Detection of cervical lymph node metastasis from oral cavity cancer using a non-radiating, noninvasive digital infrared thermal imaging system

Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma

Ultrasonography is the preferred modality for detailed evaluation of enlarged lymph nodes (LNs) identified on computed tomography and/or magnetic resonance imaging, owing to its high spatial resolution. However, the diagnostic performance of ultrasonography depends on the examiner's expertise. To support the ultrasonographic diagnosis, we developed YOLOv7-based deep learning models for metastatic LN detection on ultrasonography and compared their detection performance with that of highly experienced radiologists and less experienced residents. We enrolled 462 B- and D-mode ultrasound images of 261 metastatic and 279 non-metastatic histopathologically confirmed LNs from 126 patients with head and neck squamous cell carcinoma. The YOLOv7-based B- and D-mode models were optimized using B- and D-mode training and validation images and their detection performance for metastatic LNs was evaluated using B- and D-mode testing images, respectively. The D-mode model's performance was comparable to that of radiologists and superior to that of residents' reading of D-mode images, whereas the B-mode model's performance was higher than that of residents but lower than that of radiologists on B-mode images. Thus, YOLOv7-based B- and D-mode models can assist less experienced residents in ultrasonographic diagnoses. The D-mode model could raise the diagnostic performance of residents to the same level as experienced radiologists.

Ultrasound Characteristics of Metastatic Occult Cervical Lymph Nodes in Early Tongue Cancer

Introduction: Identification of occult lymph node metastasis is challenging in early tongue cancers. We conducted a prospective study to determine the most characteristics ultrasonic feature suggestive of metastatic node. Material and Methods: A preliminary study based on feasibility was planned on twenty five patients with squamous cell carcinoma of tongue (T1,T2) and N0 neck underwent ultrasonography of neck. The results of each ultrasonic parameters (size, shape, echogenicity, margin and hilum) for suspicion were analysed. Pathologic evaluation of surgical resected neck specimen served as the reference standard. Results: USG yielded sensitivity and specificity by size, by morphology, either size or morphology are 50.0% and 87.5%, 75.0% and 87.5, 75.0 and 83.3% respectively. Morphology alone has highest negative predictive value (NPV:91.3%) with accuracy of 84.3%. Conclusion: Morphology of the lymph node had highest sensitivity and specificity with highest negative predictive value correlating with its metastatic nature.

Machine-Learning Applications in Oral Cancer: A Systematic Review

Over the years, several machine-learning applications have been suggested to assist in various clinical scenarios relevant to oral cancer. We offer a systematic review to identify, assess, and summarize the evidence for reported uses in the areas of oral cancer detection and prevention, prognosis, pre-cancer, treatment, and quality of life. The main algorithms applied in the context of oral cancer applications corresponded to SVM, ANN, and LR, comprising 87.71% of the total published articles in the field. Genomic, histopathological, image, medical/clinical, spectral, and speech data were used most often to predict the four areas of application found in this review. In conclusion, our study has shown that machine-learning applications are useful for prognosis, diagnosis, and prevention of potentially malignant oral lesions (pre-cancer) and therapy. Nevertheless, we strongly recommended the application of these methods in daily clinical practice.

Portable, handheld, and affordable blood perfusion imager for screening of subsurface cancer in resource-limited settings

Existing procedures of screening subsurface cancers are either prohibitively resource-intensive and expensive or are unable to provide direct quantitative estimates of the relevant physiological parameters for accurate classification accommodating interpatient variabilities and overlapping clinical manifestations. Here, we introduce a handheld and inexpensive blood perfusion imager that provides a noninvasive in situ screening approach for distinguishing precancer, cancer, and normal scenarios by precise quantitative estimation of the localized blood circulation in the tissue over an unrestricted region of interest without any unwarranted noise in the data, augmented by machine learning–based classification. Clinical trials in minimally resourced settings have established the efficacy of the method in differentiating cancerous and precancerous stages of suspected oral abnormalities, as verified by gold-standard biopsy reports.

Precise information on localized variations in blood circulation holds the key for noninvasive diagnostics and therapeutic assessment of various forms of cancer. While thermal imaging by itself may provide significant insights on the combined implications of the relevant physiological parameters, viz. local blood perfusion and metabolic balance due to active tumors as well as the ambient conditions, knowledge of the tissue surface temperature alone may be somewhat inadequate in distinguishing between some ambiguous manifestations of precancer and cancerous lesions, resulting in compromise of the selectivity in detection. This, along with the lack of availability of a user-friendly and inexpensive portable device for thermal-image acquisition, blood perfusion mapping, and data integration acts as a deterrent against the emergence of an inexpensive, contact-free, and accurate in situ screening and diagnostic approach for cancer detection and management. Circumventing these constraints, here we report a portable noninvasive blood perfusion imager augmented with machine learning–based quantitative analytics for screening precancerous and cancerous traits in oral lesions, by probing the localized alterations in microcirculation. With a proven overall sensitivity >96.66% and specificity of 100% as compared to gold-standard biopsy-based tests, the method successfully classified oral cancer and precancer in a resource-limited clinical setting in a double-blinded patient trial and exhibited favorable predictive capabilities considering other complementary modes of medical image analysis as well. The method holds further potential to achieve contrast-free, accurate, and low-cost diagnosis of abnormal microvascular physiology and other clinically vulnerable conditions, when interpreted along with complementary clinically evidenced decision-making perspectives.

