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    Author: Deepbio

    [Abstract] ASCO 2023 – Deep learning-based histomorphological pattern profiles for effective risk stratification in prostate cancer.

    Posted on by Deepbio

    Authors:
    JaeHeon Lee, Tae-Yeong Kwak, Joonyoung Cho, Sun Woo Kim, Hyeyoon Chang

    Organizations
    Deep Bio Inc., Seoul, South Korea

    Background
    Prostate cancer affects millions of men globally and biochemical recurrence (BCR) is an important indicator of its progression. With the increasing use of computational techniques, there has been a growing interest in utilizing whole slide images (WSIs) of H&E stained prostate tissue to evaluate the patients’ clinical risks. Our study aimed to develop a novel method for predicting BCR by performing clustering on features extracted from deep learning models, thereby capturing morphological characteristics.

    Methods
    The WSIs and clinical information were collected from The Cancer Genome Atlas (TCGA, from 2000 to 2013) dataset, for 321 patients (43 patients with BCR) who were not treated with any adjuvant therapies, with 2.5 years of median follow-up time. 356 collected WSIs were split into 1024 x 1024 patches at 5x magnification for the deep learning-based analysis. We used an ImageNet-pretrained deep learning-based clustering model (SwAV) to extract features from each patch and applied additional K-means clustering to assign the patches into one of 16 clusters. We tested both the cosine similarity and Euclidean distance as the clustering measure. After excluding the dissimilar patches under specific thresholds, the proportion of each cluster was calculated per slide to describe the distribution of morphological patterns as a 16-dimensional vector. To evaluate the predictability, we trained Cox models with the proportion vectors and assessed their performance via the concordance index (c-index). We repeated a stratified 5-fold cross-validation process 100 times to achieve robustness. As a reference, we conducted the same experiment with a BCR prediction nomogram published by MSKCC. Moreover, with the median proportion of each cluster, we separated two patient groups, drawed Kaplan-Meier curves, performed the log-rank test for significance, and we obtained the hazard ratio (HR) of each cluster from the trained Cox model of each fold.

    Results
    We explored several thresholds to find ones yielding the best performance. When using the similarity, the best c-index was 0.704 with the threshold value of 0.25. With the distance value of 0.60, the c-index was 0.701. When using the MSKCC nomogram that incorporates clinical features, the average c-index was 0.717. The table contains the results of statistical analysis for selected clusterings that were significant in the log-rank test. Particularly in cluster B, the patches showed high-risk patterns including perineural invasion, high grade cancer, and necrosis in agreement with the high HR.

    Conclusions
    Our results suggest that the histomorphological pattern profiles depicted by the deep learning model have the potential for effective risk stratification in prostate cancer.

    [Abstract] ASCO 2023 – Exploring the efficacy of a continuous form of the histologic grade in prostate cancer prognosis prediction

    Posted on by Deepbio

    Authors:
    Tae-Yeong Kwak, JaeHeon Lee, Joonyoung Cho, Sun Woo Kim, Hyeyoon Chang

    Organizations
    Deep Bio Inc., Seoul, South Korea

    Background

    Adenocarcinoma of the prostate is the second most common type of cancer among men worldwide. The Gleason grading system remains the gold standard for evaluating the prognosis of prostate cancer by assessing the cancer morphology. However, the current discretized Gleason pattern regime limits the depiction of fine-grained histomorphological changes. We evaluated the efficacy of the algorithm-generated continuous histologic grade value in prostate cancer prognosis prediction.

    Methods
    Whole-slide images of H&E prostatectomy tissue, along with the follow-up data including new tumor event (NTE) and biochemical recurrence (BCR), were obtained for the cases diagnosed as prostate cancer during 2000-2013 from The Cancer Genome Atlas (TCGA) database. Cases with missing data were excluded, resulting in a total of 308 and 397 cases being used for the analysis of NTE and BCR, respectively. Our study utilized a deep learning-based algorithm that performs Gleason scoring as follows. First, it computes per-pixel likelihood values for each of the 4 classes: benign, Gleason 3, 4, and 5. Then, the per-pixel class with the highest likelihood value and the slide-wise ISUP grade group (GG-AI) is successively determined. We tweaked the algorithm to aggregate the likelihood-weighted Gleason grade assigned to each pixel into a continuous form of the histologic grade (c-HG). We compared the prognostic performance of c-HG with that of the original ISUP grade group in TCGA (GG) and that of algorithm-generated GG-AI in predicting the risk of NTE as well as BCR. The Cox regression analysis was conducted, setting the cases from one of three donating institutions (EJ, HC, KK) as the target, fitting the Cox model with the cases from the other institutions, and evaluating the fitted model on the target.

