Semi-automatic decision-making process in histopathological specimens from Barrett’s carcinoma patients using hyperspectral imaging (HSI)

Maktabi, Marianne; Köhler, Helmut; Chalopin, Claire; Neumuth, Thomas; Wichmann, Yrjö; Jansen-Winkeln, B; Gockel, Ines; Thieme, René; Ahle, Henning; Lorenz, D.; Bange, Michael; Braun, Susanne

doi:10.1515/cdbme-2020-3066

Cited by 2 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, in this study and in Hu et al, a realistic LOPOCV was done 13 . This work was based on a previous study, which had shown the best performance for tumor cell identification by a neuronal network with a MLP algorithm 21 . Additionally, SVM and LR models have been used.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Tumor cell identification and classification in esophageal adenocarcinoma specimens by hyperspectral imaging

Maktabi

Wichmann

Köhler

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

Esophageal cancer is the sixth leading cause of cancer-related death worldwide. Histopathological confirmation is a key step in tumor diagnosis. Therefore, simplification in decision-making by discrimination between malignant and non-malignant cells of histological specimens can be provided by combination of new imaging technology and artificial intelligence (AI). In this work, hyperspectral imaging (HSI) data from 95 patients were used to classify three different histopathological features (squamous epithelium cells, esophageal adenocarcinoma (EAC) cells, and tumor stroma cells), based on a multi-layer perceptron with two hidden layers. We achieved an accuracy of 78% for EAC and stroma cells, and 80% for squamous epithelium. HSI combined with machine learning algorithms is a promising and innovative technique, which allows image acquisition beyond Red–Green–Blue (RGB) images. Further method validation and standardization will be necessary, before automated tumor cell identification algorithms can be used in daily clinical practice.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Additionally, SVM and LR models have been used. By evaluating the data in a grid seach cross-validation a MLP showed the highest sensitivity of 90% 21 . The sensitivity for the cohort investigated here was 77% for EAC and tumor stroma cells.…”

Section: Discussionmentioning

confidence: 99%

Tumor cell identification and classification in esophageal adenocarcinoma specimens by hyperspectral imaging

Maktabi

Wichmann

Köhler

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Specimens from gastric cancer patients were stained with hematoxylin and eosin (HE). Areas with healthy gastric mucosa ( a ) and gastric cancer (GC) ( b ) were selected and imaged by a HSI camera as described previously [ 50 ]. The RGB images were reconstructed.…”

Section: Figurementioning

confidence: 99%

New Intraoperative Imaging Tools and Image-Guided Surgery in Gastric Cancer Surgery

et al. 2022

Self Cite

View full text Add to dashboard Cite

Innovations and new advancements in intraoperative real-time imaging have gained significant importance in the field of gastric cancer surgery in the recent past. Currently, the most promising procedures include indocyanine green fluorescence imaging (ICG-FI) and hyperspectral imaging or multispectral imaging (HSI, MSI). ICG-FI is utilized in a broad range of clinical applications, e.g., assessment of perfusion or lymphatic drainage, and additional implementations are currently investigated. HSI is still in the experimental phase and its value and clinical relevance require further evaluation, but initial studies have shown a successful application in perfusion assessment, and prospects concerning non-invasive tissue and tumor classification are promising. The application of machine learning and artificial intelligence technologies might enable an automatic evaluation of the acquired image data in the future. Both methods facilitate the accurate visualization of tissue characteristics that are initially indistinguishable for the human eye. By aiding surgeons in optimizing the surgical procedure, image-guided surgery can contribute to the oncologic safety and reduction of complications in gastric cancer surgery and recent advances hold promise for the application of HSI in intraoperative tissue diagnostics.

show abstract

Semi-automatic decision-making process in histopathological specimens from Barrett’s carcinoma patients using hyperspectral imaging (HSI)

Cited by 2 publications

References 6 publications

Tumor cell identification and classification in esophageal adenocarcinoma specimens by hyperspectral imaging

Tumor cell identification and classification in esophageal adenocarcinoma specimens by hyperspectral imaging

New Intraoperative Imaging Tools and Image-Guided Surgery in Gastric Cancer Surgery

Contact Info

Product

Resources

About