Role of Apparent Diffusion Coefficient Map-Based First- and High-Order Radiomic Features for the Discrimination of Sacral Chordomas and Chondrosarcomas With Overlapping Conventional Imaging Features.

PURPOSE: Chondrosarcomas arise from the lateral pelvis; however, midline chondrosarcomas (10%) display similar imaging features to chordoma, causing a diagnostic challenge. This study aims to determine the diagnostic accuracy of apparent diffusion coefficient (ADC)-based radiomic features and two novel diffusion indices for differentiating sacral chordomas and chondrosarcomas. METHODS: A retrospective, multireader review was performed of 82 pelvic MRIs (42 chordomas and 40 chondrosarcomas) between December 2014 and September 2021, split into training (n = 69) and validation (n = 13) data sets. Lesions were segmented on a single slice from ADC maps. Eight first-order features (minimum, mean, median, and maximum ADC, standard deviation, skewness, kurtosis, and entropy) and two novel indices: restriction index (RI, proportion of lesions with restricted diffusion) and facilitation index (FI, proportion of lesions with facilitated diffusion) were estimated. One hundred seven radiomic features comparing patients with chondrosarcoma versus chordoma were sorted based on mean group differences. RESULTS: There was good to excellent interobserver reliability for eight of the 10 ADC metrics on the training data set. Significant differences were observed (P < .005) for RI, FI, median, mean, and skewness using the training data set. Optimal cutpoints for diagnosis of chordoma were RI > 0.015; FI < 0.25; mean ADC < 1.7 × 10-3 mm2/s; and skewness >0.177. The optimal decision tree relied on FI. In a secondary analysis, significant differences (P < .00047) in chondrosarcoma versus chordoma were found in 18 of 107 radiomic features, including six first-order and 12 high-order features. CONCLUSION: The novel ADC index, FI, in addition to ADC mean, skewness, and 12 high-order radiomic features, could help differentiate sacral chordomas from chondrosarcomas.

Aligning Small Datasets Using Domain Adversarial Learning: Applications in Automated in Vivo Oral Cancer Diagnosis.

Deep learning approaches for medical image analysis are limited by small data set size due to factors such as patient privacy and difficulties in obtaining expert labelling for each image. In medical imaging system development pipelines, phases for system development and classification algorithms often overlap with data collection, creating small disjoint data sets collected at numerous locations with differing protocols. In this setting, merging data from different data collection centers increases the amount of training data. However, a direct combination of datasets will likely fail due to domain shifts between imaging centers. In contrast to previous approaches that focus on a single data set, we add a domain adaptation module to a neural network and train using multiple data sets. Our approach encourages domain invariance between two multispectral autofluorescence imaging (maFLIM) data sets of in vivo oral lesions collected with an imaging system currently in development. The two data sets have differences in the sub-populations imaged and in the calibration procedures used during data collection. We mitigate these differences using a gradient reversal layer and domain classifier. Our final model trained with two data sets substantially increases performance, including a significant increase in specificity. We also achieve a significant increase in average performance over the best baseline model train with two domains (p = 0.0341). Our approach lays the foundation for faster development of computer-aided diagnostic systems and presents a feasible approach for creating a robust classifier that aligns images from multiple data centers in the presence of domain shifts.

Clinical label-free endoscopic imaging of biochemical and metabolic autofluorescence biomarkers of benign, precancerous, and cancerous oral lesions.

Early detection is critical for improving the survival rate and quality of life of oral cancer patients; unfortunately, dysplastic and early-stage cancerous oral lesions are often difficult to distinguish from oral benign lesions during standard clinical oral examination. Therefore, there is a critical need for novel clinical technologies that would enable reliable oral cancer screening. The autofluorescence properties of the oral epithelial tissue provide quantitative information about morphological, biochemical, and metabolic tissue and cellular alterations accompanying carcinogenesis. This study aimed to identify novel biochemical and metabolic autofluorescence biomarkers of oral dysplasia and cancer that could be clinically imaged using novel multispectral autofluorescence lifetime imaging (maFLIM) endoscopy technologies. In vivo maFLIM clinical endoscopic images of benign, precancerous, and cancerous lesions from 67 patients were acquired using a novel maFLIM endoscope. Widefield maFLIM feature maps were generated, and statistical analyses were applied to identify maFLIM features providing contrast between dysplastic/cancerous vs. benign oral lesions. A total of 14 spectral and time-resolved maFLIM features were found to provide contrast between dysplastic/cancerous vs. benign oral lesions, representing novel biochemical and metabolic autofluorescence biomarkers of oral epithelial dysplasia and cancer. To the best of our knowledge, this is the first demonstration of clinical widefield maFLIM endoscopic imaging of novel biochemical and metabolic autofluorescence biomarkers of oral dysplasia and cancer, supporting the potential of maFLIM endoscopy for early detection of oral cancer.

End-to-End Neural Network for Feature Extraction and Cancer Diagnosis of In Vivo Fluorescence Lifetime Images of Oral Lesions.