Establishing a Thermal Imaging Technology (IRT) Based System for Evaluating Rat Erectile Function

Introduction

Measurement of intra-cavernous pressure (ICP) is an internationally recognized method to evaluate erectile function of animals, however, this process is invasive, destructive, and cannot be repeated, leading to a daunting challenge for monitoring the changes in erectile function throughout the whole treatment duration.

Aim

To verify whether infrared ray thermography technology based system could be a good substitution of ICP for evaluating rat erectile function.

Methods

A novel thermal image-based method, infrared ray thermography technology (IRT) was employed to monitor erectile function in erectile dysfunction (ED) rats. To detect the sensitivity and specificity of this new technology, 4 ED rat models (Diabetic, nerve-injury, vascular-injury and aged ED models) were established and subjected to both ICP and IRT test.

Outcomes

Statistical comparisons were done to test the effectiveness of this new way for detecting and dynamically monitoring erectile function.

Results

Based on the data curves obtained from ICP and IRT, the IRT showed a similar trend (including peak value, climbing speed) as that of ICP. IRT is considered as a precise way to monitor the real-time changes of erectile function in all ED rat models. The AUC of peak temperature detected by IRT in DMED, aged ED, vascular-injury ED, the nerve-injury ED and total ED rat models were 0.9811,0.9836,0.9893,0.9989 and 0.9882, respectively. Meanwhile, the AUC of temperature climbing rate were 0.6486,0.8357,0.9184,0.8675and 0.8168.Also,it is a non-invasive process of dynamically monitoring erectile function of a same rat at different time points (before and after drug intervention). The data showed that the real-time recovery by tadalafil was obtained by IRT methods even after treatment for only 2 weeks in the diabetic ED (DMED) rat model.

Conclusion

A novel noninvasive method for monitoring erectile function in rat ED models was established, and can replace or supplement ICP test.

Liu S, Zhao Z, Wang Z et al. Establishing a Thermal Imaging Technology (IRT) Based System for Evaluating Rat Erectile Function. Sex Med 2022;10:100475.

Medical Infrared Thermography in Odontogenic Facial Cellulitis as a Clinical Decision Support Tool. A Technical Note

Background: Odontogenic cellulitis are frequent infections of the head and neck fascial spaces that can sometimes spread and be life-threatening, requiring urgent hospitalization. Early diagnosis of facial cellulitis with diffuse inflammatory process is crucial in patient management but not always obvious in the field. Medical infrared thermography (MIT) is a noninvasive tool increasingly used to evaluate skin temperature maps and delineate inflammatory lesions. Objective: The aim of this work was to evaluate the use of MIT to improve the clinical examination of patients with facial cellulitis. Methods: Image processing work was carried out to highlight the thermal gradient resulting from inflammation linked to infection, in 2 patients with facial cellulitis. Results: In real-time, MIT allowed to precisely locate the inflammatory focus linked to cellulitis with no propagation to danger areas such as infraorbital space or around pharyngeal axis. Conclusions: Here, we show the first cases using MIT as a powerful complementary tool in the clinical evaluation of patients with facial cellulitis. Significance: This technology could help optimize the hospitalization decision through a facilitated assessment of infection spread in head and neck tissues and helping to incision for drainage.

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

Artificial intelligence has been emerging as an increasingly important aspect of our daily lives and is widely applied in medical science. One major application of artificial intelligence in medical science is medical imaging. As a major component of artificial intelligence, many machine learning models are applied in medical diagnosis and treatment with the advancement of technology and medical imaging facilities. The popularity of convolutional neural network in dental, oral and craniofacial imaging is heightening, as it has been continually applied to a broader spectrum of scientific studies. Our manuscript reviews the fundamental principles and rationales behind machine learning, and summarizes its research progress and its recent applications specifically in dental, oral and craniofacial imaging. It also reviews the problems that remain to be resolved and evaluates the prospect of the future development of this field of scientific study.

Development of a machine learning model for the prediction of nodal metastasis in early T classification oral squamous cell carcinoma: SEER-based population study

BACKGROUND

This study aimed to develop and compare machine learning (ML) based predictive models for lymph node metastasis (LNM) in early T classification oral squamous cell carcinoma (OSCC).