    Results
    The median follow-up duration in months was 32 for NTE and 29 for BCR, respectively. The number of cases with NTE was 51, while the one with BCR was 82. The table presents the c-index values obtained from each institution for GG, GG-AI, and c-HG. Note that the number of cases in each institution is different for NTE and BCR, due to the different number of removed cases with missing data. c-HG showed the best average performance in both NTE and BCR risk predictions. It is also notable that c-HG showed a stable performance for varying institutions.

    Conclusions
    Our findings support the usefulness of the algorithm-based analysis of prostatectomy specimens, which can benefit clinicians in the hospital.

    We proposed an algorithm-based method of representing histologic grade as a continuous value, which may give better predictions in disease progression for prostate adenocarcinoma.

    [Abstract] ASCO 2023 – Algorithm-based histologic grade and tumor ratio for radical prostatectomy: Comparison with pathology reports.

    Posted on by Deepbio

    Authors:
    Tae-Yeong Kwak, JaeHeon Lee, Joonyoung Cho, Sun Woo Kim, Hyeyoon Chang, Hong Koo Ha

    Organizations
    Deep Bio Inc., Seoul, South Korea, Pusan National University, Pusan, South Korea

    Background

    The histologic grade (Gleason score) as well as the tumor ratio of the resected specimen plays a significant role in assessing the clinical risk of prostate cancer patients who underwent radical prostatectomy. Gleason scoring is known to be prone to inter- and intra-observer discordance, and eyeballing measurement of tumor ratio is possibly coarse and inaccurate. Several deep learning-based tissue image analysis algorithms that perform prostate cancer diagnosis have been developed and are about to be applied clinically. We evaluated the utility of one of those algorithms by comparing its output with the pathology reports.

    Methods
    A total of 29681 H&E-stained tissue slides were collected for 1001 radical prostatectomy cases during 2010-2021 at Pusan National University Hospital and scanned at 40x magnification into whole-slide images. On each slide, we utilized a tissue detection algorithm to identify tissue regions and measure the area. A deep learning-based algorithm was used to identify prostate cancer lesions, measure the area, and determine the grades. The area measurement was converted into the volume figure assuming that the tissue slice thickness was 5mm. The slide-wise algorithm outputs were then aggregated for each case, resulting in the specimen volume, tumor ratio, and Gleason score. In the evaluation, the algorithm-based Gleason scores were compared with the ones in the hospital pathology reports on the ISUP grade group basis. The correlation analysis was performed for the tumor ratio. The specimen density values were calculated from the volume figures and the hospital weight measurement and utilized to exclude the cases with extreme density values.

    Results
    The min, max, and average number of slides per case were 1, 67, and 29.7. For two cases with a single slide collected, both the algorithm and the expert pathologist found no cancer. The pathologist also confirmed that 7 had residual tumors among 15 cases where there was no Gleason score in the pathology reports while the algorithm found cancer. For the remaining 984 cases, the algorithm provided the grade groups equal to, higher than, and lower than the pathology reports for 482, 384, and 118 cases respectively, showing moderate agreement. The min, max, and mean values of the specimen volume (mL) were 0.01, 75.3, and 32.7, while the corresponding values of the specimen density (g/mL) were 0.11, 3001.79, and 4.38. The density values at the lower and upper 2.5% were 0.42 and 1.40 respectively, and the mean of the values between them (inner 95%) was 0.92. The degree of tumor ratio correlation between the algorithm and the pathology reports was high with the coefficient 0.813 (95% CI: 0.791-0.833), which went up to 0.826 (95% CI: 0.805-0.845) for the inner 95% density cases.

