Ex vivo study of Ho:YAG and thulium fiber lasers for soft tissue surgery: which laser for which case?

The goal of this study was to assess the ablation, coagulation, and carbonization characteristics of the holmium:YAG (Ho:YAG) laser and thulium fiber lasers (TFL). The Ho:YAG laser (100 W av.power), the quasi-continuous (QCW) TFL (120 W av.power), and the SuperPulsed (SP) TFL (50 W av.power) were compared on a non-frozen porcine kidney. To control the cutting speed (2 or 5 mm/s), an XY translation stage was used. The Ho:YAG was tested using E = 1.5 J and Pav = 40 W or Pav = 70 W settings. The TFL was tested using E = 1.5 J and Pav = 30 W or Pav = 60 W settings. After ex vivo incision, histological analysis was performed in order to estimate thermal damage. At 40 W, the Ho:YAG displayed a shallower cutting at 2 and 5 mm/s (1.1 ± 0.2 mm and 0.5 ± 0.2 mm, respectively) with virtually zero coagulation. While at 70 W, the minimal coagulation depth measured 0.1 ± 0.1 mm. The incisions demonstrated zero carbonization. Both the QCW and SP TFL did show effective cutting at all speeds (2.1 ± 0.2 mm and 1.3 ± 0.2 mm, respectively, at 30 W) with prominent coagulation (0.6 ± 0.1 mm and 0.4 ± 0.1 mm, respectively, at 70 W) and carbonization. Our study introduced the TFL as a novel efficient alternative for soft tissue surgery to the Ho:YAG laser. The SP TFL offers a Ho:YAG-like incision, while QCW TFL allows for fast, deep, and precise cutting with increased carbonization.

Prediction of Clinical Outcomes with Explainable Artificial Intelligence in Patients with Chronic Lymphocytic Leukemia.

BACKGROUND: The International Prognostic Index (IPI) is applied to predict the outcome of chronic lymphocytic leukemia (CLL) with five prognostic factors, including genetic analysis. We investigated whether multiparameter flow cytometry (MPFC) data of CLL samples could predict the outcome by methods of explainable artificial intelligence (XAI). Further, XAI should explain the results based on distinctive cell populations in MPFC dot plots. METHODS: We analyzed MPFC data from the peripheral blood of 157 patients with CLL. The ALPODS XAI algorithm was used to identify cell populations that were predictive of inferior outcomes (death, failure of first-line treatment). The diagnostic ability of each XAI population was evaluated with receiver operating characteristic (ROC) curves. RESULTS: ALPODS defined 17 populations with higher ability than the CLL-IPI to classify clinical outcomes (ROC: area under curve (AUC) 0.95 vs. 0.78). The best single classifier was an XAI population consisting of CD4+ T cells (AUC 0.78; 95% CI 0.70-0.86; p < 0.0001). Patients with low CD4+ T cells had an inferior outcome. The addition of the CD4+ T-cell population enhanced the predictive ability of the CLL-IPI (AUC 0.83; 95% CI 0.77-0.90; p < 0.0001). CONCLUSIONS: The ALPODS XAI algorithm detected highly predictive cell populations in CLL that may be able to refine conventional prognostic scores such as IPI.