled readers suggest that they prefer that approach.