SummaryDespite a worldwide need to expand the lung donor pool, approximately 75% of lung offers are not accepted for transplantation. We investigated the impact of liberalizing lung donor acceptance criteria during the last decade on the number of effective transplants and early and late outcomes in our center. All 514 consecutive lung transplants (LTx) performed between Jan 2000 and Oct 2011 were included. Donors were classified as matching standard criteria (SCD; n = 159) or extended criteria (ECD; n = 272) in case they fulfilled at least one of the following criteria: age >55 years, PaO 2 /FiO 2 at PEEP 5 cmH 2 O < 300 mmHg at time of offer, presence of abnormalities on chest X-ray, smoking history, presence of aspiration, presence of chest trauma, or donation after circulatory death. Outcome parameters were primary graft dysfunction (PGD) grade at 0, 12, 24, and 48 h after LTx, time to extubation, stay in intensive care unit (ICU), early and late infection, acute rejection and bronchiolitis obliterans syndrome (BOS), and survival. Two hundred and seventy-two recipients (63.1%) received ECD lungs. PGD grade at T0 was similar between groups, while at T12 (<0.01), T24 (<0.01), and T48 (<0.05), PGD3 was observed more often in ECDs. ICU stay (P < 0.05) was longer in ECDs compared with SCDs. Time to extubation, respiratory infections, acute rejection, lymphocytic bronchiolitis, BOS, and survival were not different between groups. Accepting ECDs contributed in increasing the number of lung transplants performed in our center. Although this lung donor strategy has an impact on early postoperative outcome, liberalizing criteria did not influence long-term outcome after LTx.

The number of lymph node stations examined during VATS resections is similar to open resections for cStage-I NSCLC. Almost one-third of the patients with a central cStage-I NSCLC were upstaged to pN1. Tumour location was the only independent variable for pN1 upstaging in logistic regression analysis. It is a potential bias in retrospective studies and should therefore be accounted for when comparing different surgical resection techniques for cStage-I NSCLC.

By making a tunnel between the bronchovascular structures and parenchyma from anteriorly to posteriorly, one can open the fissure completely with staplers at an early stage of an anatomical lung resection. This combines the advantages of both the 'fissureless' hilum first technique and classic (open) fissure first dissection, i.e. minimal air leak and optimal anatomical overview before bronchovascular structures are divided, potentially avoiding inadvertent transections. A cohort of 198 consecutive patients operated with this alternative fissureless technique demonstrates the feasibility and non-inferiority regarding hospital stay, chest tube duration, operation time and complications in comparison with the hilum first technique.

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Objectives: within this investigation we investigated several approaches to enhance the positive predictive value (PPV) of chest digital tomosynthesis (DTS) in the lung cancer detection Methods: the investigation was carried out within the SOS clinical trial (NCT03645018) for lung cancer screening with DTS. Lung nodules were identified by visual analysis and then classified using the diameter and the radiological aspect of the nodule following a modified lung-RADS classification.Haralick texture features were extracted from the segmented nodules. Both semantic variables and radiomics features were used to build a predictive model using two approaches: logistic regression model on a sub-set of variables selected with backward feature selection or machine learning using the whole sub-set of variables. We used two machine learning methods: a Random Forest and a neural network. Machine learning methods were applied to a training set and validated on a test set. Methods were compared using diagnostic accuracy metrics.Results: binary visual analysis had a good sensitivity (0.95) but a low PPV (0.14). Lung-RADS classification increased the PPV (0.19) but with an unacceptable low sensitivity (0.65). Analogously, logistic regression showed a mildly increased PPV (0.22) and a low sensitivity (0.67). Random Forest demonstrated a low accuracy with a moderate PPV (0.40) but with a dramatically low sensitivity (0.30). Neural network demonstrated to be the best predictor with a nearly perfect PPV (0.95) and a high sensitivity (0.90).Conclusions: among the various technique to reduce the false positive rates of DTS the neural network demonstrated a very high PPV. The use of visual analysis along with neural network could help radiologists to depict a follow-up strategy after a positive DTS.