Oncotarget

BUFFALO, NY - November 27, 2024 – A new #editorial was #published in Oncotarget's Volume 15 on November 12, 2024, entitled “Persistence landscapes: Charting a path to unbiased radiological interpretation.”

In this editorial, Yashbir Singh, Colleen Farrelly, Quincy A. Hathaway, and Gunnar Carlsson from the Department of Radiology, Mayo Clinic (Rochester, MN), introduce persistence landscapes, a mathematical method designed to address biases in medical imaging and artificial intelligence (AI). Persistence landscapes build on persistence images, which track how patterns in data appear and disappear across different scales. By transforming this complex data into simpler, more manageable forms, persistence landscapes create a format that is easy to analyze and compare. This makes it a valuable tool for identifying and correcting biases in medical imaging.

Medical imaging plays a critical role in healthcare, but it is not perfect. Biases, caused by differences in equipment, technology, or even the patient population, can lead to inaccurate diagnoses. Persistence landscapes offer a way to identify and fix these hidden issues.

"[...] persistence landscapes have the potential to play a crucial role in identifying and mitigating biases in radiological practice, whether these biases stem from demographic factors, equipment variations, or the limitations of AI algorithms.”

Persistence landscapes are particularly effective at reducing random noise in medical images while preserving important details. This makes it easier for clinicians and researchers to focus on the most meaningful parts of an image. The method also improves AI tools by addressing common problems, such as when models are too focused on specific details or when they miss important information. Additionally, persistence landscapes also simplify the integration of data from different scan types, like positron emission tomography (PET) and magnetic resonance imaging (MRI), without introducing new errors.

Despite its potential, the use of persistence landscapes in real-world medical imaging comes with challenges. It requires powerful computers to process large data, which can be costly and time-consuming, and expert interpretation for meaningful use. Better tools are needed to make this method more accessible for clinicians. While integrating this method into clinical settings will take effort, the benefits could be transformative. With further research and refinement, persistence landscapes hold enormous promise for advancing equitable healthcare.

“Persistence landscapes represent a powerful new tool in our ongoing efforts to achieve unbiased and accurate radiological interpretation.”

DOI - https://doi.org/10.18632/oncotarget.28671

Correspondence to - Yashbir Singh - [email protected]

Video short - https://www.youtube.com/watch?v=kq1pEhZvLXc

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About Oncotarget

Oncotarget (a primarily oncology-focused, peer-reviewed, open access journal) aims to maximize research impact through insightful peer-review; eliminate borders between specialties by linking different fields of oncology, cancer research and biomedical sciences; and foster application of basic and clinical science.

Oncotarget is indexed and archived by PubMed/Medline, PubMed Central, Scopus, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).

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BUFFALO, NY - November 25, 2024 – A new #editorial was #published in Oncotarget's Volume 15 on November 12, 2024, entitled, “Visualizing radiological data bias through persistence images.”

This editorial highlights a powerful tool called "persistence images," which could improve how medical imaging and artificial intelligence (AI) systems are developed and used. Authors Yashbir Singh, Colleen Farrelly, Quincy A. Hathaway, and Gunnar Carlsson from the Department of Radiology, Mayo Clinic (Rochester, MN), provide a detailed explanation of how persistence images uncover hidden biases and advance fairness in healthcare AI.

AI is becoming a major part of healthcare, helping clinicians analyze X-rays, magnetic resonance imaging, and computed tomography scans. However, if the data used to train AI systems is biased, it could lead to unfair or inaccurate results. Derived from topological data analysis (TDA), persistence images transform complex medical scans into simple, stable visuals. These images make it easier to spot patterns or irregularities that could indicate bias. For example, they can reveal whether certain groups—such as patients of a specific age, gender, or ethnicity—are underrepresented in the data used to train AI systems.

“The use of persistence images in radiological analysis opens up new possibilities for identifying and addressing biases in both data interpretation and AI model training...”

This could help ensure that AI systems work equitably for all patient groups, resulting in more reliable diagnoses and better outcomes.

