Development and Assessment of a Machine Learning Model to Help Predict Survival Among Patients With Oral Squamous Cell Carcinoma

Potential of AI and ML in oncology research including diagnosis, treatment and future directions: A comprehensive prospective

Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in cancer research, offering the ability to process huge data rapidly and make precise therapeutic decisions. Over the last decade, AI, particularly deep learning (DL) and machine learning (ML), has significantly enhanced cancer prediction, diagnosis, and treatment by leveraging algorithms such as convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs). These technologies provide reliable, efficient solutions for managing aggressive diseases like cancer, which have high recurrence and mortality rates. This review prospective highlights the applications of AI in oncology, a long with FDA-approved technologies like EFAI RTSuite CT HN-Segmentation System, Quantib Prostate, and Paige Prostate, and explore their role in advancing cancer detection, personalized care, and treatment. Furthermore, we also explored broader applications of AI in healthcare, addressing challenges, limitations, regulatory considerations, and ethical implications. By presenting these advancements, we underscore AI's potential to revolutionize cancer care, management and treatment.

Unplanned rehospitalizations after abdominal wall surgery: Update according to a review of the literature

Unplanned readmission (UR) is defined as an unforeseen readmission of a patient within 30days of discharge to the same facility for a reason other than mental health, chemotherapy or dialysis. In the literature, UR rates after groin hernia repair range from 2.7 to 5.1% after open or laparoscopic primary ventral hernia repair, and 12% after complex incisional hernia repair. Postoperative complications are the major cause of UR, irrespective of the type of parietal surgery. Risk factors for UR include diabetes, smoking, chronic obstructive pulmonary disease, obesity, therapeutic anticoagulation, ASA score≥3, long duration or emergency surgery, and low socioeconomic status. Anticipating and managing these risk factors can help limit UR.

Diagnosis of lymph node metastasis in oral squamous cell carcinoma by an MRI-based deep learning model

BACKGROUND

Cervical lymph node metastasis (LNM) is a well-established poor prognosticator of oral squamous cell carcinoma (OSCC), in which occult metastasis is a subtype that makes prediction challenging. Here, we developed and validated a deep learning (DL) model using magnetic resonance imaging (MRI) for the identification of LNM in OSCC patients.

METHODS

This retrospective diagnostic study developed a three-stage DL model by 45,664 preoperative MRI images from 723 patients in 10 Chinese hospitals between January 2015 and October 2020. It was comprehensively processed from training (8:2), multicenter external validation to reader study. The performance of the DL model was accessed and compared with general and specialized radiologists.

RESULTS

LNM was found in 36.51% of all patients, and the occult metastasis rate was 16.45%. The three-stage DL model together with a random forest classifier achieved the performance in identification of LNM with areas under curve (AUC) of 0.97 (0.93-0.99) in training cohort and AUC of 0.81 (0.74-0.86) in external validation cohorts. The models can reduce the occult metastasis rate up to 89.50% and add more benefit in guiding neck dissection in cN0 patients. DL models tied or exceeded average performance relative to both general and specialized radiologists.

CONCLUSION

Our three-stage DL model based on MRI with three-dimensional sequences was beneficial in detecting LNM and reducing the occult metastasis rate of OSCC patients.

Machine Learning Predicts 30-Day Readmission and Mortality After Surgical Resection of Head and Neck Cancer

Objective

To develop and validate a machine learning model to identify patients at high risk of 30-day mortality and hospital readmission using routinely collected health care data.

Study Design

Prognostic predictive modeling and retrospective cohort study. The study was conducted in 2024 using data from 2006 to 2018, with at least a 30-day follow-up.

Setting

The 2006 to 2018 National Cancer Database (NCDB).

Methods

The study used deidentified NCDB data on 103,891 head and neck squamous cell carcinoma (HNSCC) patients who underwent surgical resection. Machine learning models were trained on 80% of the data, tested on the remaining 20%, and evaluated using the area under the curve (AUC) and SHapley Additive exPlanations (SHAP) analysis to identify key predictors for 30-day mortality and readmission.

Results

Among 103,891 patients, 5838 (5.6%) were readmitted, and 829 (0.8%) died within 30 days. The median age was 62, 69% male, and 89% white. Predictors included demographic and clinical data from the NCDB. Five machine learning models were combined and achieved an AUC of 0.80 (95% CI: 0.77-0.83) for mortality prediction and 0.67 (95% CI: 0.65-0.68) for readmission prediction. SHAP analysis identified sex and urban-rural index as key predictors of mortality and readmission, respectively.

Conclusion

Machine learning models can accurately predict mortality and readmission risks, offering insights into the most influential factors. With further validation, these models may enhance clinical decision-making in postsurgical care for HNSCC patients.

Machine Learning Methods in Classification of Prolonged Radiation Therapy in Oropharyngeal Cancer: National Cancer Database

OBJECTIVE

To investigate the accuracy of machine learning (ML) algorithms in stratifying risk of prolonged radiation treatment duration (RTD), defined as greater than 50 days, for patients with oropharyngeal squamous cell carcinoma (OPSCC).

STUDY DESIGN

Retrospective cohort study.

SETTING

National Cancer Database (NCDB).

METHODS

The NCDB was queried between 2004 to 2016 for patients with OPSCC treated with radiation therapy (RT) or chemoradiation as primary treatment. To predict risk of prolonged RTD, 8 different ML algorithms were compared against traditional logistic regression using various performance metrics. Data was split into a distribution of 70% for training and 30% for testing.

RESULTS

A total of 3152 patients were included (1928 prolonged RT, 1224 not prolonged RT). As a whole, based on performance metrics, random forest (RF) was found to most accurately predict prolonged RTD compared to both other ML methods and traditional logistic regression.

CONCLUSION

Our assessment of various ML techniques showed that RF was superior to traditional logistic regression at classifying OPSCC patients at risk of prolonged RTD. Application of such algorithms may have potential to identify high risk patients and enable early interventions to improve survival.

Prediction model of ICU readmission in Chinese patients with acute type A aortic dissection: a retrospective study

BACKGROUND

Readmission to the intensive care unit (ICU) remains a severe challenge, leading to higher rates of death and a greater financial burden. This study aimed to develop a nomogram-based prediction model for individuals with acute type A aortic dissection (ATAAD).

METHODS

A total of 846 ATAAD patients were retrospectively enrolled between May 2014 and October 2021. Logistic regression was employed to identify the independent risk factors. The prediction model was evaluated using the Hosmer-Lemeshow (H-L) test, the calibration curve, and the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was used to assess the clinical utility.

RESULTS

57 (6.7%) ATAAD patients were readmitted to ICU following their release from the ICU. ICU readmission was predicted with age ≥ 65 years old, body mass index (BMI) ≥ 28 kg/m2, tracheotomy, continuous renal replacement therapy (CRRT), and the length of initial ICU stay were predictors of ICU readmission. The AUC was 0.837 (95%CI: 0.789-0.884) and the model fit the data well (H-L test, P = 0.519). DCA also demonstrated good clinical practicability.

CONCLUSIONS

This prediction model may be helpful for clinicians to assess the risk of ICU readmission, and facilitate the early identification of ATAAD patients at high risk.