In contrast to previous studies that focused on classical machine learning algorithms and hand-crafted features, we present an end-to-end neural network classification method able to accommodate lesion heterogeneity for improved oral cancer diagnosis using multispectral autofluorescence lifetime imaging (maFLIM) endoscopy. Our method uses an autoencoder framework jointly trained with a classifier designed to handle overfitting problems with reduced databases, which is often the case in healthcare applications. The autoencoder guides the feature extraction process through the reconstruction loss and enables the potential use of unsupervised data for domain adaptation and improved generalization. The classifier ensures the features extracted are task-specific, providing discriminative information for the classification task. The data-driven feature extraction method automatically generates task-specific features directly from fluorescence decays, eliminating the need for iterative signal reconstruction. We validate our proposed neural network method against support vector machine (SVM) baselines, with our method showing a 6.5%-8.3% increase in sensitivity. Our results show that neural networks that implement data-driven feature extraction provide superior results and enable the capacity needed to target specific issues, such as inter-patient variability and the heterogeneity of oral lesions.Clinical relevance- We improve standard classification algorithms for in vivo diagnosis of oral cancer lesions from maFLIm for clinical use in cancer screening, reducing unnecessary biopsies and facilitating early detection of oral cancer.

Machine-Learning Assisted Discrimination of Precancerous and Cancerous from Healthy Oral Tissue Based on Multispectral Autofluorescence Lifetime Imaging Endoscopy.

Multispectral autofluorescence lifetime imaging (maFLIM) can be used to clinically image a plurality of metabolic and biochemical autofluorescence biomarkers of oral epithelial dysplasia and cancer. This study tested the hypothesis that maFLIM-derived autofluorescence biomarkers can be used in machine-learning (ML) models to discriminate dysplastic and cancerous from healthy oral tissue. Clinical widefield maFLIM endoscopy imaging of cancerous and dysplastic oral lesions was performed at two clinical centers. Endoscopic maFLIM images from 34 patients acquired at one of the clinical centers were used to optimize ML models for automated discrimination of dysplastic and cancerous from healthy oral tissue. A computer-aided detection system was developed and applied to a set of endoscopic maFLIM images from 23 patients acquired at the other clinical center, and its performance was quantified in terms of the area under the receiver operating characteristic curve (ROC-AUC). Discrimination of dysplastic and cancerous from healthy oral tissue was achieved with an ROC-AUC of 0.81. This study demonstrates the capabilities of widefield maFLIM endoscopy to clinically image autofluorescence biomarkers that can be used in ML models to discriminate dysplastic and cancerous from healthy oral tissue. Widefield maFLIM endoscopy thus holds potential for automated in situ detection of oral dysplasia and cancer.

Clinical label-free biochemical and metabolic fluorescence lifetime endoscopic imaging of precancerous and cancerous oral lesions.

INTRODUCTION: Incomplete head and neck cancer resection occurs in up to 85% of cases, leading to increased odds of local recurrence and regional metastases; thus, image-guided surgical tools for accurate, in situ and fast detection of positive margins during head and neck cancer resection surgery are urgently needed. Oral epithelial dysplasia and cancer development is accompanied by morphological, biochemical, and metabolic tissue and cellular alterations that can modulate the autofluorescence properties of the oral epithelial tissue. OBJECTIVE: This study aimed to test the hypothesis that autofluorescence biomarkers of oral precancer and cancer can be clinically imaged and quantified by means of multispectral fluorescence lifetime imaging (FLIM) endoscopy. METHODS: Multispectral autofluorescence lifetime images of precancerous and cancerous lesions from 39 patients were imaged in vivo using a novel multispectral FLIM endoscope and processed to generate widefield maps of biochemical and metabolic autofluorescence biomarkers of oral precancer and cancer. RESULTS: Statistical analyses applied to the quantified multispectral FLIM endoscopy based autofluorescence biomarkers indicated their potential to provide contrast between precancerous/cancerous vs. healthy oral epithelial tissue. CONCLUSION: To the best of our knowledge, this study represents the first demonstration of label-free biochemical and metabolic clinical imaging of precancerous and cancerous oral lesions by means of widefield multispectral autofluorescence lifetime endoscopy. Future studies will focus on demonstrating the capabilities of endogenous multispectral FLIM endoscopy as an image-guided surgical tool for positive margin detection during head and neck cancer resection surgery.

Endogenous Fluorescence Lifetime Imaging (FLIM) Endoscopy For Early Detection Of Oral Cancer And Dysplasia.

We have performed a pilot clinical study, in which multispectral endogenous fluorescence (or autofluorescence) lifetime imaging (FLIM) was performed on clinically suspicious oral lesions of 73 patients undergoing tissue biopsy for oral dysplasia and cancer diagnosis. The results from this pilot study indicated that mild-dysplasia and early stage oral cancer could be detected from benign lesions using a computed aided diagnosis system developed based on biochemical and metabolic biomarkers derived from the endogenous FLIM images. The diagnostic performance of this novel FLIM clinical tool was estimated using a leave-onepatient-out cross-validation approach, which reported levels of sensitivity >90%, specificity >85%, and Area Under the Receiving Operating Curve (ROC-AUC) >0.9.