In addition to detecting bias, persistence images also help filter out noise, or irrelevant details, from medical scans. This makes it easier for both AI systems and radiologists to focus on meaningful features in the images, improving overall accuracy. These insights help AI systems perform better and make more accurate, trustworthy decisions.

Despite their potential, persistence images face challenges. Generating persistence images for large datasets demands substantial computing power, while integration into clinical workflows requires user-friendly tools and specialized training for healthcare professionals.

As healthcare becomes more data-driven, tools like persistence images could transform how medical imaging is used.

“By helping us visualize and address hidden biases, they can contribute to improved patient outcomes and more personalized healthcare delivery.”

In conclusion, this editorial envisions a future where advanced mathematical tools like persistence images play a vital role in eliminating bias and improving patient outcomes. Integrating these tools into clinical workflows could enhance radiological analysis, setting new standards for accuracy and equity in healthcare worldwide.

DOI - https://doi.org/10.18632/oncotarget.28670

Correspondence to - Yashbir Singh - [email protected]

Video short - https://www.youtube.com/watch?v=sQELv8oi3ew

About Oncotarget

Oncotarget (a primarily oncology-focused, peer-reviewed, open access journal) aims to maximize research impact through insightful peer-review; eliminate borders between specialties by linking different fields of oncology, cancer research and biomedical sciences; and foster application of basic and clinical science.

Oncotarget is indexed and archived by PubMed/Medline, PubMed Central, Scopus, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).

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BUFFALO, NY - November 20, 2024 – A new #editorial was #published in Oncotarget's Volume 15 on November 12, 2024, entitled, “Persistence barcodes: A novel approach to reducing bias in radiological analysis.”

This editorial, authored by Yashbir Singh, Colleen Farrelly, Quincy A. Hathaway and Gunnar Carlsson from the Department of Radiology, Mayo Clinic (Rochester, MN), introduces persistence barcodes as a groundbreaking tool in medical imaging, particularly radiology.

Derived from topological data analysis (TDA), this method transforms complex medical images into clear, interpretable patterns. By highlighting features such as tissue densities, blood vessels, and tumors, persistence barcodes reduce diagnostic bias and uncover subtle details that traditional artificial intelligence (AI) systems might miss. This innovative approach holds great promise for enhancing diagnostic accuracy and improving patient care.

Unlike some AI tools, like Graph Neural Networks, which risk oversmoothing and blurring critical features, persistence barcodes preserve key structural details. This method visualizes how features in medical images emerge, persist, and fade across different scales, providing clearer insights into the data.

By detecting subtle changes in tissue density that could indicate early disease and filtering out irrelevant artifacts or noise from imaging errors, persistence barcodes enhance diagnostic accuracy and reliability.

Persistence barcodes enhance fairness and consistency by standardizing analyses across different machines and radiologists, ensuring reliable diagnoses regardless of the imaging system. Their robustness against equipment-related variations makes them a valuable tool for improving diagnostic accuracy in diverse clinical settings.

While promising, the integration of persistence barcodes into routine medical practice faces challenges, such as the computational demands of processing high-resolution images and the need for user-friendly visualization tools.

“As we continue to refine and validate this approach, persistence barcodes could play a crucial role in developing more accurate, consistent, and unbiased diagnostic tools. This, in turn, has the potential to improve patient outcomes and advance the field of radiology as a whole.”

In conclusion, with continued development and refinement, persistence barcodes have the potential to revolutionize medical imaging by facilitating earlier and more accurate disease detection, minimizing diagnostic errors, and significantly improving patient outcomes.

DOI - https://doi.org/10.18632/oncotarget.28667

Correspondence to - Yashbir Singh - [email protected]

Video short - https://www.youtube.com/watch?v=eVOqpV2vFsg

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Oncotarget (a primarily oncology-focused, peer-reviewed, open access journal) aims to maximize research impact through insightful peer-review; eliminate borders between specialties by linking different fields of oncology, cancer research and biomedical sciences; and foster application of basic and clinical science.

Oncotarget is indexed and archived by PubMed/Medline, PubMed Central, Scopus, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).

