Artificial Intelligence in Cancer Pathology and Prognosis

Topic Information

Dear Colleague,

Artificial Intelligence (AI) has transformed the landscape of cancer pathology and prognosis, presenting unparalleled opportunities for early detection, precise diagnosis, and tailored treatment strategies. This topic delves into the latest advancements in AI technologies, encompassing machine learning algorithms, deep learning models, and computer vision techniques, applied across various domains of cancer pathology. From the analysis of histopathological images to the identification of biomarkers and the prediction of patient outcomes, AI-driven approaches have exhibited remarkable efficacy in enhancing the accuracy and efficiency of both cancer diagnosis and prognosis. This topic elucidates key research findings, addresses prevailing challenges, and outlines future directions for leveraging AI to augment cancer care and management, ultimately resulting in improved patient outcomes and advancements in precision oncology. All papers from related journals are welcome for peer review.

Dr. Hamid Khayyam
Prof. Dr. Ali Hekmatnia
Dr. Rahele Kafieh
Dr. Ali Jamali
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Cancers cancers	4.5	8.0	2009	17.4 Days	CHF 2900	Submit
Current Oncology curroncol	2.8	3.3	1994	19.8 Days	CHF 2200	Submit
Diagnostics diagnostics	3.0	4.7	2011	20.3 Days	CHF 2600	Submit
Diseases diseases	2.9	0.8	2013	21.4 Days	CHF 1800	Submit
Onco onco	-	-	2021	27.8 Days	CHF 1000	Submit

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Published Papers (3 papers)

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All Cancers Current Oncology Diagnostics Diseases Onco

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23 pages, 11538 KiB  

Open AccessArticle

A Novel Deep Learning Model for Breast Tumor Ultrasound Image Classification with Lesion Region Perception

by Jinzhu Wei, Haoyang Zhang and Jiang Xie

Curr. Oncol. 2024, 31(9), 5057-5079; https://doi.org/10.3390/curroncol31090374 - 28 Aug 2024

Cited by 2 | Viewed by 2481

Abstract

Multi-task learning (MTL) methods are widely applied in breast imaging for lesion area perception and classification to assist in breast cancer diagnosis and personalized treatment. A typical paradigm of MTL is the shared-backbone network architecture, which can lead to information sharing conflicts and [...] Read more.

Multi-task learning (MTL) methods are widely applied in breast imaging for lesion area perception and classification to assist in breast cancer diagnosis and personalized treatment. A typical paradigm of MTL is the shared-backbone network architecture, which can lead to information sharing conflicts and result in the decline or even failure of the main task’s performance. Therefore, extracting richer lesion features and alleviating information-sharing conflicts has become a significant challenge for breast cancer classification. This study proposes a novel Multi-Feature Fusion Multi-Task (MFFMT) model to effectively address this issue. Firstly, in order to better capture the local and global feature relationships of lesion areas, a Contextual Lesion Enhancement Perception (CLEP) module is designed, which integrates channel attention mechanisms with detailed spatial positional information to extract more comprehensive lesion feature information. Secondly, a novel Multi-Feature Fusion (MFF) module is presented. The MFF module effectively extracts differential features that distinguish between lesion-specific characteristics and the semantic features used for tumor classification, and enhances the common feature information of them as well. Experimental results on two public breast ultrasound imaging datasets validate the effectiveness of our proposed method. Additionally, a comprehensive study on the impact of various factors on the model’s performance is conducted to gain a deeper understanding of the working mechanism of the proposed framework. Full article

(This article belongs to the Topic Artificial Intelligence in Cancer Pathology and Prognosis)

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10 pages, 1354 KiB  

Open AccessArticle

Could CT Radiomic Analysis of Benign Adrenal Incidentalomas Suggest the Need for Further Endocrinological Evaluation?

by Alessandro Toniolo, Elena Agostini, Filippo Ceccato, Irene Tizianel, Giulio Cabrelle, Amalia Lupi, Alessia Pepe, Cristina Campi, Emilio Quaia and Filippo Crimì

Curr. Oncol. 2024, 31(9), 4917-4926; https://doi.org/10.3390/curroncol31090364 - 25 Aug 2024

Cited by 1 | Viewed by 1138

Abstract

We studied the application of CT texture analysis in adrenal incidentalomas with baseline characteristics of benignity that are highly suggestive of adenoma to find whether there is a correlation between the extracted features and clinical data. Patients with hormonal hypersecretion may require medical [...] Read more.

We studied the application of CT texture analysis in adrenal incidentalomas with baseline characteristics of benignity that are highly suggestive of adenoma to find whether there is a correlation between the extracted features and clinical data. Patients with hormonal hypersecretion may require medical attention, even if it does not cause any symptoms. A total of 206 patients affected by adrenal incidentaloma were retrospectively enrolled and divided into non-functioning adrenal adenomas (NFAIs, n = 115) and mild autonomous cortisol secretion (MACS, n = 91). A total of 136 texture parameters were extracted in the unenhanced phase for each volume of interest (VOI). Random Forest was used in the training and validation cohorts to test the accuracy of CT textural features and cortisol-related comorbidities in identifying MACS patients. Twelve parameters were retained in the Random Forest radiomic model, and in the validation cohort, a high specificity (81%) and positive predictive value (74%) were achieved. Notably, if the clinical data were added to the model, the results did not differ. Radiomic analysis of adrenal incidentalomas, in unenhanced CT scans, could screen with a good specificity those patients who will need a further endocrinological evaluation for mild autonomous cortisol secretion, regardless of the clinical information about the cortisol-related comorbidities. Full article

(This article belongs to the Topic Artificial Intelligence in Cancer Pathology and Prognosis)

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28 pages, 963 KiB  

Open AccessReview

Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition

by Hamidreza Ashayeri, Navid Sobhi, Paweł Pławiak, Siamak Pedrammehr, Roohallah Alizadehsani and Ali Jafarizadeh

Cancers 2024, 16(11), 2138; https://doi.org/10.3390/cancers16112138 - 4 Jun 2024

Cited by 3 | Viewed by 2664

Abstract

Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, [...] Read more.

Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, are addressed by transfer learning (TL), leveraging pre-existing models for faster training. TL shows potential in genetic research, improving tasks like gene expression analysis, mutation detection, genetic syndrome recognition, and genotype–phenotype association. This review explores the role of TL in overcoming challenges in mutation detection, genetic syndrome detection, gene expression, or phenotype–genotype association. TL has shown effectiveness in various aspects of genetic research. TL enhances the accuracy and efficiency of mutation detection, aiding in the identification of genetic abnormalities. TL can improve the diagnostic accuracy of syndrome-related genetic patterns. Moreover, TL plays a crucial role in gene expression analysis in order to accurately predict gene expression levels and their interactions. Additionally, TL enhances phenotype–genotype association studies by leveraging pre-trained models. In conclusion, TL enhances AI efficiency by improving mutation prediction, gene expression analysis, and genetic syndrome detection. Future studies should focus on increasing domain similarities, expanding databases, and incorporating clinical data for better predictions. Full article

(This article belongs to the Topic Artificial Intelligence in Cancer Pathology and Prognosis)

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