Rapid identification of breast cancer subtypes using micro-FTIR and machine learning methods

Semi-Supervised Autoencoder for Chemical Gas Classification with FTIR Spectrum

Chemical warfare agents pose a serious threat due to their extreme toxicity, necessitating swift the identification of chemical gases and individual responses to the identified threats. Fourier transform infrared (FTIR) spectroscopy offers a method for remote material analysis, particularly in detecting colorless and odorless chemical agents. In this paper, we propose a deep neural network utilizing a semi-supervised autoencoder (SSAE) for the classification of chemical gases based on FTIR spectra. In contrast to traditional methods, the SSAE concurrently trains an autoencoder and a classifier attached to a latent vector of the autoencoder, enhancing feature extraction for classification. The SSAE was evaluated on laboratory-collected FTIR spectra, demonstrating a superior classification performance compared to existing methods. The efficacy of the SSAE lies in its ability to generate denser cluster distributions in latent vectors, thereby enhancing gas classification. This study established a consistent experimental environment for hyperparameter optimization, offering valuable insights into the influence of latent vectors on classification performance.

Recognition of breast cancer subtypes using FTIR hyperspectral data

Fourier-transform infrared spectroscopy (FTIR) is a powerful, non-destructive, highly sensitive and a promising analytical technique to provide spectrochemical signatures of biological samples, where markers like carbohydrates, proteins, and phosphate groups of DNA can be recognized in biological micro-environment. However, method of measurements of large cells need an excessive time to achieve high quality images, making its clinical use difficult due to speed of data-acquisition and lack of optimized computational procedures. To address such challenges, Machine Learning (ML) based technologies can assist to assess an accurate prognostication of breast cancer (BC) subtypes with high performance. Here, we applied FTIR spectroscopy to identify breast cancer subtypes in order to differentiate between luminal (BT474) and non-luminal (SKBR3) molecular subtypes. For this reason, we tested multivariate classification technique to extract feature information employing three-dimension (3D)-discriminant analysis approach based on 3D-principle component analysis-linear discriminant analysis (3D-PCA-LDA) and 3D-principal component analysis-quadratic discriminant analysis (3D-PCA-QDA), showing an improvement in sensitivity (98%), specificity (94%) and accuracy (98%) parameters compared to conventional unfolded methods. Our results evidence that 3D-PCA-LDA and 3D-PCA-QDA are potential tools for discriminant analysis of hyperspectral dataset to obtain superior classification assessment.

Machine learning-based model constructed from ultrasound radiomics and clinical features for predicting HER2 status in breast cancer patients with indeterminate (2+) immunohistochemical results

BACKGROUND

We aimed to predict human epidermal growth factor receptor 2 (HER2) 2+ status in patients with breast cancer by constructing and validating machine learning models utilizing ultrasound (US) radiomics and clinical features.

METHODS

We analyzed 203 breast cancer cases immunohistochemically determined as HER2 2+ and used fluorescence in situ hybridization (FISH) as the confirmation method. From each case, the study analyzed 840 extracted radiomics features and 11 clinicopathologic features. Cases were randomly split into training (n = 141) and validation sets (n = 62) at a 7:3 ratio. Univariate logistic regression analysis was first performed on the 11 clinicopathologic characteristics. The least absolute shrinkage and selection operator (LASSO) and decision tree (DT) techniques were employed for post-feature selection. Finally, 19 radiomics features were utilized in logistic regression (LR) and Naive Bayesian (NB) classifiers. Model performance was gauged using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

RESULTS

Our models exhibited notable diagnostic efficacy in differentiating HER2-positive from negative breast cancer cases. In the validation sets, the LR model outperformed the NB model with an AUC of 0.860 and accuracy of 83.8% compared to NB's AUC of 0.684 and accuracy of 79.0%. The LR model demonstrated higher sensitivity (92.3% vs. 46.2%) while the NB model had a better specificity (91.8% vs. 63.3%) in the validation set.

CONCLUSIONS

Machine learning models grounded on radiomics efficiently predicted IHC HER2 2+ status in breast cancer patients, suggesting potential enhancements in clinical decision-making for treatment and management.

Dynamic light scattering microscopy sensing mitochondria dynamics for label-free detection of triple-negative breast cancer enhanced by deep learning

We established a deep learning-based dynamic light scattering (DLS) microscopy sensing mitochondria dynamic for label-free identification of triple-negative breast cancer (TNBC) cells. The capacity of DLS microscopy to detect the intracellular motility of subcellular scatters was verified with the analysis of the autocorrelation function. We also conducted an in-depth examination of the impact of mitochondrial dynamics on DLS within TNBC cells, employing confocal fluorescent imaging to visualize the morphology of the mitochondria. Furthermore, we applied the DLS microscopy incorporating the two-stream deep learning method to differentiate the TNBC subtype and HER2 positive breast cancer subtype, with the classification accuracy achieving 0.89.

Latin America Optics and Photonics 2022: introduction to the feature issue

The 2022 Latin America Optics and Photonics Conference (LAOP 2022), the major international conference sponsored by Optica in Latin America, returned to Recife, Pernambuco, Brazil, after its first edition in 2010. Held every two years since (except for 2020), LAOP has the explicit objective to promote Latin American excellence in optics and photonics research and support the regional community. In the 6th edition in 2022, it featured a comprehensive technical program with recognized experts in fields critical to Latin America, highly multidisciplinary, with themes from biophotonics to 2D materials. The 191 attendees of LAOP 2022 listened to five plenary speakers, 28 keynotes, 24 invited talks, and 128 presentations, including oral and posters.