Opinion - (2025) Volume 7, Issue 1
Received: 02-Feb-2025, Manuscript No. jspd-25-172568;
Editor assigned: 04-Feb-2025, Pre QC No. P-172568;
Reviewed: 18-Feb-2025, QC No. Q-172568;
Revised: 24-Feb-2025, Manuscript No. R-172568;
Published:
28-Feb-2025
, DOI: 10.37421/2684-4575.2025.7.003
Citation: Lucia Fernandez. ”The Future of Cancer Grading and Staging.” J Surg Path Diag 07 (2025):3.
Copyright: © 2025 F. Lucia This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
The principles of cancer grading and staging are in a period of rapid and fundamental evolution, shifting away from a reliance on purely anatomical and histological features towards a multi-layered, integrated approach that incorporates molecular data and advanced computational tools. This transformation is driven by the need for more accurate risk stratification, personalized treatment planning, and improved patient outcomes. The introduction of the Grade Group (GG) system for prostate cancer over a decade ago exemplifies this push for clarity and precision, as it successfully simplified complex Gleason patterns into five distinct prognostic groups, thereby providing clearer information to both clinicians and patients and helping to curb the overtreatment of low-risk disease[1].
This trend towards greater precision is most prominently seen in the recent updates from the World Health Organization (WHO). The 2021 WHO classification for tumors of the central nervous system marked a major turning point by formally integrating molecular diagnostics with traditional histology[2].
This integrated diagnosis is now considered essential for a range of tumors, particularly adult diffuse gliomas, where tumors that appear similar under a microscope can exhibit vastly different clinical behaviors based on their underlying molecular profiles, such as IDH mutation status and 1p/19q codeletion[5].
The WHO has continued this molecular focus in other areas, with the 2022 classification of bladder cancer emphasizing molecular subtyping alongside refined histological criteria to improve the correlation between pathology and clinical outcomes[10].
Established staging systems for other cancers are also adapting to incorporate more comprehensive data. For example, the 2018 update to the International Federation of Gynecology and Obstetrics (FIGO) staging system for cervical cancer represented a significant modernization by allowing, for the first time, the use of imaging and pathological findings to determine tumor spread, moving beyond the constraints of purely clinical assessment[6].
However, this paradigm shift is not without its difficulties. The venerable American Joint Committee on Cancer (AJCC) staging system, built upon the traditional TNM framework, faces significant challenges in its effort to incorporate non-anatomic factors like molecular biomarkers and other patient-specific data[4].
The future of staging will require evolving beyond this traditional framework to create more dynamic and personalized systems that fully integrate the wealth of available genomic and proteomic information for more accurate prognosis and treatment guidance. Even within established histopathological grading, there is a push to incorporate more quantitative and objective markers. In the grading of lung neuroendocrine tumors, the Ki-67 proliferation index is an increasingly vital parameter used to supplement mitotic count and necrosis, helping to resolve ambiguous cases and better stratify patient risk[8].
This evolution is being powerfully accelerated by technological innovation. Artificial Intelligence (AI), specifically deep learning, is emerging as a powerful tool for automating and improving the accuracy of breast cancer grading from histopathology slides[3].
These AI systems can objectively quantify key grading features, offering the potential to significantly reduce the inter-observer variability that has long been a challenge in pathology and thereby improve prognostic consistency. In a similar vein, the fields of radiomics and deep learning are enabling the analysis of medical images to extract quantitative features that are invisible to the human eye[9].
These computational techniques offer a non-invasive method to improve the accuracy of T-staging (local tumor extent) and N-staging (lymph node involvement), potentially reducing the need for more invasive diagnostic procedures. The frontier of non-invasive diagnostics is also being expanded by liquid biopsies, which analyze circulating tumor DNA (ctDNA) in the bloodstream[7].
This technology is transforming colorectal cancer management by providing a tool to supplement traditional staging, allowing for the detection of minimal residual disease after surgery and identifying patients at high risk of recurrence who may benefit from additional therapy.
The current evolution in cancer classification is fundamentally reshaping how tumors are diagnosed, graded, and staged, driven by a convergence of molecular science and advanced technology. A primary driver of this change is the integration of molecular diagnostics into the core of tumor classification. The 2021 World Health Organization (WHO) classification for Central Nervous System (CNS) tumors epitomizes this shift, establishing a new standard where molecular data is no longer just an adjunct but a required component for precise diagnosis [2]. This is practically applied in the grading of adult diffuse gliomas, where histological appearance alone is insufficient; molecular markers such as IDH status and 1p/19q codeletion are now critical determinants of a tumor's classification and predicted clinical course, reflecting their profound biological and prognostic significance [5]. This molecular-centric approach has been extended to other malignancies, as seen in the 2022 WHO classification of bladder cancer, which incorporates molecular subtyping to refine pathological definitions and better align them with clinical outcomes [10]. This deep integration of molecular data presents a significant challenge to traditional staging systems like the American Joint Committee on Cancer (AJCC), which was built on an anatomical (TNM) framework and must now evolve to incorporate a host of non-anatomic factors for continued relevance and accuracy [4].
