## 4 New Tools for Digital Pathology in the Era of Artificial Intelligence
### DSA and HistomicsTK  
The DSA (Digital Slide Archive) is an open-source web-based platform for digital pathology that was developed for the management, integration, and analysis of histological images. The tool includes cell segmentation, stain color deconvolution, and normalization and augmentation [39] for cancer research. HistomicsTK is a companion library of image analysis algorithms providing image analysis capabilities for the DSA. As reported in the original study, the DSA and HistomicsTK provide cancer researchers with a free digital pathology platform, avoiding the need for costly commercial software, which is sometimes also expensive to scale. HistomicsTK was used for the extraction of pathogenic features in a study on tumor-infiltrating lymphocyte classification in breast cancer (the best performance was obtained by Random Forest with an ROC AUC of 0.86) [40]. Another study used HistomicsTK in the context of image analysis for renal allograft evaluation and fibrosis quantification [41]. In 2022, a study [42] used HistomicsTK for nucleus classification and segmentation in breast cancer to manage data and annotations. The positive pixel counting  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Link** & **License** & **Citations Original Work** \\ \hline QuPath & [[https://qupath.github.io/](https://qupath.github.io/)]([https://qupath.github.io/](https://qupath.github.io/)) (accessed on 21 May 2024) & Open source & 4731 \\ HistomicsTK & [[https://github.com/DigitalSlideArchive/HistomicsTK](https://github.com/DigitalSlideArchive/HistomicsTK)]([https://github.com/DigitalSlideArchive/HistomicsTK](https://github.com/DigitalSlideArchive/HistomicsTK)) & Open source & 152 \\ HistoQC & [[https://github.com/choosehappy/HistoQC](https://github.com/choosehappy/HistoQC)]([https://github.com/choosehappy/HistoQC](https://github.com/choosehappy/HistoQC)) (accessed on 21 May 2024) & Open source & 246 \\ MONAI & [[https://monai.io/](https://monai.io/)]([https://monai.io/](https://monai.io/)) (accessed on 21 May 2024) & Open source & 246 \\ PathML & [[https://pathml.org/](https://pathml.org/)]([https://pathml.org/](https://pathml.org/)) (accessed on 21 May 2024) & Open source & 15 \\ Histolab & [[https://github.com/histolab/histolab](https://github.com/histolab/histolab)]([https://github.com/histolab/histolab](https://github.com/histolab/histolab)) (accessed on 21 May 2024) & Open source & 15 \\ SliD & [[https://github.com/markowetzlab/slid-tutorial](https://github.com/markowetzlab/slid-tutorial)]([https://github.com/markowetzlab/slid-tutorial](https://github.com/markowetzlab/slid-tutorial)) (accessed on 21 May 2024) & Open source & 3 \\ SISH & [[https://github.com/mahmoodlab/SISH](https://github.com/mahmoodlab/SISH)]([https://github.com/mahmoodlab/SISH](https://github.com/mahmoodlab/SISH)) (accessed on 21 May 2024) & Open source & 62 \\ \hline \hline \end{tabular}
\end{table}
Table 1: A list of tools analyzed in the present review. The number of citations refers to that available in Google Scholar on 20 May 2024. The tools are listed and discussed in chronological order, considering the publication date.  
function included in this tool was used to correlate age-related cognitive impairment with neuropathologic features using WSI images (a mean AUC of 0.63 and a mean balanced accuracy of 0.59 were found in the hippocampus) [43]. Moreover, nuclear size, shape, staining intensity, boundary complexity, edges (chromatin clumping), and texture were extracted using HistomicsTK to investigate a digital histologic biomarker for the prognosis of invasive breast cancer [44].