Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

Surgery Surgery

Preoperative Prognosis Assessment of Lumbar Spinal Surgery for Low Back Pain and Sciatica Patients based on Multimodalities and Multimodal Learning

ArXiv Preprint

Low back pain (LBP) and sciatica may require surgical therapy when they are symptomatic of severe pain. However, there is no effective measures to evaluate the surgical outcomes in advance. This work combined elements of Eastern medicine and machine learning, and developed a preoperative assessment tool to predict the prognosis of lumbar spinal surgery in LBP and sciatica patients. Standard operative assessments, traditional Chinese medicine body constitution assessments, planned surgical approach, and vowel pronunciation recordings were collected and stored in different modalities. Our work provides insights into leveraging modality combinations, multimodals, and fusion strategies. The interpretability of models and correlations between modalities were also inspected. Based on the recruited 105 patients, we found that combining standard operative assessments, body constitution assessments, and planned surgical approach achieved the best performance in 0.81 accuracy. Our approach is effective and can be widely applied in general practice due to simplicity and effective.

Li-Chin Chen, Jung-Nien Lai, Hung-En Lin, Hsien-Te Chen, Kuo-Hsuan Hung, Yu Tsao

2023-03-16

Public Health Public Health

Clinicomics-guided distant metastasis prediction in breast cancer via artificial intelligence.

In BMC cancer

BACKGROUND : Breast cancer has become the most common malignant tumour worldwide. Distant metastasis is one of the leading causes of breast cancer-related death. To verify the performance of clinicomics-guided distant metastasis risk prediction for breast cancer via artificial intelligence and to investigate the accuracy of the created prediction models for metachronous distant metastasis, bone metastasis and visceral metastasis.

METHODS : We retrospectively enrolled 6703 breast cancer patients from 2011 to 2016 in our hospital. The figures of magnetic resonance imaging scanning and ultrasound were collected, and the figures features of distant metastasis in breast cancer were detected. Clinicomics-guided nomogram was proven to be with significant better ability on distant metastasis prediction than the nomogram constructed by only clinical or radiographic data.

RESULTS : Three clinicomics-guided prediction nomograms on distant metastasis, bone metastasis and visceral metastasis were created and validated. These models can potentially guide metachronous distant metastasis screening and lead to the implementation of individualized prophylactic therapy for breast cancer patients.

CONCLUSION : Our study is the first study to make cliniomics a reality. Such cliniomics strategy possesses the development potential in artificial intelligence medicine.

Zhang Chao, Qi Lisha, Cai Jun, Wu Haixiao, Xu Yao, Lin Yile, Li Zhijun, Chekhonin Vladimir P, Peltzer Karl, Cao Manqing, Yin Zhuming, Wang Xin, Ma Wenjuan

2023-Mar-14

Artificial Intelligence, Breast Cancer, Image, Metastasis, Prediction

Pathology Pathology

The NCI Imaging Data Commons as a platform for reproducible research in computational pathology

ArXiv Preprint

Objective: Reproducibility is critical for translating machine learning-based (ML) solutions in computational pathology (CompPath) into practice. However, an increasing number of studies report difficulties in reproducing ML results. The NCI Imaging Data Commons (IDC) is a public repository of >120 cancer image collections, including >38,000 whole-slide images (WSIs), that is designed to be used with cloud-based ML services. Here, we explore the potential of the IDC to facilitate reproducibility of CompPath research. Materials and Methods: The IDC realizes the FAIR principles: All images are encoded according to the DICOM standard, persistently identified, discoverable via rich metadata, and accessible via open tools. Taking advantage of this, we implemented two experiments in which a representative ML-based method for classifying lung tumor tissue was trained and/or evaluated on different datasets from the IDC. To assess reproducibility, the experiments were run multiple times with independent but identically configured sessions of common ML services. Results: The AUC values of different runs of the same experiment were generally consistent and in the same order of magnitude as a similar, previously published study. However, there were occasional small variations in AUC values of up to 0.044, indicating a practical limit to reproducibility. Discussion and conclusion: By realizing the FAIR principles, the IDC enables other researchers to reuse exactly the same datasets. Cloud-based ML services enable others to run CompPath experiments in an identically configured computing environment without having to own high-performance hardware. The combination of both makes it possible to approach the reproducibility limit.

Daniela P. Schacherer, Markus D. Herrmann, David A. Clunie, Henning Höfener, William Clifford, William J. R. Longabaugh, Steve Pieper, Ron Kikinis, Andrey Fedorov, André Homeyer

2023-03-16

Radiology Radiology

Transcranial focused ultrasound-mediated unbinding of phenytoin from plasma proteins for suppression of chronic temporal lobe epilepsy in a rodent model.