Artificial Intelligence in Head and Neck Surgery

This article explores artificial intelligence's (AI's) role in otolaryngology for head and neck cancer diagnosis and management. It highlights AI's potential in pattern recognition for early cancer detection, prognostication, and treatment planning, primarily through image analysis using clinical, endoscopic, and histopathologic images. Radiomics is also discussed at length, as well as the many ways that radiologic image analysis can be utilized, including for diagnosis, lymph node metastasis prediction, and evaluation of treatment response. The study highlights AI's promise and limitations, underlining the need for clinician-data scientist collaboration to enhance head and neck cancer care.

Perceptions of Artificial Intelligence Among Otolaryngologists in Saudi Arabia: A Cross-Sectional Study

Purpose

Otolaryngology has experienced notable advancements and growth in the application of artificial intelligence (AI). However, otolaryngologists' perception of these tools are lacking. This study aims to assess the knowledge and attitudes of otolaryngologists toward AI.

Patients and Methods

A cross-sectional study was conducted among 110 otolaryngologists in the Eastern Province of Saudi Arabia. A piloted questionnaire was used to gather information on knowledge, attitude, and opinions regarding AI. Data analysis was conducted using SPSS version 26.

Results

Of the sample, 60% indicated average perceived knowledge of AI, while approximately 44.5% perceived their AI knowledge in the field of otolaryngology to be below average. A significant positive correlation was identified between knowledge and attitude scores. It was found that a higher knowledge score was more closely associated with seeing more than 15 patients per day, while a higher attitude score was more closely associated with being older, being a consultant, and having more years of professional experience. Of the sample, 38.2% strongly agreed that the application of AI in scientific research should be included in the residency training program.

Conclusion

These findings underscore the importance of incorporating AI tools into certain aspects of the otolaryngology residency training program, highlighting their significance.

Predicting reoperation and readmission for head and neck free flap patients using machine learning

BACKGROUND

To develop machine learning (ML) models predicting unplanned readmission and reoperation among patients undergoing free flap reconstruction for head and neck (HN) surgery.

METHODS

Data were extracted from the 2012-2019 NSQIP database. eXtreme Gradient Boosting (XGBoost) was used to develop ML models predicting 30-day readmission and reoperation based on demographic and perioperative factors. Models were validated using 2019 data and evaluated.

RESULTS

Four-hundred and sixty-six (10.7%) of 4333 included patients were readmitted within 30 days of initial surgery. The ML model demonstrated 82% accuracy, 63% sensitivity, 85% specificity, and AUC of 0.78. Nine-hundred and four (18.3%) of 4931 patients underwent reoperation within 30 days of index surgery. The ML model demonstrated 62% accuracy, 51% sensitivity, 64% specificity, and AUC of 0.58.

CONCLUSION

XGBoost was used to predict 30-day readmission and reoperation for HN free flap patients. Findings may be used to assist clinicians and patients in shared decision-making and improve data collection in future database iterations.

Length of Stay Prediction Models for Oral Cancer Surgery: Machine Learning, Statistical and ACS-NSQIP

OBJECTIVE

Accurate prediction of hospital length of stay (LOS) following surgical management of oral cavity cancer (OCC) may be associated with improved patient counseling, hospital resource utilization and cost. The objective of this study was to compare the performance of statistical models, a machine learning (ML) model, and The American College of Surgeons National Surgical Quality Improvement Program's (ACS-NSQIP) calculator in predicting LOS following surgery for OCC.

MATERIALS AND METHODS

A retrospective multicenter database study was performed at two major academic head and neck cancer centers. Patients with OCC who underwent major free flap reconstructive surgery between January 2008 and June 2019 surgery were selected. Data were pooled and split into training and validation datasets. Statistical and ML models were developed, and performance was evaluated by comparing predicted and actual LOS using correlation coefficient values and percent accuracy.

RESULTS

Totally 837 patients were selected with mean patient age being 62.5 ± 11.7 [SD] years and 67% being male. The ML model demonstrated the best accuracy (validation correlation 0.48, 4-day accuracy 70%), compared with the statistical models: multivariate analysis (0.45, 67%) and least absolute shrinkage and selection operator (0.42, 70%). All were superior to the ACS-NSQIP calculator's performance (0.23, 59%).

CONCLUSION

We developed statistical and ML models that predicted LOS following major free flap reconstructive surgery for OCC. Our models demonstrated superior predictive performance to the ACS-NSQIP calculator. The ML model identified several novel predictors of LOS. These models must be validated in other institutions before being used in clinical practice.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:3664-3672, 2024.

Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review

BACKGROUND

This systematic review aimed to evaluate the performance of machine learning (ML) models in predicting post-treatment survival and disease progression outcomes, including recurrence and metastasis, in head and neck cancer (HNC) using clinicopathological structured data.

METHODS

A systematic search was conducted across the Medline, Scopus, Embase, Web of Science, and Google Scholar databases. The methodological characteristics and performance metrics of studies that developed and validated ML models were assessed. The risk of bias was evaluated using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

RESULTS

Out of 5,560 unique records, 34 articles were included. For survival outcome, the ML model outperformed the Cox proportional hazards model in time-to-event analyses for HNC, with a concordance index of 0.70-0.79 vs. 0.66-0.76, and for all sub-sites including oral cavity (0.73-0.89 vs. 0.69-0.77) and larynx (0.71-0.85 vs. 0.57-0.74). In binary classification analysis, the area under the receiver operating characteristics (AUROC) of ML models ranged from 0.75-0.97, with an F1-score of 0.65-0.89 for HNC; AUROC of 0.61-0.91 and F1-score of 0.58-0.86 for the oral cavity; and AUROC of 0.76-0.97 and F1-score of 0.63-0.92 for the larynx. Disease-specific survival outcomes showed higher performance than overall survival outcomes, but the performance of ML models did not differ between three- and five-year follow-up durations. For disease progression outcomes, no time-to-event metrics were reported for ML models. For binary classification of the oral cavity, the only evaluated subsite, the AUROC ranged from 0.67 to 0.97, with F1-scores between 0.53 and 0.89.

CONCLUSIONS

ML models have demonstrated considerable potential in predicting post-treatment survival and disease progression, consistently outperforming traditional linear models and their derived nomograms. Future research should incorporate more comprehensive treatment features, emphasize disease progression outcomes, and establish model generalizability through external validations and the use of multicenter datasets.

Artificial intelligence in head and neck surgery: Potential applications and future perspectives

Artificial intelligence (AI) has the potential to improve the surgical treatment of patients with head and neck cancer. AI algorithms can analyse a wide range of data, including images, voice, molecular expression and raw clinical data. In the field of oncology, there are numerous AI practical applications, including diagnostics and treatment. AI can also develop predictive models to assess prognosis, overall survival, the likelihood of occult metastases, risk of complications and hospital length of stay.

Predicting Colonic Neoplasia Surgical Complications: A Machine Learning Approach

BACKGROUND

A range of statistical approaches have been used to help predict outcomes associated with colectomy. The multifactorial nature of complications suggests that machine learning algorithms may be more accurate in determining postoperative outcomes by detecting nonlinear associations, which are not readily measured by traditional statistics.

OBJECTIVE

The aim of this study was to investigate the utility of machine learning algorithms to predict complications in patients undergoing colectomy for colonic neoplasia.

DESIGN

Retrospective analysis using decision tree, random forest, and artificial neural network classifiers to predict postoperative outcomes.

SETTINGS

National Inpatient Sample database (2003-2017).

PATIENTS

Adult patients who underwent elective colectomy with anastomosis for neoplasia.