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Samuel Silva from the Department of Pathology at Federal University of Ceará in Fortaleza, Brazil, discusses a research paper he co-authored that was published in Oncotarget Volume 15, titled, “Relationship between the expressions of DLL3, ASC1, TTF-1 and Ki-67: First steps of precision medicine at SCLC.”

DOI - https://doi.org/10.18632/oncotarget.28660

Correspondence to - Fabio Tavora - [email protected]

Video interview - https://www.youtube.com/watch?v=bJO2MD8AXkY

Video transcription - https://www.oncotarget.net/2024/11/18/behind-the-study-dll3-asc1-ttf-1-ki-67-in-precision-medicine-for-sclc/

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Keywords - cancer, DLL3, pathology, biomarkers, qupath, small cell carcinoma

About Oncotarget

Oncotarget (a primarily oncology-focused, peer-reviewed, open access journal) aims to maximize research impact through insightful peer-review; eliminate borders between specialties by linking different fields of oncology, cancer research and biomedical sciences; and foster application of basic and clinical science.

Oncotarget is indexed and archived by PubMed/Medline, PubMed Central, Scopus, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).

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BUFFALO, NY - November 18, 2024 – A new #editorial was #published in Oncotarget's Volume 15 on November 12, 2024, entitled, “Mitigating bias in radiology: The promise of topological data analysis and simplicial complexes.”

In this publication, researchers Yashbir Singh, Colleen Farrelly, Quincy A. Hathaway, and Gunnar Carlsson from the Department of Radiology at the Mayo Clinic in Rochester, MN, explore how a mathematical technique called Topological Data Analysis (TDA) can enhance the reliability and reduce bias in AI systems used for medical diagnosis. By addressing issues of fairness and accuracy in current AI tools, TDA holds the potential to transform the field of radiology.

Radiology increasingly relies on AI to analyze medical images like X-rays and Magnetic Resonance Imaging (MRIs). While these tools provide speed and efficiency, they can sometimes yield biased or inconsistent results due to limitations in the data or algorithms. Researchers suggest that TDA can address these challenges by capturing critical details in medical images—such as subtle tissue patterns or branching structures in blood vessels—that traditional methods might overlook.

TDA analyzes the "shape" and structure of data, which uncovers patterns and relationships beyond individual pixels. This innovative approach offers three key benefits: 1) It captures intricate features, such as looping blood vessels, 2) provides a more comprehensive analysis by examining interactions between pixel groups, creating a holistic view, and 3) enhances transparency that allows clinicians to better understand how AI reaches its conclusions and identify potential errors or biases.

AI tools in radiology are often trained on limited or unbalanced data, meaning they might not work as well for certain groups of people. This can lead to unfair or inaccurate diagnoses. TDA offers a way to fix that by creating more comprehensive and diverse data models. It can also handle noise and inconsistencies in images, like differences caused by different equipment or patient positions.

“This mathematical framework has the potential to significantly improve the accuracy and fairness of radiological assessments, paving the way for more equitable patient care.”

In conclusion, this new approach has the potential to revolutionize how AI is used in radiology and improve diagnosis for everyone. While still in early development, researchers are optimistic about TDA’s ability to transform medical imaging.

“As researchers and clinicians, we must continue to explore and develop these innovative approaches to ensure that the future of AI-assisted radiology is both highly accurate and equitable for all patients.”

DOI - https://doi.org/10.18632/oncotarget.28668

Correspondence to - Yashbir Singh - [email protected]

Video short - https://www.youtube.com/watch?v=v7eWFjmKoNk

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Oncotarget (a primarily oncology-focused, peer-reviewed, open access journal) aims to maximize research impact through insightful peer-review; eliminate borders between specialties by linking different fields of oncology, cancer research and biomedical sciences; and foster application of basic and clinical science.

Oncotarget is indexed and archived by PubMed/Medline, PubMed Central, Scopus, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).

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Cancer dormancy is a phenomenon in which, after treatment, residual cancer cells remain inactive in the body for months or even years. During this time, patients often show no signs of the disease. These dormant cells can unpredictably reawaken, leading to tumor recurrence—a significant challenge in cancer treatment. Despite progress in cancer research, the factors that control dormancy and subsequent reactivation remain poorly understood. Identifying these factors and understanding how cancer cells dormancy and reactivation occur could be crucial to preventing cancer recurrence.

