Ovarian cancer is currently one of the most common cancers of the female reproductive organs, and its mortality rate is the highest among all types of gynecologic cancers. Rapid and accurate classification of ovarian cancer plays an important role in the determination of treatment plans and prognoses. Nevertheless, the most commonly used classification method is based on histopathological specimen examination, which is time‐consuming and labor‐intensive. Thus, in this study, we utilize radiomics feature extraction methods and the automated machine learning tree‐based pipeline optimization tool (TOPT) for analysis of 3D, second harmonic generation images of benign, malignant and normal human ovarian tissues, to develop a high‐efficiency computer‐aided diagnostic model. Area under the receiver operating characteristic curve values of 0.98, 0.96 and 0.94 were obtained, respectively, for the classification of the three tissue types. Furthermore, this approach can be readily applied to other related tissues and diseases, and has great potential for improving the efficiency of medical diagnostic processes.
Lots of SAR polarimetric features have been proposed to discriminate the different scattering processes of earth terrain. Using the full set of these features for classification is computationally too expensive and some of the features may be irrelevant to the classification task and other may be redundant. Thus, it is useful to exploit the discriminative power offered by a selection and combination of these features. Due to the resulting redundancy and the added computation complexity, an improved sparse support vector machine feature selection algorithm is presented to select a set of discriminative features for efficiently classifying crops by polarimetric SAR. We modify the original algorithm with a simple voting strategy, which extends the original binary-class problem into a multi-class issue. Meanwhile, it can automatically select a feature subset that is well suited for all classes. Experimental results show that the proposed feature selection algorithm can effectively select a good subset of features to discriminate different crops in polarimetric SAR images.
Gastric cancer, one of the most common malignant tumors that can affect the digestive system, poses a serious threat to human life. The survival rate of gastric cancer patients depends on early detection and treatment. The widespread adoption of endoscopy has improved the detection rate of early gastric cancer. Accurate preoperative diagnosis of early gastric cancer is key to developing individualized treatment strategies. Here, nonlinear optical microscopy (NLOM) is used to differentiate between normal gastric mucosae and those with early gastric cancer. Furthermore, the quantitative relationship between submucosal infiltration depth and collagen signals in early gastric cancer is explored. First, the two-dimensional collagen direction angle was measured as an indicator to identify cancerous tissue. The orientation indexes of collagen fibers in normal and cancerous tissues were found to be 0.8511 ± 0.0839 and 0.6466 ± 0.07429 (P < 0.0001), respectively, indicating a significant decrease for the cancerous site. The backscattered second harmonic generation (SHG) signal corresponding to the collagen content and the three-dimensional collagen fiber orientation were then studied for early gastric cancer at different infiltration depths. The backscattered collagen SHG signal (a.u.) in the infiltrated lamina propria, muscularis mucosa, and submucosa were found to be 0.1850 ± 0.0393, 0.0870 ± 0.0189, and 0.0435 ± 0.0163, respectively. The 3D directional variance of collagen corresponding to the three infiltration depth sites were 0.6108 ± 0.0707, 0.6794 ± 0.0610, and 0.8200 ± 0.0618 (P < 0.05), respectively. Significant differences between the early gastric cancer collagen signals at different infiltration depths were observed. Our results indicate that NLOM can differentiate cancerous tissue from normal tissue, and thus diagnose early gastric cancer based on infiltration depth. NLOM provides a new evaluation method for the real-time in situ diagnosis of early gastric cancer and has important clinical significance for preparing accurate individualized treatment guidance.
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