The field of onco-microbiome is rapidly expanding. Multiple studies have shown the crucial role of gut microbiota in the regulation of nutrient metabolism, immunomodulation and protection against pathogens. Tools for manipulating the gut microbiota include dietary modification and faecal microbiota transfer. Accumulating evidence has also documented the application of specific intestinal microbiome in cancer immunotherapy, notably in enhancing the efficacy of immune checkpoint inhibitors. The aim of this review is to focus on the East Asian microbiome and to provide a current overview of microbiome science and its clinical application in cancer biology and immunotherapy.
Humans ; Gastrointestinal Microbiome ; Neoplasms/microbiology* ; Immunotherapy/methods* ; Asia, Eastern ; Medical Oncology ; Fecal Microbiota Transplantation ; Immune Checkpoint Inhibitors/therapeutic use* ; East Asian People

With the rapid development of artificial intelligence, computational pathology has been seamlessly integrated into the entire clinical workflow, which encompasses diagnosis, treatment, prognosis, and biomarker discovery. This integration has significantly enhanced clinical accuracy and efficiency while reducing the workload for clinicians. Traditionally, research in this field has depended on the collection and labeling of large datasets for specific tasks, followed by the development of task-specific computational pathology models. However, this approach is labor intensive and does not scale efficiently for open-set identification or rare diseases. Given the diversity of clinical tasks, training individual models from scratch to address the whole spectrum of clinical tasks in the pathology workflow is impractical, which highlights the urgent need to transition from task-specific models to foundation models (FMs). In recent years, pathological FMs have proliferated. These FMs can be classified into three categories, namely, pathology image FMs, pathology image-text FMs, and pathology image-gene FMs, each of which results in distinct functionalities and application scenarios. This review provides an overview of the latest research advancements in pathological FMs, with a particular emphasis on their applications in oncology. The key challenges and opportunities presented by pathological FMs in precision oncology are also explored.
Humans ; Precision Medicine/methods* ; Medical Oncology/methods* ; Artificial Intelligence ; Neoplasms/pathology* ; Computational Biology/methods*

Primary lung cancer is the most common malignant disease and the leading cause of cancer death in China, with an estimated 828 thousand incident cases and 657 thousand deaths in 2016. Due to the absence of effective early screening methods, most patients with lung cancer are in stage Ⅳ when diagnosed. Multi-disciplinary treatment based on systemic therapy is the treatment principle for patients with stage Ⅳ lung cancer, chemotherapy is the cornerstone of stage Ⅳ lung cancer, but its efficacy is unsatisfactory. In recent years, with the rapid development of molecular targeted therapy and immunotherapy, the treatment concept has continuously changed and treatment outcome for patients has also been greatly improved. In order to update the progress in the treatment of stage Ⅳ lung cancer worldwide timely, and further improve the level of standardized diagnosis and treatment of stage Ⅳ lung cancer in China, Chinese Association for Clinical Oncologists and Medical Oncology Branch of Chinese International Exchange and Promotion Association for Medical and Healthcare organized experts to compose "Clinical Practice Guideline for Stage Ⅳ Primary Lung Cancer in China (2023 edition)" .
Humans ; Lung Neoplasms/drug therapy* ; Immunotherapy ; Molecular Targeted Therapy ; China/epidemiology* ; Medical Oncology

Humans ; Cardiologists ; Neoplasms/drug therapy* ; Medical Oncology ; Heart ; Oncologists

Background: To respond to the pandemic, many societies, including the American Society for Radiation Therapy (ASTRO), the United Kingdom’s National Institute for Health and Care Excellence (NICE), and the Philippine Radiation Oncology Society (PROS), recommended guidelines to allow for continued safe delivery of oncologic services. Yet, the delivery of radiotherapy during the COVID-19 pandemic remains a challenge. Objective: To describe the situation of radiotherapy delivery in Metro Manila (NCR) during the COVID-19-related quarantine. Specifically, the objectives were to determine: (1) how the radiotherapy providers implemented the recommended changes, (2) if these implemented changes allowed the hospitals to operate with pre-COVID capacities, and (3) the causative factors of treatment interruptions if these were present. Additionally, in the face of treatment interruptions, the authors sought to put forth recommendations to decrease treatment interruptions. Methods: Investigators gathered data on the prevailing situation of RT services in their respective institutions during the strictest period of quarantine — Enhanced Community Quarantine (ECQ). Patients aged 18-70 years old who missed at least one fraction during the ECQ from March 16 – April 15, 2020, were invited to participate in a phone survey to determine factors contributing to treatment interruptions. Results: All the institutions implemented global recommendations to adapt to the pandemic, including infection control measures, telemedicine, and modification of RT plans. Despite this, most institutions had increased treatment interruptions during ECQ. The percentage of patients with interruptions was also much higher during the ECQ (66.37%) than during the pre-COVID month (30.56%). Among 142 patients unable to continue treatment, there were no significant differences in demographic variable and oncologic profile rates. The majority were more worried about getting COVID-19 than missing RT. The most common factor for treatment interruptions was transportation, followed by fear of getting COVID-19. Conclusion Compliance with global recommendations is not enough to ensure that the patients who require radiotherapy will receive it. Based on institutional and patient results, the causative factors of interruptions included suspension of services, lack of transportation, and anxiety of patients and staff. Especially in low-resource settings, recommendations are to use available resources as efficiently as possible by having an organized referral system, providing transportation or nearby accommodation for patients and staff, and communicating effectively to reassure patients that radiotherapy can be continued safely.
radiotherapy ; radiation therapy ; radiation oncology

