Artificial intelligence and digital pathology: Opportunities and implications for immuno-oncology

Sobhani, Faranak; Robinson, Ruth; Hamidinekoo, Azam; Roxanis, Ioannis; Somaiah, Navita; Yuan, Yinyin

doi:10.1016/j.bbcan.2021.188520

Cited by 29 publications

(23 citation statements)

References 110 publications

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“…( digital pathology [Title]) AND ( artificial intelligence [Title]) currently reports 17 works [ 6 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ].…”

Section: Towards the Revolution Of The Digital Pathology And Artificial Intelligencementioning

confidence: 99%

“…The first aspect is that when scholars talk about artificial intelligence in digital pathology, they refer more to the aspects of imaging and essentially histological imaging. The second aspect is that scholars begin to identify interesting perspectives—for example, in oncology [ 15 , 25 ] or in toxicology [ 14 , 24 ]. The last aspect, in line with our objective, is that scholars are interrogating the work to be completed in a prospective way [ 25 , 26 , 27 , 28 ].…”

Section: Towards the Revolution Of The Digital Pathology And Artificial Intelligencementioning

confidence: 99%

“…Important perspectives have been identified for example: Through a review [ 15 ] on immuno-oncology. In a Special Issue [ 14 ] and in an opinion article [ 24 ] of a working group in pathological diagnostics in toxicology.…”

Section: Towards the Revolution Of The Digital Pathology And Artificial Intelligencementioning

confidence: 99%

“…There is currently optimism about the opportunities of ANNs, as has been highlighted above in the studies [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. There are several difficulties with deploying AI tools in practice depending on the expectance and the objectives of the pathologist.…”

Section: Towards the Revolution Of The Digital Pathology And Artificial Intelligencementioning

confidence: 99%

“…A quick look at PubMed with the following search key: (digital pathology [Title]) AND (artificial intelligence [Title]) currently reports 17 works [6,[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28].…”

Section: Towards the Revolution Of The Digital Pathology And Artificial Intelligence 41 What Is Emerging In The Application Of The Artifimentioning

confidence: 99%

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Artificial Intelligence in Digital Pathology: What Is the Future? Part 1: From the Digital Slide Onwards

Giovagnoli

Giansanti

2021

Healthcare

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This commentary aims to address the field of Artificial intelligence (AI) in Digital Pathology (DP) both in terms of the global situation and research perspectives. It has four polarities. First, it revisits the evolutions of digital pathology with particular care to the two fields of the digital cytology and the digital histology. Second, it illustrates the main fields in the employment of AI in DP. Third, it looks at the future directions of the research challenges from both a clinical and technological point of view. Fourth, it discusses the transversal problems among these challenges and implications and introduces the immediate work to implement.

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“…( digital pathology [Title]) AND ( artificial intelligence [Title]) currently reports 17 works [ 6 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ].…”

Section: Towards the Revolution Of The Digital Pathology And Artificial Intelligencementioning

confidence: 99%

Section: Towards the Revolution Of The Digital Pathology And Artificial Intelligencementioning

confidence: 99%

Section: Towards the Revolution Of The Digital Pathology And Artificial Intelligencementioning

confidence: 99%

Section: Towards the Revolution Of The Digital Pathology And Artificial Intelligencementioning

confidence: 99%

Section: Towards the Revolution Of The Digital Pathology And Artificial Intelligence 41 What Is Emerging In The Application Of The Artifimentioning

confidence: 99%

See 3 more Smart Citations

Artificial Intelligence in Digital Pathology: What Is the Future? Part 1: From the Digital Slide Onwards

Giovagnoli

Giansanti

2021

Healthcare

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Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology

2023

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Background: The currently available immunotherapies already changed the strategy how many cancers are treated from first to last line. Understanding even the most complex heterogeneity in tumor tissue and mapping the spatial cartography of the tumor immunity allows the best and optimized selection of immune modulating agents to (re-)activate the patient's immune system and direct it against the individual cancer in the most effective way.Recent Findings: Primary cancer and metastases maintain a high degree of plasticity to escape any immune surveillance and continue to evolve depending on many intrinsic and extrinsic factors In the field of immune-oncology (IO) immune modulating agents are recognized as practice changing therapeutic modalities. Recent studies have shown that an optimal and lasting efficacy of IO therapeutics depends on the understanding of the spatial communication network and functional context of immune and cancer cells within the tumor microenvironment. Artificial intelligence (AI) provides an insight into the immune-cancer-network through the visualization of very complex tumor and immune interactions in cancer tissue specimens and allows the computer-assisted development and clinical validation of such digital biomarker. Conclusions:The successful implementation of AI-supported digital biomarker solutions guides the clinical selection of effective immune therapeutics based on the retrieval and visualization of spatial and contextual information from cancer tissue images and standardized data. As such, computational pathology (CP) turns into "precision pathology" delivering individual therapy response prediction. Precision Pathology does not only include digital and computational solutions but also high levels of standardized processes in the routine histopathology workflow and the use of mathematical tools to support clinical and diagnostic decisions as the basic principle of a "precision oncology".

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Weakly supervised annotation‐free cancer detection and prediction of genotype in routine histopathology

Schrammen

Laleh

Echle

et al. 2021

The Journal of Pathology

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Deep learning is a powerful tool in computational pathology: it can be used for tumor detection and for predicting genetic alterations based on histopathology images alone. Conventionally, tumor detection and prediction of genetic alterations are two separate workflows. Newer methods have combined them, but require complex, manually engineered computational pipelines, restricting reproducibility and robustness. To address these issues, we present a new method for simultaneous tumor detection and prediction of genetic alterations: The Slide-Level Assessment Model (SLAM) uses a single off-the-shelf neural network to predict molecular alterations directly from routine pathology slides without any manual annotations, improving upon previous methods by automatically excluding normal and non-informative tissue regions. SLAM requires only standard programming libraries and is conceptually simpler than previous approaches. We have extensively validated SLAM for clinically relevant tasks using two large multicentric cohorts of colorectal cancer patients, Darmkrebs: Chancen der Verhütung durch Screening (DACHS) from Germany and Yorkshire Cancer Research Bowel Cancer Improvement Programme (YCR-BCIP) from the UK. We show that SLAM yields reliable slide-level classification of tumor presence with an area under the receiver operating curve (AUROC) of 0.980 (confidence interval 0.975, 0.984; n = 2,297 tumor and n = 1,281 normal slides). In addition, SLAM can detect microsatellite instability (MSI)/mismatch repair deficiency (dMMR) or microsatellite stability/mismatch repair proficiency with an AUROC of 0.909 (0.888, 0.929; n = 2,039 patients) and BRAF mutational status with an AUROC of 0.821 (0.786, 0.852; n = 2,075 patients). The improvement with respect to previous methods was validated in a large external testing cohort in which MSI/dMMR status was detected with an AUROC of 0.900 (0.864, 0.931; n = 805 patients). In addition, SLAM provides human-interpretable visualization maps, enabling the analysis of multiplexed network predictions by human experts. In summary, SLAM is a new simple and powerful method for computational pathology that could be applied to multiple disease contexts.

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Artificial intelligence and digital pathology: Opportunities and implications for immuno-oncology

Cited by 29 publications

References 110 publications

Artificial Intelligence in Digital Pathology: What Is the Future? Part 1: From the Digital Slide Onwards

Artificial Intelligence in Digital Pathology: What Is the Future? Part 1: From the Digital Slide Onwards

Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology

Weakly supervised annotation‐free cancer detection and prediction of genotype in routine histopathology

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