Improving Prostate Cancer Detection with Breast Histopathology Images

Khan, Umair; Stürenberg, Carolin; Gencoglu, Oguzhan; Sandeman, Kevin; Heikkinen, Tuomas; Rannikko, Antti; Mirtti, Tuomas

doi:10.1007/978-3-030-23937-4_11

Cited by 13 publications

(6 citation statements)

References 15 publications

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“…Additionally, the simple labeling in binary detection tasks allows for deep learning methods to generalize across different organs where similar cancers form. 72 , 73 , 74 …”

Section: Clinical Applications For Cpathmentioning

confidence: 99%

Computational pathology: A survey review and the way forward

Hosseini,

Bejnordi,

Trinh

et al. 2024

Journal of Pathology Informatics

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“…Additionally, the simple labeling in binary detection tasks allows for deep learning methods to generalize across different organs where similar cancers form. 72 , 73 , 74 …”

Section: Clinical Applications For Cpathmentioning

confidence: 99%

Computational pathology: A survey review and the way forward

Hosseini,

Bejnordi,

Trinh

et al. 2024

Journal of Pathology Informatics

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“…the model, such as transfer learning [12,27] and domain adaptation [7,20], and manipulation of the data, which is the focus of this paper.…”

Section: Related Workmentioning

confidence: 99%

Primary Tumor and Inter-Organ Augmentations for Supervised Lymph Node Colon Adenocarcinoma Metastasis Detection

Tsirikoglou¹,

Stacke²,

Eilertsen³

et al. 2021

Preprint

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The scarcity of labeled data is a major bottleneck for developing accurate and robust deep learning-based models for histopathology applications. The problem is notably prominent for the task of metastasis detection in lymph nodes, due to the tissue's low tumor-to-nontumor ratio, resulting in labor-and time-intensive annotation processes for the pathologists. This work explores alternatives on how to augment the training data for colon carcinoma metastasis detection when there is limited or no representation of the target domain. Through an exhaustive study of cross-validated experiments with limited training data availability, we evaluate both an inter-organ approach utilizing already available data for other tissues, and an intra-organ approach, utilizing the primary tumor. Both these approaches result in little to no extra annotation effort. Our results show that these data augmentation strategies can be an efficient way of increasing accuracy on metastasis detection, but fore-most increase robustness.

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“…In last years, we observe a growing interest in the application of DL systems to support prostate cancer evaluation. In literature several related studies on prostate cancer were published [10][11][12][13][14] . Two main tasks can be distinguished: (a) cancer detection and segmentation, and (b) Gleason grading.…”

Section: Related Workmentioning

confidence: 99%

“…However, the basic assumption that glands not detected as healthy are cancerous does not hold, especially given the wide range of gland in clinical practice. Khan et al 14 showed that transfer learning based on the same domain can improve final segmentation results. They present decent results with an area under the curve (AUC) of 0.924 at the patch level.…”

Section: Related Workmentioning

confidence: 99%

Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer

Świderska-Chadaj

Bel

Blanchet

et al. 2020

Sci Rep

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Algorithms can improve the objectivity and efficiency of histopathologic slide analysis. In this paper, we investigated the impact of scanning systems (scanners) and cycle-GAn-based normalization on algorithm performance, by comparing different deep learning models to automatically detect prostate cancer in whole-slide images. Specifically, we compare U-Net, DenseNet and EfficientNet. Models were developed on a multi-center cohort with 582 WSIs and subsequently evaluated on two independent test sets including 85 and 50 WSIs, respectively, to show the robustness of the proposed method to differing staining protocols and scanner types. We also investigated the application of normalization as a pre-processing step by two techniques, the whole-slide image color standardizer (WSICS) algorithm, and a cycle-GAN based method. For the two independent datasets we obtained an AUC of 0.92 and 0.83 respectively. After rescanning the AUC improves to 0.91/0.88 and after style normalization to 0.98/0.97. In the future our algorithm could be used to automatically pre-screen prostate biopsies to alleviate the workload of pathologists. Prostate cancer is the most common cancer in men and the third most common tumor type worldwide 1,2. In 2018, 1.3 million new cases have been diagnosed (7.1% of all diagnosed cancers), and 28% of these patients died as a result of the disease 1. Prostate cancer is typically diagnosed through ultrasound-guided biopsy after initial suspicion has arisen through, for example, a prostate specific antigen (PSA) blood test. During the prostate biopsy procedure, 6-12 core samples are taken from a patient 3 resulting worldwide in more than 15 million specimens annually, which is expected to increase further with the aging of the population. All these specimens have to be evaluated by pathologists. However, in many countries there is a lack of pathologists which is only expected to increase in the years to come. Automating (part of) the evaluation of prostate biopsies might help mitigate the lack of clinical pathology. The histopathological analysis could be streamlined significantly if these negative slides (i.e. slides without pathology) could automatically be excluded without expelling any slides containing cancer. Significant progress has been made in this respect, revealing the huge potential of deep learning (DL) methods 4-6. In histopathology, deep learning based algorithms have been used to solve a variety of tasks, such as mitotic Figure detection 7 , lung adenocarcinoma segmentation 4 , glomeruli detection 8 or tissue analysis in colorectal cancer 9. However, histological slides from different institutions show heterogeneous appearance as a result of the different preparation and staining procedures (different colors, intensity, saturation) (Fig. 1). As a result, there is a high probability that a model trained on data from one medical center may not be applicable to slides from another center. The key challenge is to develop a system robust to a variety of biological, staining or scanning settings.

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Improving Prostate Cancer Detection with Breast Histopathology Images

Cited by 13 publications

References 15 publications

Computational pathology: A survey review and the way forward

Computational pathology: A survey review and the way forward

Primary Tumor and Inter-Organ Augmentations for Supervised Lymph Node Colon Adenocarcinoma Metastasis Detection

Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer

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