Alongside the molecular revolution, existing histopathological and clinical staging systems are being refined for greater clarity and prognostic power. The Grade Group (GG) system in prostate cancer serves as a key example, having successfully replaced the more ambiguous Gleason score with five distinct groups that more accurately stratify risk and guide treatment decisions, particularly in reducing the overtreatment of indolent disease [1]. Similarly, the FIGO staging system for cervical cancer was updated in 2018 to allow for the inclusion of findings from imaging and pathology, a crucial change that provides a more accurate picture of tumor spread than what is possible with clinical assessment alone [6]. Even within specific tumor types, grading criteria are becoming more sophisticated. For lung neuroendocrine tumors, the traditional metrics of mitotic count and necrosis are now often supplemented by the Ki-67 proliferation index, a valuable tool that helps to better stratify risk, especially in cases where the diagnosis is borderline [8]. These refinements demonstrate a concerted effort across specialties to make cancer classification more objective, reproducible, and clinically useful.
The push for objectivity and reproducibility is being powerfully accelerated by computational tools like Artificial Intelligence (AI) and radiomics. In breast cancer histopathology, AI and deep learning algorithms are being developed to automate the grading process from digital slides. These systems can objectively quantify morphological features central to grading, which promises to minimize the well-documented issue of inter-observer variability among pathologists and lead to more consistent and reliable prognostication [3]. A parallel development is occurring in radiology, where radiomics and deep learning are used to analyze medical images such as CT scans and MRIs. These techniques can extract a vast number of quantitative features, many of which are imperceptible to the human eye, to provide a more detailed characterization of a tumor's properties. This offers a non-invasive pathway to enhance the accuracy of local tumor (T) and lymph node (N) staging, potentially reducing the need for surgical biopsies or other invasive procedures [9].
Finally, the frontier of cancer staging is expanding beyond tissue and imaging to include liquid biopsies, a transformative non-invasive technology. By analyzing circulating tumor DNA (ctDNA) from a simple blood sample, clinicians are gaining new insights into cancer dynamics. In colorectal cancer, for instance, liquid biopsies are used to supplement traditional TNM staging by detecting minimal residual disease after surgery. The presence of ctDNA post-operatively is a strong indicator of a high risk of recurrence, allowing for the identification of patients who would benefit most from adjuvant therapy [7]. This ability to monitor disease non-invasively, combined with the integration of molecular data [5, 10] and the analytical power of AI [3, 9], points toward a future of cancer staging that is not static but dynamic, personalized, and far more precise than ever before.
The landscape of cancer grading and staging is undergoing a significant transformation, moving from traditional anatomical and histological assessments toward a more integrated and technologically advanced paradigm. Foundational systems are being updated to provide clearer prognostic information. For instance, the Grade Group (GG) system for prostate cancer has refined risk stratification over the older Gleason score, reducing overtreatment [1]. The World Health Organization (WHO) has been central to this evolution, with its 2021 classification for Central Nervous System (CNS) tumors and 2022 update for bladder cancer mandating the integration of molecular diagnostics with histology [2, 10]. This molecular-first approach is critical for tumors like adult diffuse gliomas, where genetic markers dictate clinical behavior more than microscopic appearance [5]. This shift, however, presents challenges for established frameworks like the AJCC staging system, which must adapt to incorporate non-anatomic factors such as biomarkers [4]. Concurrently, new technologies are revolutionizing the field. Artificial Intelligence (AI) is emerging as a tool to automate breast cancer grading, promising to reduce inter-observer variability [3]. In medical imaging, radiomics and deep learning are extracting quantitative features to improve the accuracy of non-invasive tumor staging [9]. Furthermore, non-invasive methods like liquid biopsies are transforming management for cancers like colorectal cancer by detecting minimal residual disease through circulating tumor DNA (ctDNA), offering insights beyond conventional staging [7]. Even within existing histological practices, such as for lung neuroendocrine tumors, supplementary markers like the Ki-67 index are gaining prominence for better risk assessment [8]. The FIGO system for cervical cancer has also been updated to include imaging findings for more accurate staging [6]. Collectively, these advancements point toward a future of more personalized, precise, and dynamic cancer classification.
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Journal of Surgical Pathology and Diagnosis received 15 citations as per Google Scholar report