MAIN OUTCOME MEASURES

Performance was quantified using sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve to predict the incidence of anastomotic leak, prolonged length of stay, and inpatient mortality.

RESULTS

A total of 14,935 patients (4731 laparoscopic, 10,204 open) were included. They had an average age of 67 ± 12.2 years, and 53% of patients were women. The 3 machine learning models successfully identified patients who developed the measured complications. Although differences between model performances were largely insignificant, the neural network scored highest for most outcomes: predicting anastomotic leak, area under the receiver operating characteristic curve 0.88/0.93 (open/laparoscopic, 95% CI, 0.73-0.92/0.80-0.96); prolonged length of stay, area under the receiver operating characteristic curve 0.84/0.88 (open/laparoscopic, 95% CI, 0.82-0.85/0.85-0.91); and inpatient mortality, area under the receiver operating characteristic curve 0.90/0.92 (open/laparoscopic, 95% CI, 0.85-0.96/0.86-0.98).

LIMITATIONS

The patients from the National Inpatient Sample database may not be an accurate sample of the population of all patients undergoing colectomy for colonic neoplasia and does not account for specific institutional and patient factors.

CONCLUSIONS

Machine learning predicted postoperative complications in patients with colonic neoplasia undergoing colectomy with good performance. Although validation using external data and optimization of data quality will be required, these machine learning tools show great promise in assisting surgeons with risk-stratification of perioperative care to improve postoperative outcomes. See Video Abstract .

PREDICCIN DE LAS COMPLICACIONES QUIRRGICAS DE LA NEOPLASIA DE COLON UN ENFOQUE DE MODELO DE APRENDIZAJE AUTOMTICO

ANTECEDENTES:Se han utilizado una variedad de enfoques estadísticos para ayudar a predecir los resultados asociados con la colectomía. La naturaleza multifactorial de las complicaciones sugiere que los algoritmos de aprendizaje automático pueden ser más precisos en determinar los resultados posoperatorios al detectar asociaciones no lineales, que generalmente no se miden en las estadísticas tradicionales.OBJETIVO:El objetivo de este estudio fue investigar la utilidad de los algoritmos de aprendizaje automático para predecir complicaciones en pacientes sometidos a colectomía por neoplasia de colon.DISEÑO:Análisis retrospectivo utilizando clasificadores de árboles de decisión, bosques aleatorios y redes neuronales artificiales para predecir los resultados posoperatorios.AJUSTE:Base de datos de la Muestra Nacional de Pacientes Hospitalizados (2003-2017).PACIENTES:Pacientes adultos sometidos a colectomía electiva con anastomosis por neoplasia.INTERVENCIONES:N/A.PRINCIPALES MEDIDAS DE RESULTADO:El rendimiento se cuantificó utilizando la sensibilidad, especificidad, precisión y la característica operativa del receptor del área bajo la curva para predecir la incidencia de fuga anastomótica, duración prolongada de la estancia hospitalaria y mortalidad de los pacientes hospitalizados.RESULTADOS:Se incluyeron un total de 14.935 pacientes (4.731 laparoscópicos, 10.204 abiertos). Presentaron una edad promedio de 67 ± 12,2 años y el 53% eran mujeres. Los tres modelos de aprendizaje automático identificaron con éxito a los pacientes que desarrollaron las complicaciones medidas. Aunque las diferencias entre el rendimiento del modelo fueron en gran medida insignificantes, la red neuronal obtuvo la puntuación más alta para la mayoría de los resultados: predicción de fuga anastomótica, característica operativa del receptor del área bajo la curva 0,88/0,93 (abierta/laparoscópica, IC del 95%: 0,73-0,92/0,80-0,96); duración prolongada de la estancia hospitalaria, característica operativa del receptor del área bajo la curva 0,84/0,88 (abierta/laparoscópica, IC del 95%: 0,82-0,85/0,85-0,91); y mortalidad de pacientes hospitalizados, característica operativa del receptor del área bajo la curva 0,90/0,92 (abierto/laparoscópico, IC del 95%: 0,85-0,96/0,86-0,98).LIMITACIONES:Los pacientes de la base de datos de la Muestra Nacional de Pacientes Hospitalizados pueden no ser una muestra precisa de la población de todos los pacientes sometidos a colectomía por neoplasia de colon y no tienen en cuenta factores institucionales y específicos del paciente.CONCLUSIONES:El aprendizaje automático predijo con buen rendimiento las complicaciones postoperatorias en pacientes con neoplasia de colon sometidos a colectomía. Aunque será necesaria la validación mediante datos externos y la optimización de la calidad de los datos, estas herramientas de aprendizaje automático son muy prometedoras para ayudar a los cirujanos con la estratificación de riesgos de la atención perioperatoria para mejorar los resultados posoperatorios. (Traducción-Dr. Fidel Ruiz Healy ).

Semilicoisoflavone B induces oral cancer cell apoptosis by targeting claspin and ATR-Chk1 signaling pathways

The prevalence of oral squamous cell carcinoma (OSCC) is increasing worldwide mainly due to poor oral hygiene and unrestricted lifestyle. Advanced-stage OSCC is associated with poor prognosis and a 5-year survival rate of only 30%-50%. The present study was designed to investigate the anticancer effect and mode of action of Glycyrrhiza-derived semilicoisoflavone B (SFB) in 5-fluorourasil (5FU)-resistant human OSCC cell lines. The study findings revealed that SFB significantly reduces OSCC cell viability and colony formation ability by arresting cell cycle at the G2/M and S phases and reducing the expressions of key cell cycle regulators including cyclin A, cyclin B, CDC2, and CDK2. The compound caused a significant induction in the percentage of nuclear condensation and apoptotic cells in OSCC. Regarding pro-apoptotic mode of action, SFB was found to increase Fas-associated death domain and death receptor 5 expressions and reduce decoy receptor 2 expression, indicating involvement of extrinsic pathway. Moreover, SFB was found to increase pro-apoptotic Bim expression and reduce anti-apoptotic Bcl-2 and Bcl-xL expressions, indicating involvement of intrinsic pathway. Moreover, SFB-mediated induction in cleaved caspases 3, 8, and 9 and cleaved poly(ADP-ribose) polymerase confirmed the induction of caspase-mediated apoptotic pathways. Regarding upstream signaling pathway, SFB was found to reduce extracellular signal regulated kinase 1/2 (ERK) phosphorylation to execute its pro-apoptotic activity. The Human Apoptotic Array findings revealed that SFB suppresses claspin expression, which in turn caused reduced phosphorylation of ATR, checkpoint kinase 1 (Chk1), Wee1, and CDC25C, indicating disruption of ATR-Chk1 signaling pathway by SFB. Taken together, these findings indicate that SFB acts as a potent anticancer compound against 5FU-resistant OSCC by modulating mitogen-activated protein kinase (MAPK) and ATR-Chk1 signaling pathways.

Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.

Prediction of postoperative recurrence of oral cancer by artificial intelligence model: Multilayer perceptron

BACKGROUND

Postoperative recurrence of oral cancer is an important factor affecting the prognosis of patients. Artificial intelligence is used to establish a machine learning model to predict the risk of postoperative recurrence of oral cancer.

METHODS

The information of 387 patients with postoperative oral cancer were collected to establish the multilayer perceptron (MLP) model. The comprehensive variable model was compared with the characteristic variable model, and the MLP model was compared with other models to evaluate the sensitivity of different models in the prediction of postoperative recurrence of oral cancer.