Female ; Humans ; Breast Neoplasms/surgery* ; Inflammatory Breast Neoplasms ; Mastectomy ; Medical Oncology

Genome-guided oncology refers to a new treatment concept that transcends histological classification and pathological ty-ping and uses drugs according to the genetic characteristics of tumors. New drug development technology and clinical trial design based on this concept provide new ideas for the clinical application of precision oncology. The multi-component and multi-target characteristics of Chinese medicine provide rich resources for the development of tumor-targeting drugs from natural products, and the design of the master protocol trial aiming at the characteristics of precision oncology supports the rapid clinical screening of effective tumor-targeting drugs. The emergence of the synthetic lethality strategy breaks through the bottleneck that the drug can only target the oncogene but cannot do anything to the tumor suppressor gene with the loss-of-function mutation in the past. With the rapid development of high-throughput sequencing technology, the cost of sequencing is also decreasing. For the development of tumor-targeting drugs, how to keep up with the update speed of target information is a difficult problem of concern. Based on the integration of innovative ideas and me-thods of precision oncology, network pharmacology, and synthetic lethality strategy on synthetic lethal interaction network of antitumor Chinese medicine compatibility formula design, and the combination of improvement of innovative clinical trial methods, such as master protocol trial, basket trial, and umbrella trial, unique advantages of Chinese medicine are expected to be exerted beyond the antibody-based drugs and small molecule-based drugs and corresponding targeted drugs are potentially developed for clinical application.
Humans ; Neoplasms/genetics* ; Medicine, Chinese Traditional ; Precision Medicine/methods* ; Medical Oncology ; Antineoplastic Agents/therapeutic use*

Humans ; COVID-19 ; Neoplasms/complications* ; Lung Diseases ; Medical Oncology

Humans ; Female ; Breast Neoplasms/drug therapy* ; Medical Oncology

OBJECTIVES: Radiation therapy is a main method for female pelvic malignancies, which can cause some adverse reactions, such as radiation proctitis (RP). The incidence of RP is highly positively correlated with radiation dose. There is an urgent need for a scientific method to accurately predict the occurrence of RP to help doctors make clinical decisions. In this study, based on the clinical data of female pelvic tumor patients and dosimetric parameters of radiotherapy, the random forest method was used to screen the hub features related to the occurrence of RP, and then a machine learning algorithm was used to construct a risk prediction model for the occurrence of RP, in order to provide technical support and theoretical basis for the prediction and prevention of RP. METHODS: A total of 100 female patients with pelvic tumors, who received static three-dimensional conformal intensity-modulated radiation therapy in the Department of Radiation Oncology of the Affiliated Hospital of Xiangnan University from January 2019 to December 2020, were retrospectively collected, and their clinically relevant data and radiotherapy planning system data were collected. During radiotherapy and 18 months after radiotherapy, 35 cases developed RP (RP group), and the remaining 65 cases had no RP (non-RP group). The clinical and dosimetric characteristics of patients were ranked by the importance of random forest algorithm, and the independent prognostic characteristics associated with the occurrence of RP were selected for machine learning modeling. A total of 6 machine learning algorithms including support vector machines, random forests, logistic regression, lightweight gradient boosting machines, Gaussian naïve Bayes, and adaptive enhancement were used to build models. The performance of the model was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Finally, the random forest model was determined as the prediction model, and the calibration curve and decision curve of the prediction model were drawn to evaluate the accuracy and clinical benefit of the model. RESULTS: The parameters for random forest prediction model in the training set were as follow: AUC, 1.000, accuracy, 0.988, sensitivity, 1.000, specificity, 1.000, positive predictive value, 1.000, negative predictive value, 0.981, and F1 score, 1.000. In validation set, AUC was 0.713, accuracy was 0.640, sensitivity was 0.618, specificity was 0.822, positive predictive value was 0.500, negative predictive value was 0.656, and F1 score was 0.440. Random forest showed high predictive performance. Moreover, the Brief of the calibration curve for the prediction model was 0.178, the prediction accuracy was high, and the decision curve showed that the prediction model could benefit clinically. CONCLUSIONS Based on the clinical and dosimetric parameters for the female pelvic tumor patients, the prediction model of radiation proctitis constructed by random forest algorithm has high predictive ability and strong clinical usability.
Bayes Theorem ; Female ; Humans ; Machine Learning ; Pelvic Neoplasms/radiotherapy* ; Proctitis/etiology* ; Radiation Oncology ; Retrospective Studies