The American Joint Committee on Cancer (AJCC) staging manual has become the benchmark for classifying patients with cancer, defining prognosis, and determining the best treatment approaches. Many view the primary role of the tumor, lymph node, metastasis (TNM) system as that of a standardized classification system for evaluating cancer at a population level in terms of the extent of disease, both at initial presentation and after surgical treatment, and the overall impact of improvements in cancer treatment. The rapid evolution of knowledge in cancer biology and the discovery and validation of biologic factors that predict cancer outcome and response to treatment with better accuracy have led some cancer experts to question the utility of a TNM-based approach in clinical care at an individualized patient level. In the Eighth Edition of the AJCC Cancer Staging Manual, the goal of including relevant, nonanatomic (including molecular) factors has been foremost, although changes are made only when there is strong evidence for inclusion. The editorial board viewed this iteration as a proactive effort to continue to build the important bridge from a "population-based" to a more "personalized" approach to patient classification, one that forms the conceptual framework and foundation of cancer staging in the era of precision molecular oncology. The AJCC promulgates best staging practices through each new edition in an effort to provide cancer care providers with a powerful, knowledge-based resource for the battle against cancer. In this commentary, the authors highlight the overall organizational and structural changes as well as "what's new" in the Eighth Edition. It is hoped that this information will provide the reader with a better understanding of the rationale behind the aggregate proposed changes and the exciting developments in the upcoming edition. CA Cancer J Clin 2017;67:93-99. © 2017 American Cancer Society.

https://acsjournals.onlinelibrary.wiley.com/doi/epdf/10.3322/caac.21388

The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population‐based to a more “personalized” approach to cancer staging

https://air.unimi.it/bitstream/2434/586890/2/PIIS0092867418302290.pdf

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics.

Gastric cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up†

Head and Neck Cancer Most head and neck cancers (73% in the United States) are now related to human papillomavirus infection rather than tobacco and alcohol. Primary cancers are largely squamous-ce...

https://www.nejm.org/doi/pdf/10.1056/NEJMra1715715

Head and Neck Cancer

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

https://acsjournals.onlinelibrary.wiley.com/doi/epdf/10.3322/caac.21552

Artificial intelligence in cancer imaging: Clinical challenges and applications.

The American Joint Committee on Cancer (AJCC) has increasingly recognized the need for more personalized probabilistic predictions than those delivered by ordinal staging systems, particularly through the use of accurate risk models or calculators. However, judging the quality and acceptability of a risk model is complex. The AJCC Precision Medicine Core conducted a 2-day meeting to discuss characteristics necessary for a quality risk model in cancer patients. More specifically, the committee established inclusion and exclusion criteria necessary for a risk model to potentially be endorsed by the AJCC. This committee reviewed and discussed relevant literature before creating a checklist unique to this need of AJCC risk model endorsement. The committee identified 13 inclusion and 3 exclusion criteria for AJCC risk model endorsement in cancer. The emphasis centered on performance metrics, implementation clarity, and clinical relevance. The facilitation of personalized probabilistic predictions for cancer patients holds tremendous promise, and these criteria will hopefully greatly accelerate this process. Moreover, these criteria might be useful for a general audience when trying to judge the potential applicability of a published risk model in any clinical domain. CA Cancer J Clin 2016;66:370-374. © 2016 American Cancer Society.

https://onlinelibrary.wiley.com/doi/pdf/10.3322/caac.21339

American Joint Committee on Cancer acceptance criteria for inclusion of risk models for individualized prognosis in the practice of precision medicine.

Carolyn C. Compton

Papers

The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population‐based to a more “personalized” approach to cancer staging

American Joint Committee on Cancer acceptance criteria for inclusion of risk models for individualized prognosis in the practice of precision medicine.

Comprar AJCC Cancer Staging Manual + cd-rom 7th Ed | Trotti, Andrea | 9780387884400 | Springer

Organization of the AJCC Cancer Staging Manual

AJCC cancer staging manual. 7th edition. Colon and rectum