Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

Hamamoto, Ryuji; Suvarna, Kruthi; Yamada, Masayoshi; Kobayashi, Kazuma; Shinkai, Norio; Miyake, Makito; Takahashi, Manabu; Jinnai, Shunichi; Shimoyama, Ryo; Sakai, Akira; Takasawa, Ken; Bolatkan, Amina; Shozu, Kanto; Dozen, Ai; Machino, Hidenori; Takahashi, Satoshi; Asada, Ken; Komatsu, Masaaki; Sese, Jun; Kaneko, Syuzo

doi:10.3390/cancers12123532

Cited by 136 publications

(91 citation statements)

References 174 publications

(174 reference statements)

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“…In this section, the main algorithms used to implement diagnostic and prognostic predictors starting from molecular variables in RCC will be summarized. However, extensive and recent reviews of the history of AI and its applications in medicine and oncology were carried out by Hamamoto et al [ 12 ] and Hamet et al [ 13 ]. More specific applications in urologic oncology have been carried out [ 14 ], including those based on radiological images for diagnostic and prognostic purposes in RCC [ 15 ] or those for prediction of RCC incidence over time [ 16 , 17 ].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors

Giulietti¹,

Cecati²,

Sabanovic³

et al. 2021

Diagnostics

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The increasing availability of molecular data provided by next-generation sequencing (NGS) techniques is allowing improvement in the possibilities of diagnosis and prognosis in renal cancer. Reliable and accurate predictors based on selected gene panels are urgently needed for better stratification of renal cell carcinoma (RCC) patients in order to define a personalized treatment plan. Artificial intelligence (AI) algorithms are currently in development for this purpose. Here, we reviewed studies that developed predictors based on AI algorithms for diagnosis and prognosis in renal cancer and we compared them with non-AI-based predictors. Comparing study results, it emerges that the AI prediction performance is good and slightly better than non-AI-based ones. However, there have been only minor improvements in AI predictors in terms of accuracy and the area under the receiver operating curve (AUC) over the last decade and the number of genes used had little influence on these indices. Furthermore, we highlight that different studies having the same goal obtain similar performance despite the fact they use different discriminating genes. This is surprising because genes related to the diagnosis or prognosis are expected to be tumor-specific and independent of selection methods and algorithms. The performance of these predictors will be better with the improvement in the learning methods, as the number of cases increases and by using different types of input data (e.g., non-coding RNAs, proteomic and metabolic). This will allow for more precise identification, classification and staging of cancerous lesions which will be less affected by interpathologist variability.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors

Giulietti¹,

Cecati²,

Sabanovic³

et al. 2021

Diagnostics

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“…Since image segmentation can automatically recognize the location and size of an object in pixels ( Supplementary Figure S2 ), it plays a remarkable role in image analyses in various fields [ 17 , 18 ]. For instance, cityscapes segmentation is an essential technique for visual scene understanding and contributes to self-driving car technology [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Model-Agnostic Method for Thoracic Wall Segmentation in Fetal Ultrasound Videos

et al. 2020

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The application of segmentation methods to medical imaging has the potential to create novel diagnostic support models. With respect to fetal ultrasound, the thoracic wall is a key structure on the assessment of the chest region for examiners to recognize the relative orientation and size of structures inside the thorax, which are critical components in neonatal prognosis. In this study, to improve the segmentation performance of the thoracic wall in fetal ultrasound videos, we proposed a novel model-agnostic method using deep learning techniques: the Multi-Frame + Cylinder method (MFCY). The Multi-frame method (MF) uses time-series information of ultrasound videos, and the Cylinder method (CY) utilizes the shape of the thoracic wall. To evaluate the achieved improvement, we performed segmentation using five-fold cross-validation on 538 ultrasound frames in the four-chamber view (4CV) of 256 normal cases using U-net and DeepLabv3+. MFCY increased the mean values of the intersection over union (IoU) of thoracic wall segmentation from 0.448 to 0.493 for U-net and from 0.417 to 0.470 for DeepLabv3+. These results demonstrated that MFCY improved the segmentation performance of the thoracic wall in fetal ultrasound videos without altering the network structure. MFCY is expected to facilitate the development of diagnostic support models in fetal ultrasound by providing further accurate segmentation of the thoracic wall.

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“…The Human Genome Project, completed in 2003 by an international consortium, is considered one of the most important achievements for mankind in the 21st century [ 1 ]. With the completion of this project, medical science has entered a new era known as the post-genome era [ 2 ]. In the latter half of the 20th century and into the 21st century, with the development of molecular biology, efforts to elucidate diseases at the molecular level were actively pursued worldwide.…”

mentioning

confidence: 99%

“…In such an academic field, if scientific discussions involve only limited data, then it becomes difficult to grasp the complete picture of a phenomenon, and it is easy to fall into a state of “you can’t see the forest for the trees.” In contrast, the analysis of large-scale data using AI technology will make it possible to elucidate biological phenomena more objectively and without omission; it is expected to contribute greatly to the advancement of medicine. In fact, more than 60 AI-powered medical devices are approved by the Food and Drug Administration (FDA) in the United States, and the use of AI in the medical field is trending worldwide [ 2 ].…”

mentioning

confidence: 99%

“…As shown in Figure 1 , there are three areas in which AI technology is currently implemented in the medical field: medical image analysis, omics analysis, and natural language processing [ 2 , 5 , 6 , 7 , 8 , 9 ]. In this Special Issue on the “Application of Artificial Intelligence for Medical Research”, articles were presented in the fields of medical image analysis and omics analysis, among these three areas.…”

mentioning

confidence: 99%

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