BACKGROUND Argininosuccinate synthetase (ASS) was the first of two enzymes to convert citrulline to arginine. This pathway allowed cells to synthesize arginine from citrulline, making this amino acid nonessential for the growth of most mammalian cells. Previous studies demonstrated that several human tumor cell lines were auxotrophic for arginine due to an inability to express ASS. Selective elimination of arginine from the circulation of animals with these tumors is a potentially effective anticancer treatment. The purpose of these experiments was to determine the frequency of ASS deficiency and arginine auxotrophy in a variety of human malignant tumors. METHODS The authors analyzed the expression of ASS by immunohistochemistry with a monoclonal antibody in a variety of human tumor biopsies. They found that the incidence of ASS deficiency varied greatly with the tumor type and tissue of origin. RESULTS Melanoma, hepatocellular carcinoma, and prostate carcinoma were most frequently deficient in ASS. Some human cancers were almost always positive for ASS (e.g., lung and colon carcinomas). However, other human cancers, including sarcomas, invasive breast carcinoma, and renal cell carcinoma, also were sometimes ASS deficient. CONCLUSIONS These data indicated that immunohistochemical detection of ASS may prove an effective means for determining ASS deficiency in malignant human tumors and for identifying patients most likely to respond to arginine deprivation therapy. Based on these results, human clinical trials using arginine‐degrading enzyme therapy to treat patients with advanced melanoma or hepatocellular carcinoma have been initiated. Cancer 2004;100:826–33. © 2004 American Cancer Society.
T he accurate determination of a child's developmental status is required for proper treatment of various growth disorders (1) and scoliosis (2). Other parameters, such as height, weight, secondary sexual characteristics, chronologic age, and dental age, correlate with developmental status, but skeletal age has been considered the most reliable method (3-5). The standard of care for this assessment calls for radiologists to identify the reference standard in an atlas of hand radiographs that most closely resembles an anteroposterior or posteroanterior radiograph of the participant's left hand. The most common atlas used as a reference standard is the Radiographic Atlas of Skeletal Development of the Hand and Wrist, published in 1959 (6).As part of the process of implementing an artificial intelligence (AI) algorithm in clinical practice, it is critical to properly determine its effects. However, different study designs may yield different findings about the same assistive technologies. For example, the same commercially available computer-aided detection system for detecting pulmonary nodules on chest CT scans produced different findings in studies completed within a year of each other (7-9). Findings on potential computer-aided diagnosis Background: Previous studies suggest that use of artificial intelligence (AI) algorithms as diagnostic aids may improve the quality of skeletal age assessment, though these studies lack evidence from clinical practice.Purpose: To compare the accuracy and interpretation time of skeletal age assessment on hand radiograph examinations with and without the use of an AI algorithm as a diagnostic aid. Materials and Methods:In this prospective randomized controlled trial, the accuracy of skeletal age assessment on hand radiograph examinations was performed with (n = 792) and without (n = 739) the AI algorithm as a diagnostic aid. For examinations with the AI algorithm, the radiologist was shown the AI interpretation as part of their routine clinical work and was permitted to accept or modify it. Hand radiographs were interpreted by 93 radiologists from six centers. The primary efficacy outcome was the mean absolute difference between the skeletal age dictated into the radiologists' signed report and the average interpretation of a panel of four radiologists not using a diagnostic aid. The secondary outcome was the interpretation time. A linear mixed-effects regression model with random center-and radiologist-level effects was used to compare the two experimental groups.Results: Overall mean absolute difference was lower when radiologists used the AI algorithm compared with when they did not (5.36 months vs 5.95 months; P = .04). The proportions at which the absolute difference exceeded 12 months (9.3% vs 13.0%, P = .02) and 24 months (0.5% vs 1.8%, P = .02) were lower with the AI algorithm than without it. Median radiologist interpretation time was lower with the AI algorithm than without it (102 seconds vs 142 seconds, P = .001). Conclusion:Use of an artificial intelligence algorithm ...
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