METHODS

We used data from the Surveillance Epidemiology and End Results Database to develop and validate the predictive models for LNM in patients with T1, T2 OSCC. Using simple clinical and histopathological data, we developed six ML algorithms to predict LNM. The predictive performance of models was compared.

RESULTS

The areas under the receiver operating characteristic curves (AUCs) of the six models ranged from 0.768 to 0.956. The best prediction performance was achieved with a XGBoost (AUC = 0.956). Permutation importance analysis showed that tumor size is the most important feature in predicting metastasis.

CONCLUSIONS

We developed a simplified and reproducible ML-based predictive model for metastasis in early T classification OSCC that could be helpful for the decision of a treatment strategy.

Whole-body MRI added to gadoxetic acid-enhanced liver MRI for detection of extrahepatic disease in patients considered eligible for hepatic resection and/or local ablation of colorectal cancer liver metastases

Background

Fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) can detect extrahepatic disease before local treatment of colorectal liver metastases.

Purpose

To investigate if whole-body magnetic resonance imaging (MRI) added to gadoxetic acid liver MRI could replace FDG-PET/CT for detection of extrahepatic disease in patients with colorectal liver metastases eligible for hepatic local treatment.

Material and Methods

This health-research-ethics-committee-approved prospective consecutive diagnostic accuracy study with written informed consent analyzed 79 cases included between 29 June 2015 and 7 February 2017. Whole-body MRI covering the thorax, abdomen, and pelvis and FDG-PET/CT including contrast-enhanced CT was performed 0–3 days before local treatment of liver metastases. Whole-body MR images were read independently by two readers. FDG-PET/CT images were read independently by two pairs of readers. Histopathology and follow-up imaging were used as reference standard. Sensitivities and specificities were calculated and compared including noninferiority analysis.

Results

Seventy-five malignant lesions and 419 benign lesions were confirmed. Sensitivities of both PET/CT reader pairs 1 and 2 (56.7 and 67.9%) and MRI reader 2 (63.0%) were significantly higher than that of MRI reader 1 (42.7) (P<0.04). Specificities of both PET/CT reader pairs 1 and 2 (92.5 and 92.4%) and MRI reader 1 (91.1%) were significantly higher than that of MRI reader 2 (86.3%) ( P < 0.02). Sensitivity of MRI reader 2 was non-inferior compared to PET/CT reader pair 1. Specificities of both MRI readers were non-inferior to both PET/CT reader pairs.

Conclusion

For detection of extrahepatic disease in patients with colorectal liver metastases, whole-body MRI was non-inferior to FDG-PET/CT for some reader combinations. However, reader-independent non-inferiority could not be demonstrated.

Estimating Departure Time Using Thermal Camera and Heat Traces Tracking Technique

Advancement in science and technology is playing an increasingly important role in solving difficult cases at present. Thermal cameras can help the police crack difficult cases by capturing the heat trace on the ground left by perpetrators, which cannot be spotted by the naked eye. Therefore, the purpose of this study is to establish a thermalfoot model using thermal imaging system to estimate the departure time. To this end, in the current work, we use a thermal camera to acquire the thermal sequence left on the floor, and convert it into the heat signal via image processing algorithm. We establish the model of thermalfoot print as we observe that the residual temperature would exponentially decrease with the departure time according to Newton’s Law of Cooling. The correlation coefficients of 107 thermalfoot models derived from the corresponding 107 heat signals are basically above 0.99. In a validation experiment, a residual analysis is conducted and the residuals between estimated departure time points and ground-truth times are almost within a certain range from −150 s to +150 s. The reverse accuracy of the thermalfoot model for estimating departure time at one-third, one-half, two-thirds, three-fourths, four-fifths, and five-sixths capture time points are 71.96%, 50.47%, 42.06%, 31.78%, 21.70%, and 11.21%, respectively. The results of comparison experiments with two subjective evaluation methods (subjective 1: we directly estimate the departure time according to obtained local curves; subjective 2: we utilize auxiliary means such as a ruler to estimate the departure time based on obtained local curves) further demonstrate the effectiveness of thermalfoot model for detecting the departure time inversely. Experimental results also demonstrated that the thermalfoot model has good performance on the departure time reversal within a short time window someone leaves, whereas it is probably only approximately 15% to accurately determine the departure time via thermalfoot model within a long time window someone leaves. The influence of outliers, ROI (Region of Interest) selection, ROI size, different capture time points and environment temperature on the performance of thermalfoot model on departure time reversal can be explored in the future work. Overall, the thermalfoot model can help the police solve crimes to some extent, which in turn brings more guarantees for people’s health, social security, and stability.