RESULTS

The overall performance of the MLP model under comprehensive variable input was the best.

CONCLUSION

The MLP model has good sensitivity to predict postoperative recurrence of oral cancer, and the predictive model with variable input training is better than that with characteristic variable input.

Facial nerve palsy following parotid gland surgery: A machine learning prediction outcome approach

Introduction

Machine learning (ML)-based facial nerve injury (FNI) forecasting grounded on multicentric data has not been released up to now. Three distinct ML models, random forest (RF), K-nearest neighbor, and artificial neural network (ANN), for the prediction of FNI were evaluated in this mode.

Methods

A retrospective, longitudinal, multicentric study was performed, including patients who went through parotid gland surgery for benign tumors at three different university hospitals.

Results

Seven hundred and thirty-six patients were included. The most compelling aspects related to risk escalation of FNI were as follows: (1) location, in the mid-portion of the gland, near to or above the main trunk of the facial nerve and at the top part, over the frontal or the orbital branch of the facial nerve; (2) tumor volume in the anteroposterior axis; (3) the necessity to simultaneously dissect more than one level; and (4) the requirement of an extended resection compared to a lesser extended resection. By contrast, in accordance with the ML analysis, the size of the tumor (>3 cm), as well as gender and age did not result in a determining favor in relation to the risk of FNI.

Discussion

The findings of this research conclude that ML models such as RF and ANN may serve evidence-based predictions from multicentric data regarding the risk of FNI.

Conclusion

Along with the advent of ML technology, an improvement of the information regarding the potential risks of FNI associated with patients before each procedure may be achieved with the implementation of clinical, radiological, histological, and/or cytological data.

The Use of Artificial Intelligence Algorithms in the Prognosis and Detection of Lymph Node Involvement in Head and Neck Cancer and Possible Impact in the Development of Personalized Therapeutic Strategy: A Systematic Review

Given the increasingly important role that the use of artificial intelligence algorithms is taking on in the medical field today (especially in oncology), the purpose of this systematic review is to analyze the main reports on such algorithms applied for the prognostic evaluation of patients with head and neck malignancies. The objective of this paper is to examine the currently available literature in the field of artificial intelligence applied to head and neck oncology, particularly in the prognostic evaluation of the patient with this kind of tumor, by means of a systematic review. The paper exposes an overview of the applications of artificial intelligence in deriving prognostic information related to the prediction of survival and recurrence and how these data may have a potential impact on the choice of therapeutic strategy, making it increasingly personalized. This systematic review was written following the PRISMA 2020 guidelines.

A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma

One of the most prevalent cancers is oral squamous cell carcinoma, and preventing mortality from this disease primarily depends on early detection. Clinicians will greatly benefit from automated diagnostic techniques that analyze a patient's histopathology images to identify abnormal oral lesions. A deep learning framework was designed with an intermediate layer between feature extraction layers and classification layers for classifying the histopathological images into two categories, namely, normal and oral squamous cell carcinoma. The intermediate layer is constructed using the proposed swarm intelligence technique called the Modified Gorilla Troops Optimizer. While there are many optimization algorithms used in the literature for feature selection, weight updating, and optimal parameter identification in deep learning models, this work focuses on using optimization algorithms as an intermediate layer to convert extracted features into features that are better suited for classification. Three datasets comprising 2784 normal and 3632 oral squamous cell carcinoma subjects are considered in this work. Three popular CNN architectures, namely, InceptionV2, MobileNetV3, and EfficientNetB3, are investigated as feature extraction layers. Two fully connected Neural Network layers, batch normalization, and dropout are used as classification layers. With the best accuracy of 0.89 among the examined feature extraction models, MobileNetV3 exhibits good performance. This accuracy is increased to 0.95 when the suggested Modified Gorilla Troops Optimizer is used as an intermediary layer.

Artificial Intelligence Modeling to Predict Periprosthetic Infection and Explantation following Implant-Based Reconstruction

BACKGROUND

Despite improvements in prosthesis design and surgical techniques, periprosthetic infection and explantation rates following implant-based reconstruction (IBR) remain relatively high. Artificial intelligence is an extremely powerful predictive tool that involves machine learning (ML) algorithms. We sought to develop, validate, and evaluate the use of ML algorithms to predict complications of IBR.

METHODS

A comprehensive review of patients who underwent IBR from January of 2018 to December of 2019 was conducted. Nine supervised ML algorithms were developed to predict periprosthetic infection and explantation. Patient data were randomly divided into training (80%) and testing (20%) sets.

RESULTS

The authors identified 481 patients (694 reconstructions) with a mean ± SD age of 50.0 ± 11.5 years, mean ± SD body mass index of 26.7 ± 4.8 kg/m 2 , and median follow-up time of 16.1 months (range, 11.9 to 3.2 months). Periprosthetic infection developed in 113 of the reconstructions (16.3%), and explantation was required with 82 (11.8%) of them. ML demonstrated good discriminatory performance in predicting periprosthetic infection and explantation (area under the receiver operating characteristic curve, 0.73 and 0.78, respectively), and identified nine and 12 significant predictors of periprosthetic infection and explantation, respectively.

CONCLUSIONS

ML algorithms trained using readily available perioperative clinical data accurately predict periprosthetic infection and explantation following IBR. The authors' findings support incorporating ML models into perioperative assessment of patients undergoing IBR to provide data-driven, patient-specific risk assessment to aid individualized patient counseling, shared decision-making, and presurgical optimization.

Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives

Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.

Machine learning in laryngeal cancer: A pilot study to predict oncological outcomes and the role of adverse features

BACKGROUND

Laryngeal carcinoma (LC) remains a significant economic and emotional problem to the healthcare system and severe social morbidity. New tools as Machine Learning could allow clinicians to develop accurate and reproducible treatments.

METHODS

This study aims to evaluate the performance of a ML-algorithm in predicting 1- and 3-year overall survival (OS) in a cohort of patients surgical treated for LC. Moreover, the impact of different adverse features on prognosis will be investigated. Data was collected on oncological FU of 132 patients. A retrospective review was performed to create a dataset of 23 variables for each patient.

RESULTS

The decision-tree algorithm is highly effective in predicting the prognosis, with a 95% accuracy in predicting the 1-year survival and 82.5% in 3-year survival; The measured AUC area is 0.886 at 1-year Test and 0.871 at 3-years Test. The measured AUC area is 0.917 at 1-year Training set and 0.964 at 3-years Training set. Factors that affected 1yOS are: LNR, type of surgery, and subsite. The most significant variables at 3yOS are: number of metastasis, perineural invasion and Grading.

CONCLUSIONS

The integration of ML in medical practices could revolutionize our approach on cancer pathology.

Development and Assessment of Machine Learning Models for Individualized Risk Assessment of Mastectomy Skin Flap Necrosis

OBJECTIVE

To develop, validate, and evaluate ML algorithms for predicting MSFN.

BACKGROUND

MSFN is a devastating complication that causes significant distress to patients and physicians by prolonging recovery time, compromising surgical outcomes, and delaying adjuvant therapy.

METHODS

We conducted comprehensive review of all consecutive patients who underwent mastectomy and immediate implant-based reconstruction from January 2018 to December 2019. Nine supervised ML algorithms were developed to predict MSFN. Patient data were partitioned into training (80%) and testing (20%) sets.

