Practical Clinical Oncology Pdf

[Irmgard Rossie]

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Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated. In this review, we present the opportunities and challenges of applying digital twins in clinical oncology, with a particular focus on integrating medical imaging with mechanism-based, tissue-scale mathematical modeling. Specifically, we first introduce the general digital twin framework and then illustrate existing applications of image-guided digital twins in healthcare. Next, we detail both the imaging and modeling techniques that provide practical opportunities to build patient-specific digital twins for oncology. We then describe the current challenges and limitations in developing image-guided, mechanism-based digital twins for oncology along with potential solutions. We conclude by outlining five fundamental questions that can serve as a roadmap when designing and building a practical digital twin for oncology and attempt to provide answers for a specific application to brain cancer. We hope that this contribution provides motivation for the imaging science, oncology, and computational communities to develop practical digital twin technologies to improve the care of patients battling cancer.

Catalog Description:Provide the student a base knowledge and overview of medical physics in the environment of a modern radiation oncology clinical practice, opportunities for practical clinical training as a Medical Physicist, and a familiarity with the roles and practices of the clinical team tasked with the treatment of cancer patients.

This course will include lectures and clinical participation. Students will participate in routine clinical medical physics activities weekly. These will include hands-on experience with therapy systems, analysis of measurements, familiarization with the clinical responsibilities of a Medical Physicist, and understanding the safety protocols related to using radiation therapy sytems. Attendance will be expected both during and after normal clinical hours. Specific clinically-related tasks will be assigned to students, which they will perform under the direction of the course instructor and clinical staff members. Each week students will spend 3 hours of lecture time and 2 hours of practicum time. Practicum will consist equipment demonstration and clinical procedure presentation as well as directed shadowing experience to be held during clinical hours

Since ASTRO last issued recommendations for partial breast radiation in 2017, multiple randomized trials have published results comparing clinical outcomes with partial breast and whole breast radiation, consistently finding no significant differences in recurrence in the same breast, overall survival or cancer-free survival, as well as similar or improved side effects with the partial breast treatment. Evidence from these clinical trials was included in a systematic review conducted by the Agency for Healthcare Research and Quality (AHRQ) to develop the guideline.

The guideline was based on a systematic literature review and comparative effectiveness evidence review conducted by AHRQ, which was funded by the Patient-Centered Outcomes Research Institute (PCORI). The AHRQ review included articles published through June 2022; additional details are available on the AHRQ website.

The multidisciplinary guideline task force included academic and community-based radiation, medical and surgical oncologists, a medical physicist and a patient representative. The guideline was developed in collaboration with the American Society of Clinical Oncology and the Society of Surgical Oncology and is endorsed by the Canadian Association of Radiation Oncology, the European Society for Radiotherapy and Oncology and the Royal Australian and New Zealand College of Radiologists.

ASTRO's clinical guidelines are intended as tools to promote appropriately individualized, shared decision-making between physicians and patients. None should be construed as strict or superseding the appropriately informed and considered judgments of individual physicians and patients.

The American Society for Radiation Oncology (ASTRO) is the largest radiation oncology society in the world, with nearly 10,000 members who are physicians, nurses, biologists, physicists, radiation therapists, dosimetrists and other health care professionals who specialize in treating patients with radiation therapies. Radiation therapy contributes to 40% of global cancer cures, and more than a million Americans receive radiation treatments for cancer each year. For information on radiation therapy, visit RTAnswers.org. To learn more about ASTRO, visit our website and media center and follow us on social media.

Abstract: In this article, we highlight the role and achievement of an under-recognized group: biostatisticians who center their research and collaboration in cancer therapeutic development. By organizing a special series of invited papers from distinguished statisticians who have made significant contributions to the area of oncology clinical trials, we hope to raise the recognition of the critical roles of biostatisticians, strengthen the communication between oncologists and statisticians, stimulate more innovative research in clinical trial designs, and ultimately discover revolutionary anti-cancer treatment in the near future.

It has been a long-standing paradigm that clinical oncology studies are indeed the collaborations requiring multi-disciplinary expertise. Among these scientists, there is an under-recognized group: biostatisticians who center their research and collaboration in cancer therapeutic development. Here we provide an introduction to the topic of biostatistics in oncology research. In subsequent issues, a series of invited papers will present perspectives regarding key aspects of clinical oncology studies from a group of biostatisticians who have made significant contributions to this area. Through this endeavor, we hope to raise the recognition of the critical roles of biostatisticians, strengthen the communication between oncologists and statisticians, stimulate more innovative research in clinical trial designs, and ultimately discover revolutionary anti-cancer treatment in the near future.

In the modern age, the word clinical generally refers to the care of human patients. Clinical trials are series of experimental studies aimed to search for effective treatments, compare the benefits of competing therapies, and/or establish the optimal therapeutic combinations and/or sequences of treatment. That the testing subjects are human patients is the first fact that distinguishes clinical trials from laboratory bench experiments. Genetic, behavioral and environmental heterogeneities among patients introduce great complexities in disease progresses and the impact (beneficial or harmful) of the studied treatment. While the knowledge gained by medical observation on individual patients has contributed to the advances in medicine historically, individual patient experience has proven to be not sufficient to be generalized to the population. This brings to the second distinguishable feature of clinical trials: a well-design and performed clinical trial provides generalizable inference of the tested regimen to the population level. This is mainly achieved by well-controlled and/or described person-to-person variability from known or unknown sources, and by minimizing the instrumental errors and biases through rigorous trial design and conduct.

Even though oncologists tackle the problems of cancer from biological point of view, whereas statisticians choose the pathway of mathematic modeling, making generalizable inferences based on observable data is indeed the essential task in both the clinical and statistical fields. The strategy of updating our knowledge of fighting cancers by combining emerging new data and existing old data, and making reliable decisions of adapting or abandoning a treatment at the population level, naturally bring oncologists and statisticians together to the same field of clinical trials. Statisticians provide the core techniques to transfer the conceptional ideas initiated by oncologists into sound and practical clinical trials throughout the entire lifetime of the study.

Planning a clinical trial is never as simple as one task of running a sample size calculation, although this calculation does require sufficient statistical training, particularly in the area of experimental design of medical studies. Planning a clinical trial is an interactive and iterative collaboration process between oncologists and statisticians. The conversations usually start with the dissection of the proposed hypothesis regarding a newly discovered agent or regimen. The key information a statistician will gather includes: the targeted disease population, the expected treatment effect, the most relevant endpoint, historical evidence, and allowable error rates.

At this point, one might think that the statistician can wave his or her magic wand to find the sample size of the study. Unfortunately, this thought is still rather nave. More conversations between the oncologist and the statistician regarding the study design are necessary and essential. Actually, these conversations are never just limited between two of them (statistician and oncologist). Discussions with pathologists and radiologists are valuable for rigorously outlining issues surrounding population definition and the endpoint ascertainment. Discussions with regulatory agents and/or government funding provider strengthens the feasibility and scientific rationale of the study. Inputs from potential enrolling physicians and patient advocates bring practice and ethical considerations into the investigation. All these considerations frame the study design to be scientifically sound, yet practically feasible and efficient.

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