Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board

Kim, Minseok; Park, Ha Young; Kho, Bo‐Gun; Park, Cheol‐Kyu; Oh, Il‐Seok; Kim, Young‐Chul; Kim, Seok; Yun, Ju Sik; Song, Sang‐Yun; Na, Kook-Joo; Jeong, Jae‐Uk; Yoon, Myung Ha; Ahn, Sung‐Ja; Yoo, Su Woong; Kang, Sae-Ryung; Kwon, Seong; Bom, Hee‐Seung; Jang, Won Young; Kim, In-Young; Lee, Jong-Eun; Jeong, Won-Gi; Kim, Yun‐Hyeon; Lee, Taebum; Choi, Yoo‐Duk

doi:10.21037/tlcr.2020.04.11

Cited by 40 publications

(39 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Time limitation per case also represented a negative factor. A Korean study, published in 2020, retrospectively evaluated the level of agreement between IBM Watson for Oncology cognitive computing system and 405 LC-MDT recommendations concerning surgery, radiotherapy, chemo-radiotherapy, and palliative care [38].…”

Section: Quality Of Mtbmentioning

confidence: 99%

Virtual Multidisciplinary Tumor Boards: A Narrative Review Focused on Lung Cancer

Gebbia

Guarini

Piazza³

et al. 2021

Pulm Ther

View full text Add to dashboard Cite

To date, the virtual multidisciplinary tumor boards (vMTBs) are increasingly used to achieve high-quality treatment recommendations across health-care regions, which expands and develops the local MTB team to a regional or national expert network. This review describes the process of lung cancer-specific MTBs and the transition process from face-to-face tumor boards to virtual ones. The review also focuses on the project organization's description, advantages, and disadvantages. Semi-structured interviews identified five major themes for MTBs: current practice, attitudes, enablers, barriers, and benefits for the MTB. MTB teams exhibited positive responses to modeled data feedback. Virtualization reduces time spent for travel, allowing easier and timely patient discussions. This process requires a secure web platform to assure the respect of patients' privacy and presents the same unanswered prob-

show abstract

Section: Quality Of Mtbmentioning

confidence: 99%

Virtual Multidisciplinary Tumor Boards: A Narrative Review Focused on Lung Cancer

Gebbia

Guarini

Piazza³

et al. 2021

Pulm Ther

View full text Add to dashboard Cite

show abstract

“…Furthermore, AI technology is being used not only to interpret medical images but also to predict the prognosis of disease. AI technology is also being expanded to determine physical examination findings and treatment plans [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

An Assistive Role of a Machine Learning Network in Diagnosis of Middle Ear Diseases

Byun

et al. 2021

JCM

View full text Add to dashboard Cite

The present study aimed to develop a machine learning network to diagnose middle ear diseases with tympanic membrane images and to identify its assistive role in the diagnostic process. The medical records of subjects who underwent ear endoscopy tests were reviewed. From these records, 2272 diagnostic tympanic membranes images were appropriately labeled as normal, otitis media with effusion (OME), chronic otitis media (COM), or cholesteatoma and were used for training. We developed the “ResNet18 + Shuffle” network and validated the model performance. Seventy-one representative cases were selected to test the final accuracy of the network and resident physicians. We asked 10 resident physicians to make diagnoses from tympanic membrane images with and without the help of the machine learning network, and the change of the diagnostic performance of resident physicians with the aid of the answers from the machine learning network was assessed. The devised network showed a highest accuracy of 97.18%. A five-fold validation showed that the network successfully diagnosed ear diseases with an accuracy greater than 93%. All resident physicians were able to diagnose middle ear diseases more accurately with the help of the machine learning network. The increase in diagnostic accuracy was up to 18% (1.4% to 18.4%). The machine learning network successfully classified middle ear diseases and was assistive to clinicians in the interpretation of tympanic membrane images.

show abstract

“…For the better treatment outcome, several clinical decisionsupport systems such as Watson for Oncology are being applied in several cancer fields [44]. And deep learning has the potential to be extremely useful in medicine, particularly in the interpretation of medical images such as computed tomography, PET and histopathological slides [45], [46].…”

Section: Discussionmentioning

confidence: 99%

PET-Based Deep-Learning Model for Predicting Prognosis of Patients With Non-Small Cell Lung Cancer

Kang

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

Despite recent advances in precision medicine, lung cancer remains the leading cause of cancer-related mortality worldwide. To determine the prognosis of non-small cell lung cancer (NSCLC), which accounts for 85% of lung cancer, comprehensive analysis of various clinical factors are necessary. Artificial intelligence can help physician quickly identify key information from the vast amount of medical information including positron emission tomography (PET) scan. In this study, we compared image featureextraction models and survival estimation models to determine an optimal model that effectively extracts features related to survival time. We collected PET image data of 2,685 patients who were diagnosed with NSCLC and received treatment at the Chonnam National University Hwasun Hospital in South Korea over a period of seven years. We compared four convolution neural network models, DenseNet, NFNet, EfficientNet, and ResNet, and two survival estimation models, CoxPH and CoxCC. The best model was determined based on criteria such as C-index, mean absolute error (MAE), classification accuracy for survival status, and learning time. The results show that DenseNet combined with CoxPH delivers superior performance for most of the criteria. In particular, the MAE for this combination was very low (391.50 days), and the model predicted survival days well; the five-year classification accuracy, which can indicate a cure for cancer, was high (95%). Extracted features were visualized using Score-CAM; thus, the learning process of the model could be understood without requiring expert knowledge of PET. In addition, the learning time for this model was short.

show abstract

Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board

Cited by 40 publications

References 19 publications

Virtual Multidisciplinary Tumor Boards: A Narrative Review Focused on Lung Cancer

Virtual Multidisciplinary Tumor Boards: A Narrative Review Focused on Lung Cancer

An Assistive Role of a Machine Learning Network in Diagnosis of Middle Ear Diseases

PET-Based Deep-Learning Model for Predicting Prognosis of Patients With Non-Small Cell Lung Cancer

Contact Info

Product

Resources

About