This question was the focus of a recent study titled “Initiation of Tumor Dormancy by the Lymphovascular Embolus,” published in Oncotarget Volume 15, on October 11, 2024. In this blog, we will look at the key findings and implications of this important work.

Full blog - https://www.oncotarget.org/2024/11/13/cancer-dormancy-and-tumor-recurrence-new-insights-for-breast-cancer/

Research paper DOI - https://doi.org/10.18632/oncotarget.28658

Correspondence to - Sanford H. Barsky - [email protected]

Video short - https://www.youtube.com/watch?v=z6ex7Yl8r5Q

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Keywords - cancer, dormancy, lymphovascular embolus, mTOR, E-cadherin proteolysis

About Oncotarget

Oncotarget (a primarily oncology-focused, peer-reviewed, open access journal) aims to maximize research impact through insightful peer-review; eliminate borders between specialties by linking different fields of oncology, cancer research and biomedical sciences; and foster application of basic and clinical science.

Oncotarget is indexed and archived by PubMed/Medline, PubMed Central, Scopus, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).

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BUFFALO, NY - November 12, 2024 – A new #review was #published in Oncotarget's Volume 15 on November 7, 2024, entitled “Understanding the interplay between extracellular matrix topology and tumor-immune interactions: Challenges and opportunities.”

This comprehensive review by researchers Yijia Fan, Alvis Chiu, Feng Zhao, and Jason T. George from Texas A&M University, Rice University, and MD Anderson Cancer Center sheds light on how the structural properties of the extracellular matrix (ECM) within tumors impact immune cell behavior and influence the effectiveness of cancer immunotherapies. The ECM, a network of proteins surrounding cells, often transforms in cancer, becoming denser and more aligned. These changes create physical barriers that can prevent immune cells, especially T cells, from effectively accessing and attacking tumors, thereby limiting the success of immunotherapies.

The team emphasizes the role of specific ECM configurations, known as Tumor-Associated Collagen Signatures (TACS), in cancer progression and immune evasion. TACS1 and TACS2 patterns create "immune deserts" around tumors, limiting immune cell movement and preventing T cells from recognizing and attacking cancer cells, which is essential for successful immunotherapy. In advanced stages, TACS3 aligns ECM fibers in ways that both promote tumor spread and create additional barriers, further obstructing immune cell access to the tumor.

These insights lead the way for ECM-targeted therapies designed to modify these barriers, potentially transforming “cold” (immune-non-responsive) tumors into “hot” (immune-responsive) ones, thereby improving immune cell infiltration and enhancing treatment outcomes.

“Understanding the complex interplay is relevant for developing more accurate model of tumor evasion and the identification of corresponding therapeutic intervention.”

The review highlights advanced computational models that simulate interactions between the ECM, immune cells, and tumors, offering valuable insights for developing ECM-targeted therapies. These models illustrate how modifying ECM properties could enhance immune cell migration and function, potentially overcoming immune resistance and expanding the effectiveness of immunotherapies.

The authors also suggest that targeting ECM structure could significantly enhance the effectiveness of immunotherapy, especially for cancers like breast, pancreatic, and ovarian, which often feature dense ECM regions. By reshaping the ECM, such treatments could enable immune cells to access previously unreachable tumor areas, presenting a promising strategy to combat tumors that are resistant to standard therapies.

In conclusion, the review underscores the need for continued research into ECM-focused strategies, which could support more integrated approaches to cancer treatment. By targeting the ECM’s physical barriers and immune evasion mechanisms, these strategies hold promise for improving outcomes in difficult-to-treat cancers.

DOI - https://doi.org/10.18632/oncotarget.28666

Correspondence to - Jason T. George - [email protected]

Video short - https://www.youtube.com/watch?v=7Wm-SMLJadk

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BUFFALO, NY - November 11, 2024 – A new #editorial was #published in Oncotarget's Volume 15 on November 7, 2024, titled “Beyond the hype: Navigating bias in AI-driven cancer detection.”

