Correlation between gastric volume and organs at risk dose in adjuvant radiotherapy for left breast cancer

BACKGROUND: The role of the gastric volume on the dose-effect relationship for these organs has not been investigated. The aim of the study was to evaluate the correlation between gastric volume and dose-volume histogram (DVH) parameters of the heart, left lung and stomach during left breast cancer radiotherapy (RT). MATERIALS AND METHODS: Ninety-nine left breast cancer patients who got adjuvant radiotherapy were included. Study was classified into two groups based on treatment field arrangements: 1) breast tangential fields only (T) and 2) breast tangential and supraclavicular fields (TS). Organs DVHs were extracted. Descriptive statistics, Pearson correlation, linear regression analyses, and receiver operating characteristic (ROC) analyses were performed. RESULTS: There is a direct but not significant correlation between the gastric volume and doses to the stomach and left lung. For a 100-cc increase in the gastric volume, the stomach maximum dose and the V50 increased by 3 Gy and 4%, respectively. For the left lung, V4 and V5 increased by 1% for TS cases. Considering ROC analysis results, one can make a decision for about 74% of patients due to their left lung DVH parameters, using gastric volume as a known input data. The correlation between gastric volume and heart dose was not significant. CONCLUSIONS: The gastric volume of about 170 cc or less can result in lower dose to the stomach and ipsilateral lung during left breast cancer radiotherapy, especially for TS cases. To reach this gastric volume threshold, patients should be fast for 2 hours before the procedure of CT simulation and treatment

Cancer Digital Twins in Metaverse

The Metaverse is an emerging technology to make virtual environments for users to benefit from a huge number of virtual services, while users experience immersive interactions with the real world. Digital twins, which are representatives of assets in this virtual world, play an important role to connect this environment to the actual world. Therefore, translating problematic assets, objects, and disease like cancers to this cyber world provide patients with this opportunity to benefit from its advantages. This study aims to conceptualize an approach to how machine learning (ML) can realize real-time and robust digital twins of cancers to be used in the Metaverse for diagnosis and treatment. While there are a large number of ML methods, which have advantages based on the various types of healthcare data, four classic ML techniques, including ML linear regression (ML LR), decision tree regression (DTR), Random Forest Regression (RFR), and Gradient Boosting Algorithm (GBA), have been employed to implement the main part of this approach in this research. Moreover, a comprehensive conceptual framework of the ML digital twinning method has been presented to illustrate the process of digital twining cancers with different medical data

Cancer Digital Twins in Metaverse

The Metaverse is an emerging technology to make virtual environments for users to benefit from a huge number of virtual services, while users experience immersive interactions with the real world. Digital twins, which are representatives of assets in this virtual world, play an important role to connect this environment to the actual world. Therefore, translating problematic assets, objects, and disease like cancers to this cyber world provide patients with this opportunity to benefit from its advantages. This study aims to conceptualize an approach to how machine learning (ML) can realize real-time and robust digital twins of cancers to be used in the Metaverse for diagnosis and treatment. While there are a large number of ML methods, which have advantages based on the various types of healthcare data, four classic ML techniques, including ML linear regression (ML LR), decision tree regression (DTR), Random Forest Regression (RFR), and Gradient Boosting Algorithm (GBA), have been employed to implement the main part of this approach in this research. Moreover, a comprehensive conceptual framework of the ML digital twinning method has been presented to illustrate the process of digital twining cancers with different medical data

Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer

Medical digital twins, which represent medical assets, play a crucial role in connecting the physical world to the metaverse, enabling patients to access virtual medical services and experience immersive interactions with the real world. One serious disease that can be diagnosed and treated using this technology is cancer. However, the digitalization of such diseases for use in the metaverse is a highly complex process. To address this, this study aims to use machine learning (ML) techniques to create real-time and reliable digital twins of cancer for diagnostic and therapeutic purposes. The study focuses on four classical ML techniques that are simple and fast for medical specialists without extensive Artificial Intelligence (AI) knowledge, and meet the requirements of the Internet of Medical Things (IoMT) in terms of latency and cost. The case study focuses on breast cancer (BC), the second most prevalent form of cancer worldwide. The study also presents a comprehensive conceptual framework to illustrate the process of creating digital twins of cancer, and demonstrates the feasibility and reliability of these digital twins in monitoring, diagnosing, and predicting medical parameters