RESULTS

We identified 694 mastectomies with immediate implant-based reconstruction in 481 patients. The patients had a mean age of 50 ± 11.5 years, years, a mean body mass index of 26.7 ± 4.8 kg/m 2 , and a median follow-up time of 16.1 (range, 11.9-23.2) months. MSFN developed in 6% (n = 40) of patients. The random forest model demonstrated the best discriminatory performance (area under curve, 0.70), achieved a mean accuracy of 89% (95% confidence interval, 83-94), and identified 10 predictors of MSFN. Decision curve analysis demonstrated that ML models have a superior net benefit regardless of the probability threshold. Higher body mass index, older age, hypertension, subpectoral device placement, nipple-sparing mastectomy, axillary nodal dissection, and no acellular dermal matrix use were all independently associated with a higher risk of MSFN.

CONCLUSIONS

ML algorithms trained on readily available perioperative clinical data can accurately predict the occurrence of MSFN and aid in individualized patient counseling, preoperative optimization, and surgical planning to reduce the risk of this devastating complication.

Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP

Nasopharyngeal cancer (NPC) has a unique histopathology compared with other head and neck cancers. Individual NPC patients may attain different outcomes. This study aims to build a prognostic system by combining a highly accurate machine learning model (ML) model with explainable artificial intelligence to stratify NPC patients into low and high chance of survival groups. Explainability is provided using Local Interpretable Model Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) techniques. A total of 1094 NPC patients were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database for model training and internal validation. We combined five different ML algorithms to form a uniquely stacked algorithm. The predictive performance of the stacked algorithm was compared with a state-of-the-art algorithm-extreme gradient boosting (XGBoost) to stratify the NPC patients into chance of survival groups. We validated our model with temporal validation (n = 547) and geographic external validation (Helsinki University Hospital NPC cohort, n = 60). The developed stacked predictive ML model showed an accuracy of 85.9% while the XGBoost had 84.5% after the training and testing phases. This demonstrated that both XGBoost and the stacked model showed comparable performance. External geographic validation of XGBoost model showed a c-index of 0.74, accuracy of 76.7%, and area under curve of 0.76. The SHAP technique revealed that age of the patient at diagnosis, T-stage, ethnicity, M-stage, marital status, and grade were among the prominent input variables in decreasing order of significance for the overall survival of NPC patients. LIME showed the degree of reliability of the prediction made by the model. In addition, both techniques showed how each feature contributed to the prediction made by the model. LIME and SHAP techniques provided personalized protective and risk factors for each NPC patient and unraveled some novel non-linear relationships between input features and survival chance. The examined ML approach showed the ability to predict the chance of overall survival of NPC patients. This is important for effective treatment planning care and informed clinical decisions. To enhance outcome results, including survival in NPC, ML may aid in planning individualized therapy for this patient population.

Development of a Machine Learning Model to Predict Recurrence of Oral Tongue Squamous Cell Carcinoma

Despite diagnostic advancements, the development of reliable prognostic systems for assessing the risk of cancer recurrence still remains a challenge. In this study, we developed a novel framework to generate highly representative machine-learning prediction models for oral tongue squamous cell carcinoma (OTSCC) cancer recurrence. We identified cases of 5- and 10-year OTSCC recurrence from the SEER database. Four classification models were trained using the H₂O ai platform, whose performances were assessed according to their accuracy, recall, precision, and the area under the curve (AUC) of their receiver operating characteristic (ROC) curves. By evaluating Shapley additive explanation contribution plots, feature importance was studied. Of the 130,979 patients studied, 36,042 (27.5%) were female, and the mean (SD) age was 58.2 (13.7) years. The Gradient Boosting Machine model performed the best, achieving 81.8% accuracy and 97.7% precision for 5-year prediction. Moreover, 10-year predictions demonstrated 80.0% accuracy and 94.0% precision. The number of prior tumors, patient age, the site of cancer recurrence, and tumor histology were the most significant predictors. The implementation of our novel SEER framework enabled the successful identification of patients with OTSCC recurrence, with which highly accurate and sensitive prediction models were generated. Thus, we demonstrate our framework's potential for application in various cancers to build generalizable screening tools to predict tumor recurrence.

Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review

OBJECTIVES

In biomedical research, spin is the overinterpretation of findings, and it is a growing concern. To date, the presence of spin has not been evaluated in prognostic model research in oncology, including studies developing and validating models for individualized risk prediction.

STUDY DESIGN AND SETTING

We conducted a systematic review, searching MEDLINE and EMBASE for oncology-related studies that developed and validated a prognostic model using machine learning published between 1st January, 2019, and 5th September, 2019. We used existing spin frameworks and described areas of highly suggestive spin practices.

RESULTS

We included 62 publications (including 152 developed models; 37 validated models). Reporting was inconsistent between methods and the results in 27% of studies due to additional analysis and selective reporting. Thirty-two studies (out of 36 applicable studies) reported comparisons between developed models in their discussion and predominantly used discrimination measures to support their claims (78%). Thirty-five studies (56%) used an overly strong or leading word in their title, abstract, results, discussion, or conclusion.

CONCLUSION

The potential for spin needs to be considered when reading, interpreting, and using studies that developed and validated prognostic models in oncology. Researchers should carefully report their prognostic model research using words that reflect their actual results and strength of evidence.

Epidemiology and incidence of oral squamous cell carcinoma in the Iraqi population over 5 years (2014-2018)

Background

Oral squamous cell carcinoma is one of the most common and life-threatening neoplasms worldwide, and is responsible for approximately 90% of all oral malignancies.

Aim

This study was aimed at providing updated information on oral squamous cell carcinoma in all Iraqi governorates for the 5-year period from 2014 to 2018, including the annual incidence and demographic variables.

Materials and Methods

The total number of oral squamous cell carcinoma cases in Iraq, along with associated demographic information (age, sex, and site), for the 5-year period from 2014 to 2018 was obtained. The statistical analysis consisted of descriptive analysis, including frequency, percentage, and mean ± standard deviation. A χ 2 test was performed to compare frequencies between male and female patients, among age groups, and among different OSCC sites. The χ 2 test was also used to assess the association of each OSCC site with age and sex. The significance threshold was set at p < 0.05, and the confidence interval was set at 95%. The incidence rate of oral squamous cell carcinoma for each year was calculated by dividing the number of OSCC cases per year by the population of Iraq, then multiplying the result by 100,000.

Results

A total of 722 cases were recorded. Statistically, oral squamous cell carcinoma was found to be more prevalent in males and individuals over 40 years of age. The tongue was the most common site of occurrence. Lip squamous cell carcinoma cases were high in males. The incidence rate of oral squamous cell carcinoma was estimated to be 0.4 per 100,000 people.

Conclusion

Males and older people are at relatively higher risk of developing oral cancer. The tongue is the most affected site, but any site in the oral cavity may be involved. Further exploration of the causes of oral malignancy in Iraq is necessary to improve prevention strategies.

Applying interpretable machine learning algorithms to predict risk factors for permanent stoma in patients after TME

OBJECTIVE

The purpose of this study was to develop a machine learning model to identify preoperative and intraoperative high-risk factors and to predict the occurrence of permanent stoma in patients after total mesorectal excision (TME).

METHODS

A total of 1,163 patients with rectal cancer were included in the study, including 142 patients with permanent stoma. We collected 24 characteristic variables, including patient demographic characteristics, basic medical history, preoperative examination characteristics, type of surgery, and intraoperative information. Four machine learning algorithms including extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM) and k-nearest neighbor algorithm (KNN) were applied to construct the model and evaluate the model using k-fold cross validation method, ROC curve, calibration curve, decision curve analysis (DCA) and external validation.