In Scientific reports ; h5-index 158.0

The efficacy of many anti-epileptic drugs, including phenytoin (PHT), is reduced by plasma protein binding (PPB) that sequesters therapeutically active drug molecules within the bloodstream. An increase in systemic dose elevates the risk of drug side effects, which demands an alternative technique to increase the unbound concentration of PHT in a region-specific manner. We present a low-intensity focused ultrasound (FUS) technique that locally enhances the efficacy of PHT by transiently disrupting its binding to albumin. We first identified the acoustic parameters that yielded the highest PHT unbinding from albumin among evaluated parameter sets using equilibrium dialysis. Then, rats with chronic mesial temporal lobe epilepsy (mTLE) received four sessions of PHT injection, each followed by 30 min of FUS delivered to the ictal region, across 2 weeks. Two additional groups of mTLE rats underwent the same procedure, but without receiving PHT or FUS. Assessment of electrographic seizure activities revealed that FUS accompanying administration of PHT effectively reduced the number and mean duration of ictal events compared to other conditions, without damaging brain tissue or the blood-brain barrier. Our results demonstrated that the FUS technique enhanced the anti-epileptic efficacy of PHT in a chronic mTLE rodent model by region-specific PPB disruption.

Kim Evgenii, Kim Hyun-Chul, Van Reet Jared, Böhlke Mark, Yoo Seung-Schik, Lee Wonhye

2023-Mar-13

General General

The predictive model for COVID-19 pandemic plastic pollution by using deep learning method.

In Scientific reports ; h5-index 158.0

Pandemic plastics (e.g., masks, gloves, aprons, and sanitizer bottles) are global consequences of COVID-19 pandemic-infected waste, which has increased significantly throughout the world. These hazardous wastes play an important role in environmental pollution and indirectly spread COVID-19. Predicting the environmental impacts of these wastes can be used to provide situational management, conduct control procedures, and reduce the COVID-19 effects. In this regard, the presented study attempted to provide a deep learning-based predictive model for forecasting the expansion of the pandemic plastic in the megacities of Iran. As a methodology, a database was gathered from February 27, 2020, to October 10, 2021, for COVID-19 spread and personal protective equipment usage in this period. The dataset was trained and validated using training (80%) and testing (20%) datasets by a deep neural network (DNN) procedure to forecast pandemic plastic pollution. Performance of the DNN-based model is controlled by the confusion matrix, receiver operating characteristic (ROC) curve, and justified by the k-nearest neighbours, decision tree, random forests, support vector machines, Gaussian naïve Bayes, logistic regression, and multilayer perceptron methods. According to the comparative modelling results, the DNN-based model was found to predict more accurately than other methods and have a significant predominance over others with a lower errors rate (MSE = 0.024, RMSE = 0.027, MAPE = 0.025). The ROC curve analysis results (overall accuracy) indicate the DNN model (AUC = 0.929) had the highest score among others.

Nanehkaran Yaser A, Licai Zhu, Azarafza Mohammad, Talaei Sona, Jinxia Xu, Chen Junde, Derakhshani Reza

2023-Mar-13

General General

Differentially expressed genes in systemic sclerosis: Towards predictive medicine with new molecular tools for clinicians.

In Autoimmunity reviews ; h5-index 77.0

Systemic sclerosis (SSc) is a rare and chronic autoimmune disease characterized by a pathogenic triad of immune dysregulation, vasculopathy, and progressive fibrosis. Clinical tools commonly used to assess patients, such as the modified Rodnan skin score, difference between limited or diffuse forms of skin involvement, presence of lung, heart or kidney involvement, or of various autoantibodies, are important prognostic factors, but still fail to reflect the large heterogeneity of the disease. SSc treatment options are diverse, ranging from conventional drugs to autologous hematopoietic stem cell transplantation, and predicting response is challenging. Genome-wide technologies, such as high throughput microarray analyses and RNA sequencing, allow accurate, unbiased, and broad assessment of alterations in expression levels of multiple genes. In recent years, many studies have shown robust changes in the gene expression profiles of SSc patients compared to healthy controls, mainly in skin tissues and peripheral blood cells. The objective analysis of molecular patterns in SSc is a powerful tool that can further classify SSc patients with similar clinical phenotypes and help predict response to therapy. In this review, we describe the journey from the first discovery of differentially expressed genes to the identification of enriched pathways and intrinsic subsets identified in SSc, using machine learning algorithms. Finally, we discuss the use of these new tools to predict the efficacy of various treatments, including stem cell transplantation. We suggest that the use of RNA gene expression-based classifications according to molecular subsets may bring us one step closer to precision medicine in Systemic Sclerosis.

Keret Shiri, Rimar Doron, Lansiaux Pauline, Feldman Erik, Lescoat Alain, Milman Neta, Farge Dominique

2023-Mar-12

Autologous hematopoietic stem cell transplantation, Intrinsic subsets, Machine learning, RNA gene expression, Systemic sclerosis