Artificial intelligence in lung cancer: current applications and perspectives.

Artificial intelligence (AI) has been a very active research topic over the last years and thoracic imaging has particularly benefited from the development of AI and in particular deep learning. We have now entered a phase of adopting AI into clinical practice. The objective of this article was to review the current applications and perspectives of AI in thoracic oncology. For pulmonary nodule detection, computer-aided detection (CADe) tools have been commercially available since the early 2000s. The more recent rise of deep learning and the availability of large annotated lung nodule datasets have allowed the development of new CADe tools with fewer false-positive results per examination. Classical machine learning and deep-learning methods were also used for pulmonary nodule segmentation allowing nodule volumetry and pulmonary nodule characterization. For pulmonary nodule characterization, radiomics and deep-learning approaches were used. Data from the National Lung Cancer Screening Trial (NLST) allowed the development of several computer-aided diagnostic (CADx) tools for diagnosing lung cancer on chest computed tomography. Finally, AI has been used as a means to perform virtual biopsies and to predict response to treatment or survival. Thus, many detection, characterization and stratification tools have been proposed, some of which are commercially available.

Using chest CT scan and unsupervised machine learning for predicting and evaluating response to lumacaftor-ivacaftor in people with cystic fibrosis.

OBJECTIVES: Lumacaftor-ivacaftor is a cystic fibrosis transmembrane conductance regulator (CFTR) modulator known to improve clinical status in people with cystic fibrosis (CF). This study aimed to assess lung structural changes after one year of lumacaftor-ivacaftor treatment, and to use unsupervised machine learning to identify morphological phenotypes of lung disease that are associated with response to lumacaftor-ivacaftor. METHODS: Adolescents and adults with CF from the French multicenter real-world prospective observational study evaluating the first year of treatment with lumacaftor-ivacaftor were included if they had pretherapeutic and follow-up chest computed tomography (CT)-scans available. CT scans were visually scored using a modified Bhalla score. A k-mean clustering method was performed based on 120 radiomics features extracted from unenhanced pretherapeutic chest CT scans. RESULTS: A total of 283 patients were included. The Bhalla score significantly decreased after 1 year of lumacaftor-ivacaftor (-1.40±1.53 points compared with pretherapeutic CT; p<0.001). This finding was related to a significant decrease in mucus plugging (-0.35±0.62 points; p<0.001), bronchial wall thickening (-0.24±0.52 points; p<0.001) and parenchymal consolidations (-0.23±0.51 points; p<0.001). Cluster analysis identified 3 morphological clusters. Patients from cluster C were more likely to experience an increase in percent predicted forced expiratory volume in 1 sec (ppFEV1) ≥5 under lumacaftor-ivacaftor than those in the other clusters (54% of responders versus 32% and 33%; p=0.01). CONCLUSION: One year treatment with lumacaftor-ivacaftor was associated with a significant visual improvement of bronchial disease on chest CT. Radiomics features on pretherapeutic CT scan may help in predicting lung function response under lumacaftor-ivacaftor.

Total Psoas Area and Total Muscular Parietal Area Affect Long-Term Survival of Patients Undergoing Pneumonectomy for Non-Small Cell Lung Cancer.

There is no standardization in methods to assess sarcopenia; in particular the prognostic significance of muscular fatty infiltration in lung cancer patients undergoing surgery has not been evaluated so far. We thus performed several computed tomography (CT)-based morphometric measurements of sarcopenia in 238 consecutive non-small cell lung-cancer patients undergoing pneumonectomy from 1 January 2007 to 31 December 2015. Sarcopenia was assessed by the following CT-based parameters: cross-sectional total psoas area (TPA), cross-sectional total muscle area (TMA), and total parietal muscle area (TPMA), defined as TMA without TPA. Measures were performed at the level of the third lumbar vertebra and were obtained for the entire muscle surface, as well as by excluding fatty infiltration based on CT attenuation. Findings were stratified for gender, and a threshold of the 33rd percentile was set to define sarcopenia. Furthermore, we assessed the possibility of being sarcopenic at both the TPA and TPMA level, or not, by taking into account of not fatty infiltration. Five-year survival was 39.1% for the whole population. Lower TPA, TMA, and TPA were associated with lower survival at univariate analysis; taking into account muscular fatty infiltration did not result in more powerful discrimination. Being sarcopenic at both psoas and parietal muscle level had the optimum discriminating power. At the multivariable analysis, being sarcopenic at both psoas and parietal muscles (considering the whole muscle areas, including muscular fat), male sex, increasing age, and tumor stage, as well as Charlson Comorbidity Index (CCI), were independently associated with worse long-term outcomes. We conclude that sarcopenia is a powerful negative prognostic factor in patients with lung cancer treated by pneumonectomy.

Sarcopenia as independent risk factor of postpneumonectomy respiratory failure, ARDS and mortality.

OBJECTIVES: Sarcopenia is associated with poor outcome in cancer-patients. However, the methods to define sarcopenia are not entirely standardized. We compared several morphometric measurements of sarcopenia and their prognostic value in short-term-outcome prediction after pneumonectomy. MATERIAL AND METHODS: Consecutive lung-cancer patients undergoing pneumonectomy from January 2007 to December 2015 and having a pre-operative computed tomography (CT) scan were retrospectively included. Sarcopenia was assessed by the following CT-based parameters measured at the level of the third lumbar vertebra: cross-sectional Total Psoas Area (TPA), cross-sectional Total Muscle Area (TMA), and Total Parietal Muscle Area (TPMA), defined as TMA without TPA. Measures were obtained for entire muscle surface, as well as by excluding fatty infiltration based on CT attenuation. Findings were stratified for gender, and a threshold of 33rd percentile was set to define sarcopenia. Acute Respiratory Failure (ARF), Acute Respiratory Distress Syndrome (ARDS), and 30-day mortality were assessed as parameters of short-term-outcome. RESULTS: Two hundred thirty-four patients with pneumonectomy (right, n = 107; left, n = 127) were analysed. Postoperative mortality rate was 9.0 % (21/234), 17.1 % of patients (40/234) experienced ARF requiring re-intubation, and 10.3 % (24/234) had ARDS. All parameters describing sarcopenia gave significant results; the best discriminating parameter was TMA after excluding fat (p < 0.001). While right sided pneumonectomy and sarcopenia were independently associated to the three short-term outcome parameters, Charlson Comorbidity Index only independently predicted ARF. CONCLUSIONS: Sarcopenia defined as the sex-related 33rd percentile of fat-excluded TMA at the level of the third lumbar vertebra is the most discriminating parameter to assess short-term-outcome in patients undergoing pneumonectomy.