In this editorial, researchers from the Mayo Clinic emphasize the need to address potential biases in Artificial Intelligence (AI) tools used for cancer detection to ensure fair and equitable healthcare. Authors Yashbir Singh, Heenaben Patel, Diana V. Vera-Garcia, Quincy A. Hathaway, Deepa Sarkar, and Emilio Quaia discuss the risks of biased AI systems, which can lead to disparities in diagnosis and treatment outcomes across diverse patient groups.

While AI is transforming cancer care through early diagnosis and improved treatment planning, this study warns that AI models trained on limited or non-diverse data may misdiagnose or overlook certain populations, particularly those in underserved communities, thereby increasing healthcare disparities. As explained in the editorial, “For example, if an AI model is trained on Caucasian patients, it may struggle to detect skin cancer accurately in patients with darker skin, leading to missed diagnoses or false positives.” Such biases could result in unequal access to early diagnosis and treatment, ultimately leading to poorer health outcomes for certain groups. Beyond racial bias, factors such as socioeconomic status, gender, age, and geographic location can also affect the accuracy of AI in healthcare.

The authors propose a comprehensive approach to developing fair AI models in healthcare, highlighting six key strategies. They first emphasize the importance of using diverse and representative datasets to improve diagnostic accuracy across all demographics. Rigorous testing and validation across various population groups are necessary before AI systems are widely implemented. To promote ethical AI use, models should be transparent in their decision-making processes, enabling clinicians to recognize and address potential biases. The researchers also advocate for collaborative development involving data scientists, clinicians, ethicists, and patient advocates to capture a range of perspectives. Continuous monitoring and regular audits are essential to detect and correct biases over time. Finally, training healthcare providers on AI’s strengths and limitations will empower them to use these tools responsibly and make informed interpretations.

“The goal should not merely be to create AI systems that are more accurate than humans but to develop technologies that are fundamentally fair and beneficial to all patients.”

The authors also urge regulatory bodies, such as the U.S. Food and Drug Administration (FDA), to implement updated frameworks specifically aimed at addressing AI bias in healthcare. Policies that promote diversity in clinical trials and incentivize the development of fair AI systems will help ensure that AI benefits reach all populations equitably. They caution against over-reliance on AI without a full understanding of its limitations, as unchecked biases could undermine patient trust and slow the adoption of valuable AI technologies.

In conclusion, as AI continues to transform cancer care, the healthcare sector must prioritize fairness, transparency, and robust AI regulation to ensure that it serves all patients without bias. By addressing bias from development through to implementation, AI can fulfill its promise of creating a fair and effective healthcare system for everyone.

DOI - https://doi.org/10.18632/oncotarget.28665

Correspondence to - Yashbir Singh - [email protected]

To learn more about Oncotarget, please visit https://www.oncotarget.com.

BUFFALO, NY - November 6, 2024 – A new #research paper was #published in Oncotarget's Volume 15 on October 11, 2024, entitled “Relationship between the expressions of DLL3, ASC1, TTF-1 and Ki-67: First steps of precision medicine at SCLC”

This study, led by researchers from the Federal University of Ceará in Brazil and collaborating institutions in Brazil, Argentina and Spain, presents important findings on small cell lung cancer (SCLC), one of the most aggressive forms of lung cancer with limited treatment options. The research reveals how specific biomarkers in SCLC tumors could open new opportunities for more personalized and targeted therapies for these patients.

SCLC accounts for about 15% of all lung cancer cases and is known for its rapid spread and resistance to many treatments. Currently, the five-year survival rate for SCLC patients is below 5%. Recent advances in precision medicine aim to improve these outcomes by identifying and targeting the unique characteristics of each patient’s tumor.

Researchers Samuel Silva, Juliana C. Sousa, Cleto Nogueira, Raquel Feijo, Francisco Martins Neto, Laura Cardoso Marinho, Guilherme Sousa, Valeria Denninghoff, and Fabio Tavora analyzed tumor samples from 64 SCLC patients using both traditional and digital pathology tools. Their findings highlighted promising results for two of the analyzed biomarkers: Delta-like ligand 3 (DLL3) and Thyroid transcription factor-1 (TTF-1).