Treatment of Patients With Early-Stage Colorectal Cancer: ASCO Resource-Stratified Guideline

PURPOSE To provide resource-stratified, evidence-based recommendations on the treatment and follow-up of patients with early-stage colorectal cancer. METHODS ASCO convened a multidisciplinary, multinational Expert Panel that reviewed existing guidelines and conducted a modified ADAPTE process and a formal consensus process with additional experts for one round of formal ratings. RESULTS Existing sets of guidelines from 12 guideline developers were identified and reviewed; adapted recommendations from six guidelines form the evidence base and provide evidence to inform the formal consensus process, which resulted in agreement of 75% or more on all recommendations. RECOMMENDATIONS For nonmaximal settings, the recommended treatments for colon cancer stages nonobstructing, I-IIA: in basic and limited, open resection; in enhanced, adequately trained surgeons and laparoscopic or minimally invasive surgery, unless contraindicated. Treatments for IIB-IIC: in basic and limited, open en bloc resection following standard oncologic principles, if not possible, transfer to higher-level facility; in emergency, limit to life-saving procedures; in enhanced, laparoscopic en bloc resection, if not possible, then open. Treatments for obstructing, IIB-IIC: in basic, resection and/or diversion; in limited or enhanced, emergency surgical resection. Treatment for IIB-IIC with left-sided: in enhanced, may place colonic stent. Treatment for T4N0/T3N0 high-risk features or stage II high-risk obstructing: in enhanced, may offer adjuvant chemotherapy. Treatment for rectal cancer cT1N0 and cT2n0: in basic, limited, or enhanced, total mesorectal excision principles. Treatment for cT3n0: in basic and limited, total mesorectal excision, if not, diversion. Treatment for high-risk patients who did not receive neoadjuvant chemotherapy: in basic, limited, or enhanced, may offer adjuvant therapy. Treatment for resectable cT3N0 rectal cancer: in enhanced, base neoadjuvant chemotherapy on preoperative factors. For post-treatment surveillance, a combination of medical history, physical examination, carcinoembryonic antigen testing, imaging, and endoscopy is performed. Frequency depends on setting. Maximal setting recommendations are in the guideline. Additional information can be found at www.asco.org/resource-stratified-guidelines . NOTICE It is the view of the American Society of Clinical Oncology that health care providers and health care system decision makers should be guided by the recommendations for the highest stratum of resources available. The guidelines are intended to complement but not replace local guidelines

Early Detection for Colorectal Cancer: ASCO Resource-Stratified Guideline

PURPOSE To provide resource-stratified, evidence-based recommendations on the early detection of colorectal cancer in four tiers to clinicians, patients, and caregivers. METHODS American Society of Clinical Oncology convened a multidisciplinary, multinational panel of medical oncology, surgical oncology, surgery, gastroenterology, health technology assessment, cancer epidemiology, pathology, radiology, radiation oncology, and patient advocacy experts. The Expert Panel reviewed existing guidelines and conducted a modified ADAPTE process and a formal consensus-based process with additional experts (Consensus Ratings Group) for two round(s) of formal ratings. RESULTS Existing sets of guidelines from eight guideline developers were identified and reviewed; adapted recommendations form the evidence base. These guidelines, along with cost-effectiveness analyses, provided evidence to inform the formal consensus process, which resulted in agreement of 75% or more. CONCLUSION In nonmaximal settings, for people who are asymptomatic, are ages 50 to 75 years, have no family history of colorectal cancer, are at average risk, and are in settings with high incidences of colorectal cancer, the following screening options are recommended: guaiac fecal occult blood test and fecal immunochemical testing (basic), flexible sigmoidoscopy (add option in limited), and colonoscopy (add option in enhanced). Optimal reflex testing strategy for persons with positive screens is as follows: endoscopy; if not available, barium enema (basic or limited). Management of polyps in enhanced is as follows: colonoscopy, polypectomy; if not suitable, then surgical resection. For workup and diagnosis of people with symptoms, physical exam with digital rectal examination, double contrast barium enema (only in basic and limited); colonoscopy; flexible sigmoidoscopy with biopsy (if contraindication to latter) or computed tomography colonography if contraindications to two endoscopies (enhanced only)