Automated Nuclear Morphometry: A Deep Learning Approach for Prognostication in Canine Pulmonary Carcinoma to Enhance Reproducibility

The integration of deep learning-based tools into diagnostic workflows is increasingly prevalent due to their efficiency and reproducibility in various settings. We investigated the utility of automated nuclear morphometry for assessing nuclear pleomorphism (NP), a criterion of malignancy in the current grading system in canine pulmonary carcinoma (cPC), and its prognostic implications. We developed a deep learning-based algorithm for evaluating NP (variation in size, i.e., anisokaryosis and/or shape) using a segmentation model. Its performance was evaluated on 46 cPC cases with comprehensive follow-up data regarding its accuracy in nuclear segmentation and its prognostic ability. Its assessment of NP was compared to manual morphometry and established prognostic tests (pathologists’ NP estimates (n = 11), mitotic count, histological grading, and TNM-stage). The standard deviation (SD) of the nuclear area, indicative of anisokaryosis, exhibited good discriminatory ability for tumor-specific survival, with an area under the curve (AUC) of 0.80 and a hazard ratio (HR) of 3.38. The algorithm achieved values comparable to manual morphometry. In contrast, the pathologists’ estimates of anisokaryosis resulted in HR values ranging from 0.86 to 34.8, with slight inter-observer reproducibility (k = 0.204). Other conventional tests had no significant prognostic value in our study cohort. Fully automated morphometry promises a time-efficient and reproducible assessment of NP with a high prognostic value. Further refinement of the algorithm, particularly to address undersegmentation, and application to a larger study population are required

Suture length to wound length ratio in 175 small animal abdominal midline closures.

Experimental and human studies have reported the advantages of a suture length to wound length (SL:WL) ratio greater than 4:1 in midline abdominal closure. This is achieved when the tissue bite (TB) is equal to or larger than the stitch interval (SI). Although TB and SI values are recommended in some textbooks, SL:WL ratios are rarely reported in veterinary textbooks. Additionally, no clinical data regarding these parameters could be found in small animals. Therefore, the aim of this study was to evaluate the SL:WL ratio of midline laparotomy closure in dogs and cats performed by surgeons with different levels of expertise and to compare the findings with current textbook recommendations. Midline laparotomy incisions of 100 dogs and 75 cats were closed in continuous pattern by diplomates and residents of both the European College of Veterinary Surgeons (ECVS) and the European College of Animal Reproduction (ECAR). The mean SL:WL ratio was 2.5 ± 0.7:1. The surgeons´ level of experience and the species and body weights of the animals did not have any significant influence on the SL:WL ratio. A moderate negative correlation was observed between the mean SI to mean TB (SI:TB) ratio and the SL:WL ratio. In this study, the mean SI matched the textbook recommendations both in feline and canine species, whereas the TB in cats was different. In this study, the SL:WL ratio was less than 4:1 without apparent complications. Because of the low prevalence of incisional hernia in dogs and cats larger studies are necessary to evaluate clinical significance of the presented data

Suture length to wound length ratio for simple continuous abdominal closures in veterinary surgery: An experimental in vitro study.

ObjectiveThis study aimed to investigate the suture length to wound length ratio (SL:WL) in an in vitro model of abdominal wall closure. Effects of the surgeon's experience level on the SL:WL ratio were evaluated, hypothesizing that small animal surgeons do not spontaneously apply SL:WL ratios equal to or larger than 4:1.ProceduresThree groups of surgeons with varying levels of experience performed 4 simple continuous sutures before (3 sutures) and after (1 suture) being educated on principles of the SL:WL ratio. All sutures were evaluated for their gaping, number of stitches, stitch intervals, tissue bite size and suture length.ResultsNo significant differences in suture parameters or SL:WL ratios were found among the 3 groups, and 60.5% of control sutures and 77.0% of test sutures had SL:WL ratios above 4:1. There was a significant improvement in the mean ratio after the information was provided (p = 0.003). Overall, the SL:WL ratios ranged from 1.54:1 to 6.81:1, with 36.3% falling between 4:1 and 5:1 (5.17 mm mean stitch interval, 5.52 mm mean tissue bite size). A significant negative correlation was observed between the SL:WL ratio and the stitch interval to tissue bite ratio (r = -0.886). Forty-nine of 120 sutures fulfilled the current recommendations for abdominal wall closure with a mean SL:WL ratio of 4.1:1.ConclusionA SL:WL ratio larger than 4:1 was achieved in 60% of the control sutures and in 77% of test sutures. Additional animal studies are necessary to evaluate the SL/WL ratio in small animal surgery

Automated Nuclear Morphometry: A Deep Learning Approach for Prognostication in Canine Pulmonary Carcinoma to Enhance Reproducibility

The integration of deep learning-based tools into diagnostic workflows is increasingly prevalent due to their efficiency and reproducibility in various settings. We investigated the utility of automated nuclear morphometry for assessing nuclear pleomorphism (NP), a criterion of malignancy in the current grading system in canine pulmonary carcinoma (cPC), and its prognostic implications. We developed a deep learning-based algorithm for evaluating NP (variation in size, i.e., anisokaryosis and/or shape) using a segmentation model. Its performance was evaluated on 46 cPC cases with comprehensive follow-up data regarding its accuracy in nuclear segmentation and its prognostic ability. Its assessment of NP was compared to manual morphometry and established prognostic tests (pathologists’ NP estimates (n = 11), mitotic count, histological grading, and TNM-stage). The standard deviation (SD) of the nuclear area, indicative of anisokaryosis, exhibited good discriminatory ability for tumor-specific survival, with an area under the curve (AUC) of 0.80 and a hazard ratio (HR) of 3.38. The algorithm achieved values comparable to manual morphometry. In contrast, the pathologists’ estimates of anisokaryosis resulted in HR values ranging from 0.86 to 34.8, with slight inter-observer reproducibility (k = 0.204). Other conventional tests had no significant prognostic value in our study cohort. Fully automated morphometry promises a time-efficient and reproducible assessment of NP with a high prognostic value. Further refinement of the algorithm, particularly to address undersegmentation, and application to a larger study population are required