    Conclusions
    Our findings support the usefulness of the algorithm-based analysis of prostatectomy specimens, which can benefit clinicians in the hospital.

    Deep Bio’s Novel Study Results Reaffirm Its Global Presence

    Posted on by Deepbio

    USCAP 2023 is Deep Bio’s sixth consecutive year of presenting research abstracts

    SEOUL, SOUTH KOREA (PRWEB) MARCH 14, 2023 – Deep Bio, a pioneer in medical AI for digital pathology and cancer diagnostics support software shared three research abstracts at the United States and Canadian Academy of Pathology (USCAP) 2023 which is being held in New Orleans from March 11th to 16th.

    The abstracts explore deep learning-based analysis of prostate cancer, Deep Bio’s main focus area, and c-MET measurements in lung cancer. In particular, collaborative research with ARUP, one of the top CLIA labs in the US, for the validation of a deep learning algorithm for prostate cancer detection and Gleason grading was presented at a platform session.
    Other two presentations about the c-MET research:

    • evaluation of attention-based tumor area segmentation deep learning models for c-MET immunohistochemical stained non-small cell lung cancer slide images, and
    • Comparative analysis of c-MET expression using image analysis model in IHC stained WSIs were introduced during poster sessions.

    Deep Bio is also participating in this year’s American Association for Cancer Research (AACR) and American Urology Association (AUA) in April. AACR has accepted two research abstracts this year about molecular subtype prediction in breast cancer using deep learning and molecular mapping in prostate cancer.

    “It is inspiring that Deep Bio is presenting not only our own study but collaborative research abstract with reputable US CLIA laboratory ARUP at renowned international conferences,” said Sun-Woo Kim, CEO of Deep Bio. “As demand for precise diagnosis keeps increasing in the era of personalized cancer treatment, we will strive to bring differentiated deep learning technologies that can be utilized for multiple cancer types and biomarkers in addition to prostate and breast cancers, which are our focus areas and lead digital cancer pathology both in Korean and global markets” added he.

    Deep Bio continues to focus on not only AI diagnostics, but also R&D and presenting the results at various international conferences and events. The company also continues to build its presence in the global market through collaboration with overseas digital pathology solution providers in the US, Europe, and India. Designated as an ‘Innovative Product (Fast Track II)’ by the Korean Public Procurement Service (PPS) last year, the company plans to supply DeepDx® Prostate to five university medicals in Korea.

    About Deep Bio
    Deep Bio Inc. is an AI healthcare company with in-house expertise in deep learning and cancer pathology. Our vision is to radically improve efficiency and accuracy of pathologic cancer diagnosis and prognosis, by equipping pathologists with deep learning-based IVD SaMDs (In Vitro Diagnostics Software as a Medical Device), for optimal cancer treatment decisions. To learn more, visit http://www.deepbio.co.kr.

    DeepDx® Prostate is a clinically-validated AI for prostate core needle biopsy tissue image analysis. Whole-slide images (WSIs) of H&E-stained biopsy tissue specimens are analyzed for prostate cancer, Gleason scores and grade groups. Extensively tested at 4 US CLIA labs (700k+ cores between 2019 and 2021), DeepDx® Prostate can alleviate the shortage of pathologists and the resultant increase in workload, while reducing diagnostic subjectivity and variability. To learn more, visit http://www.deepbio.co.kr

    Deep Bio and Visiopharm Announce a Collaborative Integration of a Clinical-Grade AI Prostate Cancer Solution with a Leading Digital Pathology Platform

    Posted on by Deepbio

    Pathologists can now have access to the state-of-the-art AI prostate cancer grading solution     
                               
    SEOUL, SOUTH KOREA (PRWEB) NOVEMBER 15, 2022 – Deep Bio, a leading AI biotech dedicated to cancer diagnosis, and Visiopharm, a world leader in AI-powered image analysis and tissue mining for research and diagnostics, announced a strategic partnership to provide pathologists access to the latest AI-powered prostate cancer solution.