RESULTS

The XGBoost algorithm showed the best performance among the four prediction models. The ROC curve results showed that XGBoost had a high predictive accuracy with an AUC value of 0.987 in the training set and 0.963 in the validation set. The k-fold cross-validation method was used for internal validation, and the XGBoost model was stable. The calibration curves showed high predictive power of the XGBoost model. DCA curves showed higher benefit rates for patients who received interventional treatment under the XGBoost model. The AUC value for the external validation set was 0.89, indicating that the XGBoost prediction model has good extrapolation.

CONCLUSION

The prediction model for permanent stoma in patients with rectal cancer derived from the XGBoost machine learning algorithm in this study has high prediction accuracy and clinical utility.

Empirical comparison of routinely collected electronic health record data for head and neck cancer-specific survival in machine-learnt prognostic models

BACKGROUND

Knowledge of the prognostic factors and performance of machine learning predictive models for 2-year cancer-specific survival (CSS) is limited in the head and neck cancer (HNC) population.

METHODS

Data from our facilities' oncology information system (OIS) collected for routine practice (OIS dataset, n = 430 patients) and research purposes (research dataset, n = 529 patients) were extracted on adults diagnosed between 2000 and 2017 with squamous cell carcinoma of the head and neck.

RESULTS

Machine learning demonstrated excellent performance (area under the curve, AUC) in the whole cohort (AUC = 0.97, research dataset), larynx cohort (AUC = 0.98, both datasets), and oropharynx cohort (AUC = 0.99, both datasets). Tumor site and T classification were identified as predictors of 2-year CSS in both datasets. Hypothyroidism and fitness for operation were further identified in the research dataset.

CONCLUSIONS

Datasets extracted from an OIS for routine clinical practice and research purposes demonstrated high utility for informing 2-year head and neck CSS.

Radiomics-based machine learning for the diagnosis of lymph node metastases in patients with head and neck cancer: Systematic review

Machine learning (ML) is increasingly used to detect lymph node (LN) metastases in head and neck (H&N) carcinoma. We systematically reviewed the literature on radiomic-based ML for the detection of pathological LNs in H&N cancer. A systematic review was conducted in PubMed, EMBASE, and the Cochrane Library. Baseline study characteristics and methodological quality items (modeling, performance evaluation, clinical utility, and transparency items) were extracted and evaluated. The qualitative synthesis is presented using descriptive statistics. Seven studies were included in this study. Overall, the methodological quality items were generally favorable for modeling (57% of studies). The studies were mostly unsuccessful in terms of transparency (85.7%), evaluation of clinical utility (71.3%), and assessment of generalizability employing independent or external validation (72.5%). ML may be able to predict LN metastases in H&N cancer. Further studies are warranted to improve the generalizability assessment, clinical utility evaluation, and transparency items.

Application of Deep Learning in Cancer Prognosis Prediction Model

As an important branch of artificial intelligence and machine learning, deep learning (DL) has been widely used in various aspects of cancer auxiliary diagnosis, among which cancer prognosis is the most important part. High-accuracy cancer prognosis is beneficial to the clinical management of patients with cancer. Compared with other methods, DL models can significantly improve the accuracy of prediction. Therefore, this article is a systematic review of the latest research on DL in cancer prognosis prediction. First, the data type, construction process, and performance evaluation index of the DL model are introduced in detail. Then, the current mainstream baseline DL cancer prognosis prediction models, namely, deep neural networks, convolutional neural networks, deep belief networks, deep residual networks, and vision transformers, including network architectures, the latest application in cancer prognosis, and their respective characteristics, are discussed. Next, some key factors that affect the predictive performance of the model and common performance enhancement techniques are listed. Finally, the limitations of the DL cancer prognosis prediction model in clinical practice are summarized, and the future research direction is prospected. This article could provide relevant researchers with a comprehensive understanding of DL cancer prognostic models and is expected to promote the research progress of cancer prognosis prediction.

Integrating Cutting-Edge Methods to Oral Cancer Screening, Analysis, and Prognosis

Oral cancer (OC) has become a significant barrier to health worldwide due to its high morbidity and mortality rates. OC is among the most prevalent types of cancer that affect the head and neck region, and the overall survival rate at 5 years is still around 50%. Moreover, it is a multifactorial malignancy instigated by genetic and epigenetic variabilities, and molecular heterogeneity makes it a complex malignancy. Oral potentially malignant disorders (OPMDs) are often the first warning signs of OC, although it is challenging to predict which cases will develop into malignancies. Visual oral examination and histological examination are still the standard initial steps in diagnosing oral lesions; however, these approaches have limitations that might lead to late diagnosis of OC or missed diagnosis of OPMDs in high-risk individuals. The objective of this review is to present a comprehensive overview of the currently used novel techniques viz., liquid biopsy, next-generation sequencing (NGS), microarray, nanotechnology, lab-on-a-chip (LOC) or microfluidics, and artificial intelligence (AI) for the clinical diagnostics and management of this malignancy. The potential of these novel techniques in expanding OC diagnostics and clinical management is also reviewed.

Identification of High-Risk Patients for Postoperative Myocardial Injury After CME Using Machine Learning: A 10-Year Multicenter Retrospective Study

Purpose

The occurrence of myocardial injury, a grave complication post complete mesocolic excision (CME), profoundly impacts the immediate and long-term prognosis of patients. The aim of this inquiry was to conceive a machine learning model that can recognize preoperative, intraoperative and postoperative high-risk factors and predict the onset of myocardial injury following CME.

Patients and Methods

This study included 1198 colon cancer patients, 133 of whom experienced myocardial injury after surgery. Thirty-six distinct variables were gathered, encompassing patient demographics, medical history, preoperative examination characteristics, surgery type, and intraoperative details. Four machine learning algorithms, namely, extreme gradient boosting (XGBoost), random forest (RF), multilayer perceptron (MLP), and k-nearest neighbor algorithm (KNN), were employed to fabricate the model, and k-fold cross-validation, ROC curve, calibration curve, decision curve analysis (DCA), and external validation were employed to evaluate it.

Results

Out of the four predictive models employed, the XGBoost algorithm demonstrated the best performance. The ROC curve findings indicated that the XGBoost model exhibited remarkable predictive accuracy, with an area under the curve (AUC) value of 0.997 in the training set and 0.956 in the validation set. For internal validation, the k-fold cross-validation method was utilized, and the XGBoost model was shown to be steady. Furthermore, the calibration curves demonstrated the XGBoost model's high predictive capability. The DCA curve revealed higher benefit rates for patients who underwent interventional treatment under the XGBoost model. The AUC value for the external validation set was 0.74, which indicated that the XGBoost prediction model possessed good extrapolative capacity.

Conclusion

The myocardial injury prediction model for patients undergoing CME that was developed using the XGBoost machine learning algorithm in this study demonstrates both high predictive accuracy and clinical utility.

