Pilonidal sinus disease (PSD) is a suppurative inflammatory condition characterized by a sinus tract in the sacrococcygeal gluteal cleft and surrounding skin. Surgical resection is an important treatment for PSD; however, postoperative recurrence occurs in some patients, severely affecting their quality of life. Due to the lack of objective clinical indicators for predicting recurrence, developing an artificial intelligence-based prediction model is an effective approach. In this study, postoperative hematoxylin and eosin (H&E) stained pathological sections from PSD patients were used. The horizontal and vertical distance network (HoVer-Net) deep learning model for automated nuclei segmentation and classification was employed to extract nuclear features from lesion regions. These features were then integrated with multidimensional clinical characteristics to select key features. And then a machine learning classifier was incorporated to construct a recursive feature elimination-balanced random forest (RFE-BalancedRF) ensemble model to predict PSD recurrence. Experimental results demonstrate that the proposed PSD recurrence prediction model achieves an area under the curve of receiver operating characteristic (ROC_AUC) of 0.751 ± 0.048 and an accuracy (Acc) of 0.715 ± 0.066. Furthermore, a correlation exists between multimodal features and the risk of PSD recurrence, confirming the effectiveness of the proposed model in predicting PSD recurrence. This study innovatively integrates radiomics features with clinical characteristics from the dual perspectives of pathological image analysis and multimodal feature fusion to construct a PSD recurrence risk prediction model, potentially providing a new pathway with considerable clinical translation potential for artificial intelligence assisted medicine.
Objective To evaluate the value of methylated Septin9 (mSEPT9) in the diagnosis and postoperative recurrence/metastasis monitoring of colorectal cancer. Methods A total of 76 patients with colorectal cancer who were hospitalized in Beijing Anorectal Hospital (Beijing Erlonglu Hospital) and positive for mSEPT9 before operation from January to December in 2020 were collected. Nineteen patients who were still positive for mSEPT9 at one week after operation were selected as the msept9 positive group, and 57 patients whose mSEPT9 became negative were selected as the msept9 negative group. The clinicopathological features and postoperative recurrence and metastasis of the two groups were analyzed and compared. Results There were significant differences in lymph node metastasis rate, vascular or nerve infiltration and clinical staging between the mSEPT9 positive group and the mSEPT9 negative group (P=0.024, P=0.009, P=0.009). There was no significant difference in age, gender, tumor location, degree of tumor differentiation, and tumor invasion depth (T stage), P>0.05. Two patients in the mSEPT9 positive group had liver metastases after operation, and there were no cases of metastasis or recurrence in the mSEPT9 negative group (P=0.024). Conclusion The mSEPT9 can be used as a potential tumor marker for the diagnosis of colorectal cancer and the monitoring of postoperative treatment effect, and more attention should be paid to patients who are still positive for mSEPT9 after surgery.