Novel image markers for non-small cell lung cancer classification and survival prediction

BACKGROUND: Non-small cell lung cancer (NSCLC), the most common type of lung cancer, is one of serious diseases causing death for both men and women. Computer-aided diagnosis and survival prediction of NSCLC, is of great importance in providing assistance to diagnosis and personalize therapy planning for lung cancer patients. RESULTS: In this paper we have proposed an integrated framework for NSCLC computer-aided diagnosis and survival analysis using novel image markers. The entire biomedical imaging informatics framework consists of cell detection, segmentation, classification, discovery of image markers, and survival analysis. A robust seed detection-guided cell segmentation algorithm is proposed to accurately segment each individual cell in digital images. Based on cell segmentation results, a set of extensive cellular morphological features are extracted using efficient feature descriptors. Next, eight different classification techniques that can handle high-dimensional data have been evaluated and then compared for computer-aided diagnosis. The results show that the random forest and adaboost offer the best classification performance for NSCLC. Finally, a Cox proportional hazards model is fitted by component-wise likelihood based boosting. Significant image markers have been discovered using the bootstrap analysis and the survival prediction performance of the model is also evaluated. CONCLUSIONS: The proposed model have been applied to a lung cancer dataset that contains 122 cases with complete clinical information. The classification performance exhibits high correlations between the discovered image markers and the subtypes of NSCLC. The survival analysis demonstrates strong prediction power of the statistical model built from the discovered image markers

Novel Image Markers for Non-Small Cell Lung Cancer Classification and Survival Prediction

BACKGROUND: Non-small cell lung cancer (NSCLC), the most common type of lung cancer, is one of serious diseases causing death for both men and women. Computer-aided diagnosis and survival prediction of NSCLC, is of great importance in providing assistance to diagnosis and personalize therapy planning for lung cancer patients. RESULTS: In this paper we have proposed an integrated framework for NSCLC computer-aided diagnosis and survival analysis using novel image markers. The entire biomedical imaging informatics framework consists of cell detection, segmentation, classification, discovery of image markers, and survival analysis. A robust seed detection-guided cell segmentation algorithm is proposed to accurately segment each individual cell in digital images. Based on cell segmentation results, a set of extensive cellular morphological features are extracted using efficient feature descriptors. Next, eight different classification techniques that can handle high-dimensional data have been evaluated and then compared for computer-aided diagnosis. The results show that the random forest and adaboost offer the best classification performance for NSCLC. Finally, a Cox proportional hazards model is fitted by component-wise likelihood based boosting. Significant image markers have been discovered using the bootstrap analysis and the survival prediction performance of the model is also evaluated. CONCLUSIONS: The proposed model have been applied to a lung cancer dataset that contains 122 cases with complete clinical information. The classification performance exhibits high correlations between the discovered image markers and the subtypes of NSCLC. The survival analysis demonstrates strong prediction power of the statistical model built from the discovered image markers

Large Second-Harmonic Response and Giant Birefringence of CeF2(SO4) Induced by Highly Polarizable Polyhedra

Second-harmonic generation (SHG) response and birefringence are two critically important properties of nonlinear optical (NLO) materials. However, the simultaneous optimization of these two key properties remains a major challenge because of their contrasting microstructure requirements. Herein, we report the first tetravalent rare-earth metal fluorinated sulfate, CeF2(SO4). Its structure features novel net-like layers constructed by highly distorted [CeO4F4] polyhedra, which are further interconnected by [SO4] tetrahedra to form a three-dimensional structure. CeF2(SO4) exhibits the strongest SHG effect (8 times that of KH2PO4) and the largest birefringence for sulfate-based NLO materials, the latter exceeding the birefringent limit for oxides. Theoretical calculations and crystal structure analysis reveal that the unusually large SHG response and giant birefringence can be attributed to the introduction of the highly polarizable fluorinated [CeO4F4] polyhedra as well as the favorable alignment of [CeO4F4] polyhedra and [SO4] tetrahedra. This research affords a new paradigm for the designed synthesis of high-performance NLO materials.This research was financially supported by the National Natural Science Foundation of China (no. 51432006), the Ministry of Education of China for the Changjiang Innovation Research Team (no. IRT14R23), the Ministry of Education and the State Administration of Foreign Experts Affairs for the 111 Project (no. B13025), the Innovation Program of Shanghai Municipal Education Commission, and the National and Shanghai Postdoctoral Program for Innovative Talents (nos. BX201800216 and 2018192). M.G.H. thanks the Australian Research Council for support (DP170100411)

A highly sensitive and selective antioxidant probe based on a bi-modally functionalized conjugated polyelectrolyte

A new water-soluble anionic conjugated polyelectrolyte with a nitroxide radical covalently linked to the sulfonated poly(phenylene ethynylene) backbone (PPE-SO(3)) is reported. This radical-functionalized PPE-SO(3) (RF-PPE-SO(3)) demonstrates fluorescence and electron spin resonance (ESR) bimodal signaling function and shows sensitive and selective response to antioxidants.Ministry of Science and Technology of China[2011CB910403]; National Natural Science Foundation of China[20835005, 20975086, J1030415

UV Solar‐Blind‐Region Phase‐Matchable Optical Nonlinearity and Anisotropy in a π‐Conjugated Cation‐Containing Phosphate

Wide ultraviolet (UV) transparency, strong second-harmonic generation (SHG) response, and sufficient optical birefringence for phase-matching (PM) at short SHG wavelengths are vital for practical UV nonlinear optical (NLO) materials. However, simultaneously optimizing these properties is a major challenge, particularly for metal phosphates. Herein, we report a non-traditional π-conjugated cation-based UV NLO phosphate [C(NH2)3]6(PO4)2⋅3 H2O (GPO) with a short UV cutoff edge. GPO is SHG active at 1064 nm (3.8 × KH2PO4 @ 1064 nm) and 532 nm (0.3 × β-BaB2O4 @ 532 nm) and also possesses a significant birefringence (0.078 @ 546 nm) with a band gap >6.0 eV. The PM SHG capability of GPO can extend to 250 nm, indicating GPO is a promising UV solar-blind NLO material. Calculations and crystal structure analysis show that the rare coexistence of wide UV transparency, large SHG response, and optical anisotropy is due to the introduction of π-conjugated cations [C(NH2)3]+ and their favorable arrangement with [PO4]3− anions.This research was financially supported by the NationalNatural Science Foundation of China (nos. 51432006,52002276), the Ministry of Education of China for theChangjiang Innovation Research Team (no. IRT14R23), theInnovation Program of Shanghai Municipal Education Com-mission, and the Ministry of Education and the StateAdministration of Foreign Experts Affairs for the 111 Project(no. B13025). M.G.H. thanks the Australian Research Coun-cil for support (DP170100411)