The Use of Artificial Intelligence in Dentistry Practices

Artificial intelligence can be defined as "understanding human thinking and trying to develop computer processes that will produce a similar structure." Thus, it is an attempt by a programmed computer to think. According to a broader definition, artificial intelligence is a computer equipped with human intelligencespecific capacities such as acquiring information, perceiving, seeing, thinking, and making decisions. Quality demands in dental treatments have constantly been increasing in recent years. In parallel with this, using image-based methods and multimedia-supported explanation systems on the computer is becoming widespread to evaluate the available information. The use of artificial intelligence in dentistry will greatly contribute to the reduction of treatment times and the effort spent by the dentist, reduce the need for a specialist dentist, and give a new perspective to how dentistry is practiced. In this review, we aim to review the studies conducted with artificial intelligence in dentistry and to inform our dentists about the existence of this new technology.

Artificial Intelligence in Oral and Maxillofacial Surgery Education

Artificial intelligence has become ubiquitous with modern technology. Digital transformations are occurring in every field including medicine, surgery, and education. Computers and computer programs are getting sophisticated to form neural networks globally. These algorithms allow for sophisticated and complex pattern recognitions and make accurate predictions. This allows for both accurate diagnosis and prognostication in medicine and opens opportunities for medical and surgical education. Oral and Maxillofacial surgeons and OMS education like all of the surgery are adapting well to the world of AI, incorporating machine learning into simulation, and attaching sensors to master surgeons to understand motion economy.

Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach

Overall prediction of oral cavity squamous cell carcinoma (OCSCC) remains inadequate, as more than half of patients with oral cavity cancer are detected at later stages. It is generally accepted that the differential diagnosis of OCSCC is usually difficult and requires expertise and experience. Diagnosis from biopsy tissue is a complex process, and it is slow, costly, and prone to human error. To overcome these problems, a computer-aided diagnosis (CAD) approach was proposed in this work. A dataset comprising two categories, normal epithelium of the oral cavity (NEOR) and squamous cell carcinoma of the oral cavity (OSCC), was used. Feature extraction was performed from this dataset using four deep learning (DL) models (VGG16, AlexNet, ResNet50, and Inception V3) to realize artificial intelligence of medial things (AIoMT). Binary Particle Swarm Optimization (BPSO) was used to select the best features. The effects of Reinhard stain normalization on performance were also investigated. After the best features were extracted and selected, they were classified using the XGBoost. The best classification accuracy of 96.3% was obtained when using Inception V3 with BPSO. This approach significantly contributes to improving the diagnostic efficiency of OCSCC patients using histopathological images while reducing diagnostic costs.

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.

Machine learning to predict end stage kidney disease in chronic kidney disease

The purpose of this study was to assess the feasibility of machine learning (ML) in predicting the risk of end-stage kidney disease (ESKD) from patients with chronic kidney disease (CKD). Data were obtained from a longitudinal CKD cohort. Predictor variables included patients' baseline characteristics and routine blood test results. The outcome of interest was the presence or absence of ESKD by the end of 5 years. Missing data were imputed using multiple imputation. Five ML algorithms, including logistic regression, naïve Bayes, random forest, decision tree, and K-nearest neighbors were trained and tested using fivefold cross-validation. The performance of each model was compared to that of the Kidney Failure Risk Equation (KFRE). The dataset contained 748 CKD patients recruited between April 2006 and March 2008, with the follow-up time of 6.3 ± 2.3 years. ESKD was observed in 70 patients (9.4%). Three ML models, including the logistic regression, naïve Bayes and random forest, showed equivalent predictability and greater sensitivity compared to the KFRE. The KFRE had the highest accuracy, specificity, and precision. This study showed the feasibility of ML in evaluating the prognosis of CKD based on easily accessible features. Three ML models with adequate performance and sensitivity scores suggest a potential use for patient screenings. Future studies include external validation and improving the models with additional predictor variables.

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review

BACKGROUND

Describe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology.

METHODS

We conducted a systematic review in MEDLINE and Embase between 01/01/2019 and 05/09/2019, for studies developing a prognostic prediction model using machine learning methods in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Prediction model Risk Of Bias ASsessment Tool (PROBAST) and CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) to assess the methodological conduct of included publications. Results were summarised by modelling type: regression-, non-regression-based and ensemble machine learning models.

RESULTS

Sixty-two publications met inclusion criteria developing 152 models across all publications. Forty-two models were regression-based, 71 were non-regression-based and 39 were ensemble models. A median of 647 individuals (IQR: 203 to 4059) and 195 events (IQR: 38 to 1269) were used for model development, and 553 individuals (IQR: 69 to 3069) and 50 events (IQR: 17.5 to 326.5) for model validation. A higher number of events per predictor was used for developing regression-based models (median: 8, IQR: 7.1 to 23.5), compared to alternative machine learning (median: 3.4, IQR: 1.1 to 19.1) and ensemble models (median: 1.7, IQR: 1.1 to 6). Sample size was rarely justified (n = 5/62; 8%). Some or all continuous predictors were categorised before modelling in 24 studies (39%). 46% (n = 24/62) of models reporting predictor selection before modelling used univariable analyses, and common method across all modelling types. Ten out of 24 models for time-to-event outcomes accounted for censoring (42%). A split sample approach was the most popular method for internal validation (n = 25/62, 40%). Calibration was reported in 11 studies. Less than half of models were reported or made available.

CONCLUSIONS

The methodological conduct of machine learning based clinical prediction models is poor. Guidance is urgently needed, with increased awareness and education of minimum prediction modelling standards. Particular focus is needed on sample size estimation, development and validation analysis methods, and ensuring the model is available for independent validation, to improve quality of machine learning based clinical prediction models.

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review

The application of deep machine learning, a subfield of artificial intelligence, has become a growing area of interest in predictive medicine in recent years. The deep machine learning approach has been used to analyze imaging and radiomics and to develop models that have the potential to assist the clinicians to make an informed and guided decision that can assist to improve patient outcomes. Improved prognostication of oral squamous cell carcinoma (OSCC) will greatly benefit the clinical management of oral cancer patients. This review examines the recent development in the field of deep learning for OSCC prognostication. The search was carried out using five different databases-PubMed, Scopus, OvidMedline, Web of Science, and Institute of Electrical and Electronic Engineers (IEEE). The search was carried time from inception until 15 May 2021. There were 34 studies that have used deep machine learning for the prognostication of OSCC. The majority of these studies used a convolutional neural network (CNN). This review showed that a range of novel imaging modalities such as computed tomography (or enhanced computed tomography) images and spectra data have shown significant applicability to improve OSCC outcomes. The average specificity, sensitivity, area under receiving operating characteristics curve [AUC]), and accuracy for studies that used spectra data were 0.97, 0.99, 0.96, and 96.6%, respectively. Conversely, the corresponding average values for these parameters for computed tomography images were 0.84, 0.81, 0.967, and 81.8%, respectively. Ethical concerns such as privacy and confidentiality, data and model bias, peer disagreement, responsibility gap, patient-clinician relationship, and patient autonomy have limited the widespread adoption of these models in daily clinical practices. The accumulated evidence indicates that deep machine learning models have great potential in the prognostication of OSCC. This approach offers a more generic model that requires less data engineering with improved accuracy.

Machine Learning Algorithms as a Computer-Assisted Decision Tool for Oral Cancer Prognosis and Management Decisions: A Systematic Review

INTRODUCTION

Despite multiple prognostic indicators described for oral cavity squamous cell carcinoma (OCSCC), its management still continues to be a matter of debate. Machine learning is a subset of artificial intelligence that enables computers to learn from historical data, gather insights, and make predictions about new data using the model learned. Therefore, it can be a potential tool in the field of head and neck cancer.

METHODS

We conducted a systematic review.

