This study introduces a novel diagnostic approach for the prediction of endometrial cancer risk, which involves minimally invasive exfoliation cytology specimens from 63 participants, staining with the viscosity-sensitive fluorescent probe DCVJ, and detection by fluorescence lifetime imaging microscopy (FLIM). Initially, DCVJ-stained tissue sections from cancerous and normal regions were utilized to confirm the elevated viscosity in cancerous lesions. Subsequently, DCVJ-stained aspirated cell samples were detected by FLIM to capture viscosity characteristics and cellular morphology. A total of 2348 FLIM images collected from 42 individuals, were utilized for training purposes. A deep learning model, incorporating confidence learning, was developed and demonstrated a sensitivity of 84.6% and specificity of 75.0% in predicting endometrial cancer risk. This study highlights the potential of the deep learning-FLIM model for the prediction of endometrial cancer risk.

Two-photon photodynamic therapy (TP-PDT) offers an innovative approach to cancer treatment that utilizes near-infrared light to activate photosensitizers and generate reactive oxygen species (ROS) for targeted cancer cell elimination. TiO2-CUR-Sofast (TCS), which uses TiO2 nanoparticles and Sofast cationic polymer to modify curcumin (CUR), has demonstrated potential as a photosensitizer under visible light irradiation, addressing the limitations of CUR's narrow spectral range and low bioavailability. This study explores the utility of the two-photon technique to activate TCS within the infrared spectrum, aiming to enhance ROS production and penetration depth compared to traditional CUR. TCS exhibits a significantly higher ROS production at 900 nm excitation wavelength, approximately 6-7 times that of CUR, signifying a substantial increase in efficiency. In TP-PDT, TCS showed significant phototoxicity against HeLa and T24 cell lines compared to CUR. Furthermore, TCS's photodynamic efficacy is further confirmed by cell apoptosis and necrosis studies, where approximately 89 % of cells treated with TCS under 900 nm light irradiation were observed in an apoptosis/necrosis state. And the TP-PDT effect in deep tissue was simulated using pig skin. It shows that the two-photon excitation has a significant penetration depth advantage over the single-photon excitation. These results indicate that the two-photon PDT scheme of TCS has greater potential than the single-photon PDT scheme in the treatment of cancer, and provides an experimental foundation for the effective treatment of deep lesions.

The precise determination of surgical margins is essential for the management of multifocal cutaneous cancers, including extramammary Paget’s disease. This study introduces a novel strategy for precise margin identification in such tumors, employing multichannel autofluorescence lifetime decay (MALD), fluorescence lifetime imaging microscopy (FLIM), and machine learning, including confidence learning algorithms. Using FLIM, 51 unstained frozen sections were analyzed, of which 13 (25%) sections, containing 5003 FLIM patches, were used for training the residual network model (ResNet–FLIM). The remaining 38 (75%) sections, including 16 918 patches, were retained for external validation. Application of confidence learning with deep learning reduced the reliance on extensive pathologist annotation. Refined labels obtained by ResNet–FLIM were then incorporated into a support vector machine (SVM) model, which utilized fiber-optic-based MALD data. Both models exhibited substantial agreement with the pathological assessments. Of the 35 MALD-measured tissue segments, six (17%) segments were selected as the training dataset, including 900 decay profiles. The remaining 29 segments (83%), including 2406 decay profiles, were reserved for external validation. The ResNet–FLIM model achieved 100% sensitivity and specificity. The SVM–MALD model demonstrated 94% sensitivity and 83% specificity. Notably, fiber-optic-MALD allows assessing 12 sites per patient and delivering predictions within 10 min. Variations in the necessary safe margin length were observed among patients, highlighting the necessity for patient-specific approaches to determine surgical margins. This innovative approach holds potential for wide clinical application, providing a rapid and accurate margin evaluation method that significantly reduces a pathologist’s workload and improves patient outcomes through personalized medicine.