A clinical decision support system optimising adjuvant chemotherapy for colorectal cancers by integrating deep learning and pathological staging markers: a development and validation study

Kleppe, Andreas; Skrede, Ole-Johan; Raedt, Sepp de; Hveem, Tarjei S.; Askautrud, Hanne A.; Jacobsen, Jørn E; Church, David N.; Nesbakken, Arild; Shepherd, Neil A.; Novelli, Marco; Kerr, Rachel; Liestøl, Knut; Kerr, David; Danielsen, Håvard E.

doi:10.1016/s1470-2045(22)00391-6

Cited by 49 publications

(23 citation statements)

References 31 publications

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“…NCRT is of great significance for CRC treatment, especially for patients with rectal cancer. Adjuvant chemotherapy is primarily used in the patients who are classified as intermediate risk ( 148 ). However, most patients do not need additional chemotherapy, so an accurate clinical decision-making is particularly important.…”

Section: The Application Of Ai In Crc Treatmentmentioning

confidence: 99%

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Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect

et al. 2023

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In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.

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Section: The Application Of Ai In Crc Treatmentmentioning

confidence: 99%

“…When patients were classified as low risk, they could be exempted from NCRT. As a result, the survival rate of these patients improved significantly ( 148 ).…”

Section: The Application Of Ai In Crc Treatmentmentioning

confidence: 99%

Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect

et al. 2023

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“…Furthermore, there has also been an exponential growth in the number of new artificial intelligence (AI) approaches using deep learning (DL) in digital histopathology of cancer. [1][2][3][4] AI has been applied to numerous tasks based on information that can be extracted from histology slides, including cancer detection, 5,6 predicting the origin in cancer of unknown primary, 7 survival prediction, [8][9][10] genetic subtyping, 4,11,12 and prediction of treatment response. 13 These methods are valuable research tools, which are also being incorporated into clinical routines as diagnostic algorithms approved by regulatory entities.…”

Section: Introductionmentioning

confidence: 99%

Automated curation of large‐scale cancer histopathology image datasets using deep learning

Hilgers,

Ghaffari Laleh,

West

et al. 2024

Histopathology

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BackgroundArtificial intelligence (AI) has numerous applications in pathology, supporting diagnosis and prognostication in cancer. However, most AI models are trained on highly selected data, typically one tissue slide per patient. In reality, especially for large surgical resection specimens, dozens of slides can be available for each patient. Manually sorting and labelling whole‐slide images (WSIs) is a very time‐consuming process, hindering the direct application of AI on the collected tissue samples from large cohorts. In this study we addressed this issue by developing a deep‐learning (DL)‐based method for automatic curation of large pathology datasets with several slides per patient.MethodsWe collected multiple large multicentric datasets of colorectal cancer histopathological slides from the United Kingdom (FOXTROT, N = 21,384 slides; CR07, N = 7985 slides) and Germany (DACHS, N = 3606 slides). These datasets contained multiple types of tissue slides, including bowel resection specimens, endoscopic biopsies, lymph node resections, immunohistochemistry‐stained slides, and tissue microarrays. We developed, trained, and tested a deep convolutional neural network model to predict the type of slide from the slide overview (thumbnail) image. The primary statistical endpoint was the macro‐averaged area under the receiver operating curve (AUROCs) for detection of the type of slide.ResultsIn the primary dataset (FOXTROT), with an AUROC of 0.995 [95% confidence interval [CI]: 0.994–0.996] the algorithm achieved a high classification performance and was able to accurately predict the type of slide from the thumbnail image alone. In the two external test cohorts (CR07, DACHS) AUROCs of 0.982 [95% CI: 0.979–0.985] and 0.875 [95% CI: 0.864–0.887] were observed, which indicates the generalizability of the trained model on unseen datasets. With a confidence threshold of 0.95, the model reached an accuracy of 94.6% (7331 classified cases) in CR07 and 85.1% (2752 classified cases) for the DACHS cohort.ConclusionOur findings show that using the low‐resolution thumbnail image is sufficient to accurately classify the type of slide in digital pathology. This can support researchers to make the vast resource of existing pathology archives accessible to modern AI models with only minimal manual annotations.

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“…As previous studies have demonstrated, these slides can be effectively analyzed using deep learning algorithms to predict relapse risk [18][19][20][21][22][23][24][25][26] . Such algorithms have already been approved for colorectal cancer 27,28 in Europe, though their widespread adoption is yet to be realized. One possible reason for this delay could be the limited clinical validation against the standard of care 29 .…”

Section: Introductionmentioning

confidence: 99%

Multimodal histopathologic models stratify hormone receptor-positive early breast cancer

Boehm,

El Nahhas,

Marra

et al. 2024

Preprint

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For patients with hormone receptor-positive, early breast cancer without HER2 amplification, multigene expression assays including Oncotype DX (R) recurrence score (RS) have been clinically validated to identify patients who stand to derive added benefit from adjuvant cytotoxic chemotherapy. However, cost and turnaround time have limited its global adoption despite recommendation by practice guidelines. We investigated if routinely available hematoxylin and eosin (H&E)-stained pathology slides could act as a surrogate triaging data substrate by predicting RS using machine learning methods. We trained and validated a multimodal transformer model, Orpheus, using 6,203 patients across three independent cohorts, taking both H&E images and their corresponding synoptic text reports as input. We showed accurate inference of recurrence score from whole-slide images (r = 0.63 (95% C.I. 0.58 - 0.68); n = 1,029), the raw text of their corresponding reports (r = 0.58 (95% C.I. 0.51 - 0.64); n = 972), and their combination (r = 0.68 (95% C.I. 0.64 - 0.73); n = 964) as measured by Pearson's correlation. To predict high-risk disease (RS>25), our model achieved an area under the receiver operating characteristic curve (AUROC) of 0.89 (95% C.I. 0.83 - 0.94), and area under the precision recall curve (AUPRC) of 0.64 (95% C.I. 0.60 - 0.82), compared to 0.49 (95% C.I. 0.36 - 0.64) for an existing nomogram based on clinical and pathologic features. Moreover, our model generalizes well to external international cohorts, effectively identifying recurrence risk (r = 0.61, p < 10-4, n = 452; r = 0.60, p < 10-4, n = 575) and high-risk status (AUROC = 0.80, p < 10-4, AUPRC = 0.68, p < 10-4, n = 452; AUROC = 0.83, p < 10-4, AUPRC = 0.73, p < 10-4, n = 575) from whole-slide images. Probing the biologic underpinnings of the model decisions uncovered tumor cell size heterogeneity, immune cell infiltration, a proliferative transcription program, and stromal fraction as correlates of higher-risk predictions. We conclude that at an operating point of 94.4% precision and 33.3% recall, this model could help increase global adoption and shorten lag between resection and adjuvant therapy.

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A clinical decision support system optimising adjuvant chemotherapy for colorectal cancers by integrating deep learning and pathological staging markers: a development and validation study

Cited by 49 publications

References 31 publications

Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect

Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect

Automated curation of large‐scale cancer histopathology image datasets using deep learning

Multimodal histopathologic models stratify hormone receptor-positive early breast cancer

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