RESULTS

A total of 81 manuscripts were revised, and 46 studies met the inclusion criteria. Of these, 38 were excluded for the following reasons: use of a classical statistical method (N = 16), nonspecific for OCSCC (N = 15), and not being related to OCSCC survival (N = 7). In total, 8 studies were included in the final analysis.

CONCLUSIONS

ML has the potential to significantly advance research in the field of OCSCC. Advantages are related to the use and training of ML models because of their capability to continue training continuously when more data become available. Future ML research will allow us to improve and democratize the application of algorithms to improve the prediction of cancer prognosis and its management worldwide.

Application of machine learning in oral and maxillofacial surgery

Oral and maxillofacial anatomy is extremely complex, and medical imaging is critical in the diagnosis and treatment of soft and bone tissue lesions. Hence, there exists accumulating imaging data without being properly utilized over the last decades. As a result, problems are emerging regarding how to integrate and interpret a large amount of medical data and alleviate clinicians’ workload. Recently, artificial intelligence has been developing rapidly to analyze complex medical data, and machine learning is one of the specific methods of achieving this goal, which is based on a set of algorithms and previous results. Machine learning has been considered useful in assisting early diagnosis, treatment planning, and prognostic estimation through extracting key features and building mathematical models by computers. Over the past decade, machine learning techniques have been applied to the field of oral and maxillofacial surgery and increasingly achieved expert-level performance. Thus, we hold a positive attitude towards developing machine learning for reducing the number of medical errors, improving the quality of patient care, and optimizing clinical decision-making in oral and maxillofacial surgery. In this review, we explore the clinical application of machine learning in maxillofacial cysts and tumors, maxillofacial defect reconstruction, orthognathic surgery, and dental implant and discuss its current problems and solutions.

Applications of artificial intelligence in dentistry: A comprehensive review

OBJECTIVE

To perform a comprehensive review of the use of artificial intelligence (AI) and machine learning (ML) in dentistry, providing the community with a broad insight on the different advances that these technologies and tools have produced, paying special attention to the area of esthetic dentistry and color research.

MATERIALS AND METHODS

The comprehensive review was conducted in MEDLINE/PubMed, Web of Science, and Scopus databases, for papers published in English language in the last 20 years.

RESULTS

Out of 3871 eligible papers, 120 were included for final appraisal. Study methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other ML techniques (n = 32), which were mainly applied to disease identification, image segmentation, image correction, and biomimetic color analysis and modeling.

CONCLUSIONS

The insight provided by the present work has reported outstanding results in the design of high-performance decision support systems for the aforementioned areas. The future of digital dentistry goes through the design of integrated approaches providing personalized treatments to patients. In addition, esthetic dentistry can benefit from those advances by developing models allowing a complete characterization of tooth color, enhancing the accuracy of dental restorations.

CLINICAL SIGNIFICANCE

The use of AI and ML has an increasing impact on the dental profession and is complementing the development of digital technologies and tools, with a wide application in treatment planning and esthetic dentistry procedures.

Deep Learning on Oral Squamous Cell Carcinoma Ex Vivo Fluorescent Confocal Microscopy Data: A Feasibility Study

BACKGROUND

Ex vivo fluorescent confocal microscopy (FCM) is a novel and effective method for a fast-automatized histological tissue examination. In contrast, conventional diagnostic methods are primarily based on the skills of the histopathologist. In this study, we investigated the potential of convolutional neural networks (CNNs) for automatized classification of oral squamous cell carcinoma via ex vivo FCM imaging for the first time.

MATERIAL AND METHODS

Tissue samples from 20 patients were collected, scanned with an ex vivo confocal microscope immediately after resection, and investigated histopathologically. A CNN architecture (MobileNet) was trained and tested for accuracy.

RESULTS

The model achieved a sensitivity of 0.47 and specificity of 0.96 in the automated classification of cancerous tissue in our study.

CONCLUSION

In this preliminary work, we trained a CNN model on a limited number of ex vivo FCM images and obtained promising results in the automated classification of cancerous tissue. Further studies using large sample sizes are warranted to introduce this technology into clinics.

Survival and risk factors of adenosquamous carcinoma in the oral and maxillofacial region: a population-based study

BACKGROUND

The features and prognosis of adenosquamous carcinoma (ASC) in oral and maxillofacial region have not thoroughly investigated, the purpose of this study is to describe clinicopathologic characteristics, treatment, and prognostic factors of this disease.

METHODS

The data of 276 patients diagnosed with ASC in oral and maxillofacial region between 1975 and 2016 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The prognostic factors influencing overall survival (OS) and disease-specific survival (DSS) were identified by the Kaplan-Meier analysis and Cox regression analysis. The nomograms for OS and DSS were constructed to predict the prognosis of these patients.

RESULTS

Of 276 included patients, 62.7% were male and 37.3% were female, with an average age at diagnosis of 63.5 years. The most common primary site is oral cavity (170/276), followed by salivary gland (106/276). The 3-, and 5-year OS of patients with ASC in oral and maxillofacial region were 49.0% and 38.9%, while the 3-, and 5-year DSS were 67.7%, and 60.4%, respectively. Patients who underwent surgery had longer OS (mOS: 58 m vs. 8 m) and DSS (mDSS: 193 m vs. 18 m) than those who did not. Age, AJCC-T/N/M category as well as surgery were independently associated with OS. Advanced T stage, distant metastases, and surgery were independent factors for DSS. The prognostic nomograms for OS and DSS were constructed, and the C-indexes were 0.71 (95% CI 0.66-0.76) and 0.76 (95% CI 0.67-0.85), respectively.

CONCLUSION

Surgery was the favorable prognostic factor for both OS and DSS among patients with ASC in oral and maxillofacial region.

Mitigating Burnout in an Oncological Unit: A Scoping Review

Objectives: The purpose of this study was to provide a scoping review on how to address and mitigate burnout in the profession of clinical oncology. Also, it examines how artificial intelligence (AI) can mitigate burnout in oncology. Methods: We searched Ovid Medline, PubMed, Scopus, and Web of Science, for articles that examine how to address burnout in oncology. Results: A total of 17 studies were found to examine how burnout in oncology can be mitigated. These interventions were either targeted at individuals (oncologists) or organizations where the oncologists work. The organizational interventions include educational (psychosocial and mindfulness-based course), art therapies and entertainment, team-based training, group meetings, motivational package and reward, effective leadership and policy change, and staff support. The individual interventions include equipping the oncologists with adequate training that include-communication skills, well-being and stress management, burnout education, financial independence, relaxation, self-efficacy, resilience, hobby adoption, and work-life balance for the oncologists. Similarly, AI is thought to be poised to offer the potential to mitigate burnout in oncology by enhancing the productivity and performance of the oncologists, reduce the workload and provide job satisfaction, and foster teamwork between the caregivers of patients with cancer. Discussion: Burnout is common among oncologists and can be elicited from different types of situations encountered in the process of caring for patients with cancer. Therefore, for these interventions to achieve the touted benefits, combinatorial strategies that combine other interventions may be viable for mitigating burnout in oncology. With the potential of AI to mitigate burnout, it is important for healthcare providers to facilitate its use in daily clinical practices. Conclusion: These combinatorial interventions can ensure job satisfaction, a supportive working environment, job retention for oncologists, and improved patient care. These interventions could be integrated systematically into routine cancer care for a positive impact on quality care, patient satisfaction, the overall success of the oncological ward, and the health organizations at large.