A self-attention generative adversarial network(SAGAN)is proposed to improve the accuracy of histological subtype prediction for lung cancer cases.After collecting and preprocessing the lung cancer image dataset and data augmentation,SAGAN model is trained,where the generator uses self-attention mechanism to strengthen feature extraction,while the discriminator optimizes the generation process.Experimental results show that SAGAN model achieves accuracies of 0.852 and 0.845 on the training and test sets,respectively,with recall rates of 0.833 and 0.829,outperforming the other models.Additionally,the narrow confidence intervals indicate the high stability of SAGAN model in classification.SAGAN is helpful for lung cancer image analysis,providing stronger support for clinical decision-making.

A self-attention generative adversarial network(SAGAN)is proposed to improve the accuracy of histological subtype prediction for lung cancer cases.After collecting and preprocessing the lung cancer image dataset and data augmentation,SAGAN model is trained,where the generator uses self-attention mechanism to strengthen feature extraction,while the discriminator optimizes the generation process.Experimental results show that SAGAN model achieves accuracies of 0.852 and 0.845 on the training and test sets,respectively,with recall rates of 0.833 and 0.829,outperforming the other models.Additionally,the narrow confidence intervals indicate the high stability of SAGAN model in classification.SAGAN is helpful for lung cancer image analysis,providing stronger support for clinical decision-making.

OBJECTIVEMeasures of ventilation-CO₂output relationship have been shown to be more prognostic than peak O₂uptake in assessing life expectancy in patients with chronic heart failure (CHF). Because both the ratios (VE/Vco₂) and slopes (VE-vs-Vco₂) of ventilation-co₂ output of differing durations can be used, we aim to ascertain which measurements best predicted CHF life expectancy.

METHODSTwo hundred and seventy-one CHF patients with NYHA class II-IV underwent incremental cardiopulmonary exercise testing (CPET) and were followed-up for a median duration of 479 days. Four different linear regression VE-vs- Vco₂ slopes were calculated from warm-up exercise onset to: 180 s, anaerobic threshold (AT), ventilatory compensation point (VCP); and peak exercise. Five VE/Vco₂ ratios were calculated for the following durations: rest (120 s), warm-up (30 s), AT (60 s), lowest value (90 s), and peak exercise (30 s). Death or heart transplant were considered end-points. Multiple statistical analyses were performed.

RESULTSCHF patients had high lowest VE/Vco₂ (41.0 ± 9.2, 141 ± 30%pred), high VE/Vco₂ at AT (42.5 ± 10.4, 145 ± 35%pred), and high VE-vs-Vco₂ slope to VCP (37.6 ± 12.1, 126 ± 41%pred). The best predictor of death was a higher lowest VE/Vco₂ (≥ 42, ≥ 141%pred), whereas the VE-vs-Vco₂slope to VCP was less variable than other slopes. For death prognosis in 6 months, %pred values were superior: for longer times, absolute values were superior.

CONCLUSIONThe increased lowest VE/Vco₂ ratio easily identifiable and simply measured during exercise, is the best measurement to assess the ventilation-co₂output relationship in prognosticating death in CHF patients.

Carbon Dioxide ; metabolism ; Chronic Disease ; Disease Progression ; Exercise Test ; Heart Failure ; diagnosis ; mortality ; physiopathology ; Humans ; Life Expectancy ; Respiratory Function Tests