Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma.

OBJECTIVES: To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements. METHODS: A total of 542 patients with clinical stage 0-I peripheral LUAD and with preoperative CT data of 1-mm slice thickness were included. Maximal solid size on axial image (MSSA) was evaluated by two chest radiologists. MSSA, volume of solid component (SV), and mass of solid component (SM) were evaluated by DL. Consolidation-to-tumor ratios (CTRs) were calculated. For ground glass nodules (GGNs), solid parts were extracted with different density level thresholds. The prognosis prediction efficacy of DL was compared with that of manual measurements. Multivariate Cox proportional hazards model was used to find independent risk factors. RESULTS: The prognosis prediction efficacy of T-staging (TS) measured by radiologists was inferior to that of DL. For GGNs, MSSA-based CTR measured by radiologists (RMSSA%) could not stratify RFS and OS risk, whereas measured by DL using 0HU (2D-AIMSSA0HU%) could by using different cutoffs. SM and SV measured by DL using 0 HU (AISM0HU% and AISV0HU%) could effectively stratify the survival risk regardless of different cutoffs and were superior to 2D-AIMSSA0HU%. AISM0HU% and AISV0HU% were independent risk factors. CONCLUSION: DL algorithm can replace human for more accurate T-staging of LUAD. For GGNs, 2D-AIMSSA0HU% could predict prognosis rather than RMSSA%. The prediction efficacy of AISM0HU% and AISV0HU% was more accurate than of 2D-AIMSSA0HU% and both were independent risk factors. CLINICAL RELEVANCE STATEMENT: Deep learning algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma. KEY POINTS: • Deep learning (DL) algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma (LUAD). • For GGNs, maximal solid size on axial image (MSSA)-based consolidation-to-tumor ratio (CTR) measured by DL using 0 HU could stratify survival risk than that measured by radiologists. • The prediction efficacy of mass- and volume-based CTRs measured by DL using 0 HU was more accurate than of MSSA-based CTR and both were independent risk factors.

Phenanthroline-imine ligands for iron-catalyzed alkene hydrosilylation.

Iron-catalyzed organic reactions have been attracting increasing research interest but still have serious limitations on activity, selectivity, functional group tolerance, and stability relative to those of precious metal catalysts. Progress in this area will require two key developments: new ligands that can impart new reactivity to iron catalysts and elucidation of the mechanisms of iron catalysis. Herein, we report the development of novel 2-imino-9-aryl-1,10-phenanthrolinyl iron complexes that catalyze both anti-Markovnikov hydrosilylation of terminal alkenes and 1,2-anti-Markovnikov hydrosilylation of various conjugated dienes. Specifically, we achieved the first examples of highly 1,2-anti-Markovnikov hydrosilylation reactions of aryl-substituted 1,3-dienes and 1,1-dialkyl 1,3-dienes with these newly developed iron catalysts. Mechanistic studies suggest that the reactions may involve an Fe(0)-Fe(ii) catalytic cycle and that the extremely crowded environment around the iron center hinders chelating coordination between the diene and the iron atom, thus driving migration of the hydride from the silane to the less-hindered, terminal end of the conjugated diene and ultimately leading to the observed 1,2-anti-Markovnikov selectivity. Our findings, which have expanded the types of iron catalysts available for hydrosilylation reactions and deepened our understanding of the mechanism of iron catalysis, may inspire the development of new iron catalysts and iron-catalyzed reactions.