Reconstructing virtual large slides can improve the accuracy and consistency of tumor bed evaluation for breast cancer after neoadjuvant therapy

Han, Donglin; Liao, Jun; Zhang, Meng; Qin, Chenchen; Han, Mengxue; Wu, Chun Ying; Li, Jinze; Yao, Jianhua; Liu, Yueping

doi:10.1186/s13000-022-01219-2

Cited by 2 publications

(1 citation statement)

References 23 publications

(21 reference statements)

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“…Indeed, it was recently demonstrated by Han et al that the use of such a stitching algorithm offered clear benefits for pathologists in a clinical workflow. 4 In their study on breast cancer histopathology evaluation, Han et al compared their traditional method for evaluating multiple fragments (stack splicing) with an alternative method in which different tissue fragments were digitally "stitched" into an artificial WMS. In addition to a 37-45% reduction in reading time, depending on the number of fragments for a given case, the use of artificial WMS also improved the intraclass correlation coefficient between pathologists for measuring tumour characteristics.…”

Section: Purposementioning

confidence: 99%

PythoStitcher: an iterative approach for stitching digitized tissue fragments into full resolution whole-mount reconstructions

Schouten

Litjens

2023

Medical Imaging 2023: Digital and Computational Pathology

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In histopathology, whole-mount sections (WMS) can be utilized to capture entire cross-sections of excised tissue, thereby reducing the number of slides per case, preserving tissue context, and minimizing cutting artefacts. Additionally, the use of WMS allows for easier correlation of histology and pre-operative imaging. However, in digital pathology, WMS are not frequently acquired due to the limited availability of whole-slide scanners that can scan double-width glass slides. Moreover, whole-mounts may still be too large to fit on a single double-width glass slide, and archival material is typically stored as tissue fragments. In this work, we present PythoStitcher, an improved version of the AutoStitcher algorithm for automatically stitching multiple tissue fragments and constructing a full-resolution WMS. PythoStitcher is based on a genetic algorithm that iteratively optimizes the affine transformation matrix for each tissue fragment using the Euclidean distance between corner points of neighbouring fragments. We demonstrate the efficiency and generalisability of PythoStitcher in a pilot validation of ten prostatectomy cases with four fragments and two oesophagectomy cases with two fragments. On average, PythoStitcher obtained the final stitching result in 60 seconds (range 51-73) for the prostatectomy cases and 68 seconds (range 43-94) for the oesophagectomy cases. The stitching accuracy was objectively evaluated by computing the average Euclidean distance between all corner points from the edges of neighbouring fragments. For the reconstructed WMS at full resolution, the average residual registration error was 1.6 mm (range 0.5-3.2) for the prostatectomy cases and 2.1 mm (range 0.5-3.8) for the oesophagectomy cases. The key strengths of PythoStitcher include its computational efficiency, the ability to reconstruct the WMS in full resolution, and an open-source implementation in Python.

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Section: Purposementioning

confidence: 99%

PythoStitcher: an iterative approach for stitching digitized tissue fragments into full resolution whole-mount reconstructions

Schouten

Litjens

2023

Medical Imaging 2023: Digital and Computational Pathology

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Full resolution reconstruction of whole-mount sections from digitized individual tissue fragments

Schouten,

van der Laak,

van Ginneken

et al. 2024

Sci Rep

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Whole-mount sectioning is a technique in histopathology where a full slice of tissue, such as a transversal cross-section of a prostate specimen, is prepared on a large microscope slide without further sectioning into smaller fragments. Although this technique can offer improved correlation with pre-operative imaging and is paramount for multimodal research, it is not commonly employed due to its technical difficulty, associated cost and cumbersome integration in (digital) pathology workflows. In this work, we present a computational tool named PythoStitcher which reconstructs artificial whole-mount sections from digitized tissue fragments, thereby bringing the benefits of whole-mount sections to pathology labs currently unable to employ this technique. Our proposed algorithm consists of a multi-step approach where it (i) automatically determines how fragments need to be reassembled, (ii) iteratively optimizes the stitch using a genetic algorithm and (iii) efficiently reconstructs the final artificial whole-mount section on full resolution (0.25 µm/pixel). PythoStitcher was validated on a total of 198 cases spanning five datasets with a varying number of tissue fragments originating from different organs from multiple centers. PythoStitcher successfully reconstructed the whole-mount section in 86–100% of cases for a given dataset with a residual registration mismatch of 0.65–2.76 mm on automatically selected landmarks. It is expected that our algorithm can aid pathology labs unable to employ whole-mount sectioning through faster clinical case evaluation and improved radiology-pathology correlation workflows.

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Reconstructing virtual large slides can improve the accuracy and consistency of tumor bed evaluation for breast cancer after neoadjuvant therapy

Cited by 2 publications

References 23 publications

PythoStitcher: an iterative approach for stitching digitized tissue fragments into full resolution whole-mount reconstructions

PythoStitcher: an iterative approach for stitching digitized tissue fragments into full resolution whole-mount reconstructions

Full resolution reconstruction of whole-mount sections from digitized individual tissue fragments

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