    AI-powered cancer diagnostics has been proving its utility not only in research, but increasingly in supporting and improving pathologists’ decisions, workflow efficiency, and diagnostic precision. Deep Bio’s deep learning-based CE-IVDD prostate cancer diagnosis support software, DeepDx® Prostate, empowers pathologists by identifying cancerous areas and grading their severity providing a Gleason scoring.

    With this collaboration, DeepDx® strengthens Visiopharm’s diagnostic offering to its European diagnostic customers. This application expands pathologists’ accessibility to the latest cutting-edge H&E-based AI solutions for prostate cancer, which today is a large diagnostic indication that is both time and labor-intensive in diagnostic pathology labs. With its extensive AI-based image analysis solutions for multiple cancer types, Visiopharm’s platform is contributing to the global adoption of digital pathology and AI.

    The first phase of integration of DeepDx® into Visiopharm’s digital pathology platform has been completed. In the next phases, Visiopharm will together with Deep Bio scale up the solution as interest grows. The two companies plan to add additional AI cancer pathology solutions over time for prostate and breast, among others.

    “Digital transformation in healthcare is not a new concept anymore. Pathology, which has been slow to undergo digital transformation, is fast becoming digitalized due to a diverse range of AI solutions now available for implementation and validation”, said Sun Woo Kim, the CEO of Deep Bio. “Our dedication to AI for digital pathology has taken a major step forward with this collaboration and we are pleased to offer our latest technology to more labs and hospitals across the world through this opportunity. We will continue to support medical professionals to optimize their decisions which ultimately lead to the best patient care,” he added.

    The second phase of integration will aim for tighter interoperability among the platform and AI solutions, helping both companies to better promote and distribute to healthcare institutions around the world.

    Michael Grunkin, CEO of Visiopharm said, “We are seeing a lot of demand for this particular application among both our clinical research- and diagnostic customers and partners. In diagnostic workflows, this APP has the potential to support pathologists in automating time-consuming and repetitive work, while improving turn-around-time and standardization. This partnership with Deep Bio is a good example of how our scanner, LIMS, and PACS agnostic platform allow our users to benefit both from our own apps and best-in-class apps from our growing partner network, to gain access to a full diagnostic menu.”

    About Deep Bio

    Deep Bio Inc. is an AI healthcare company with in-house expertise in deep learning and cancer pathology. As the country’s first to obtain Korea’s MFDS (Ministry of Food and Drug Safety) approval of an AI-based cancer diagnostic support solution, Deep Bio’s vision is to radically improve efficiency and accuracy of pathologic cancer diagnosis and prognosis, by equipping pathologists with deep learning-based IVD SaMDs (In Vitro Diagnostics Software as a Medical Device), for optimal cancer treatment decisions. Deep Bio is also actively engaged in the research space and maintains ongoing collaborations with top US medical centers. To learn more, visit www.deepbio.co.kr.

    DeepDx® Prostate is a clinically-validated AI for prostate core needle biopsy tissue image analysis. Whole-slide images (WSIs) of H&E-stained biopsy tissue specimens are analyzed for prostate cancer, Gleason scores, and grade groups. Extensively tested at US CLIA labs (700k cores between 2019 and 2021), DeepDx® Prostate can alleviate the shortage of pathologists and the resultant increase in workload, while reducing diagnostic subjectivity and variability.” To learn more, visit www.deepbio.co.kr.

    About Visiopharm
    Visiopharm® is a world leader in AI-driven precision pathology software. Visiopharm’s pioneering image analysis tools support thousands of scientists, pathologists, and image analysis experts in academic institutions, biopharmaceutical industry, and diagnostic centers. AI-based image analysis and tissue mining tools support research and drug development research worldwide, while CE-IVD APPs support primary diagnostics. With highly advanced and sophisticated artificial intelligence and deep learning, Visiopharm delivers tissue data mining tools, precision results, and workflows. Visiopharm was founded in 2002 and is privately owned. The company operates internationally with over 780 user accounts and countless users in more than 40 countries. The company headquarters are in Denmark’s Medicon Valley, with offices in Sweden, England, Germany, Netherlands, and the United States.