DLL3 was identified in over 70% of the tumors, highlighting its potential as a promising target for therapies like Tarlatamab. Another key finding involved TTF-1 expression; patients with TTF-1-positive tumors showed improved survival rates, underscoring its potential as a prognostic marker to refine diagnoses and predict patient outcomes.

The authors also noted that, “The use of digital pathology software QuPath enhanced the accuracy and depth of analysis, allowing for detailed morphometric analysis and potentially informing more personalized treatment approaches.”

In conclusion, the study suggests that clinical trials targeting biomarkers like DLL3 and TTF-1 could enhance SCLC patient outcomes by tailoring treatments based on individual biomarker profiles. This research marks an important step forward in precision medicine for SCLC.

DOI - https://doi.org/10.18632/oncotarget.28660

Correspondence to - Fabio Tavora - [email protected]

Video short - https://www.youtube.com/watch?v=YYsZ0UHPszg

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Keywords - cancer, DLL3, pathology, biomarkers, qupath, small cell carcinoma

About Oncotarget

Oncotarget (a primarily oncology-focused, peer-reviewed, open access journal) aims to maximize research impact through insightful peer-review; eliminate borders between specialties by linking different fields of oncology, cancer research and biomedical sciences; and foster application of basic and clinical science.

Oncotarget is indexed and archived by PubMed/Medline, PubMed Central, Scopus, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).

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BUFFALO, NY - November 4, 2024 – A new #casereport was #published in Oncotarget's Volume 15 on October 11, 2024, entitled “A case of adenosquamous pancreatic cancer with a KRAS G12C mutation with an exceptional response to immunotherapy.”

This case report highlights a remarkable and unexpected response to immunotherapy in a patient with metastatic adenosquamous pancreatic cancer (ASCP), a rare and aggressive form of pancreatic cancer. The study, led by Murtaza Ahmed, Brent K. Larson, Arsen Osipov, Nilofer Azad, and Andrew Hendifar from Cedars-Sinai Medical Center and Johns Hopkins University, provides new hope for ASCP patients, who are traditionally underserved by current treatment options.

The team documented a 68-year-old male with metastatic ASCP carrying a KRAS G12C mutation. Unexpectedly, after limited success with standard therapies, the patient’s cancer responded significantly to pembrolizumab, a type of immune checkpoint inhibitor, despite the absence of typical markers indicating suitability for immunotherapy.

Pancreatic cancer remains one of the most lethal cancer types, with few advancements in effective treatments for its rarer forms, such as ASCP, which accounts for only 1-10% of all pancreatic cancer cases. Traditionally, ASCP has been treated with chemotherapy based on protocols for the more common pancreatic ductal adenocarcinoma, despite the distinct tumor characteristics.

This case suggests that ASCP’s unique tumor microenvironment may make it more receptive to immunotherapy. Researchers are hopeful that this new understanding will drive clinical trials focused on immunotherapy specifically for ASCP patients, potentially offering new options for those with limited treatment success.

“To that point, there is an active multi-center phase 2 trial investigating outcomes and responses to ICI in patients with metastatic or unresectable ASCP or ampullary cancer.”

In conclusion, this report signals a potential shift in the treatment of rare and aggressive pancreatic cancer subtypes like ASCP. As oncology increasingly embraces personalized medicine, cases like this one open new avenues for patients who were not responsive to traditional therapies, potentially transforming the management of previously intractable cancers.

DOI - https://doi.org/10.18632/oncotarget.28659

Correspondence to - Andrew Hendifar - [email protected]

Video short - https://www.youtube.com/watch?v=VnfohGvfMoM

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Keywords - cancer, pancreatic cancer, immunotherapy, metastasis

About Oncotarget

Oncotarget (a primarily oncology-focused, peer-reviewed, open access journal) aims to maximize research impact through insightful peer-review; eliminate borders between specialties by linking different fields of oncology, cancer research and biomedical sciences; and foster application of basic and clinical science.

Oncotarget is indexed and archived by PubMed/Medline, PubMed Central, Scopus, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).

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