Prospective evaluation of watchful waiting for early-stage lung cancer with ground-glass opacity: a single-arm confirmatory multicenter study: Japan Clinical Oncology Group study JCOG1906 (EVERGREEN study).

Among early-stage lung cancers that show ground-glass nodules on chest computed tomography, the lung cancer surgical study group of the Japan Clinical Oncology Group previously reported that nodules of ≤2 cm with a consolidation/tumor ratio of ≤0.25 were considered radiologically non-invasive cancers with a low risk of metastasis. However, there is no consensus based on high-quality evidence about the optimal timing of surgical intervention for radiologically non-invasive cancers. In June 2020, we launched a multi-institutional, single-arm confirmatory trial to evaluate the efficacy and safety of watchful waiting for patients with radiologically non-invasive lung cancer. It is planned that a total of 720 patients will be enrolled from 49 institutes over a 5-year period. The primary endpoint is 10-year overall survival (OS). The secondary endpoints are 5-year OS, operation-free survival, proportion of extensive lung resection, proportion of unresectable progression, lung cancer-specific survival and adverse events.

A multi-institutional randomized phase III trial comparing postoperative radiotherapy to observation after adjuvant chemotherapy in patients with pathological N2 Stage III non-small cell lung cancer: Japan Clinical Oncology Group Study JCOG1916 (J-PORT study).

The standard treatment for pathological N2 Stage III non-small cell lung cancer with negative surgical margins in Japan is cisplatin-based adjuvant chemotherapy. However, recent studies suggest that the addition of thoracic radiotherapy after adjuvant chemotherapy prolongs survival. While thoracic radiotherapy is considered to prolong survival by improving locoregional control, it is known to increase radiation-induced adverse events. We began a randomized controlled trial in January 2021 in Japan to confirm the superiority of radiotherapy over observation after adjuvant chemotherapy in pathological N2 Stage III non-small cell lung cancer patients with negative surgical margins. We aim to accrue 330 patients from 47 institutions over 5 years. The primary endpoint is relapse-free survival; the secondary endpoints are overall survival, proportion of patients completing radiotherapy in the radiotherapy arm, early adverse events, late adverse events in the radiotherapy arm, serious adverse events and local recurrence.

A multi-institutional randomized phase III study comparing weekly carboplatin plus nab-paclitaxel and daily low-dose carboplatin as regimens for concurrent chemoradiotherapy in elderly patients with unresectable locally advanced non-small cell lung cancer: Japan Clinical Oncology Group Study JCOG1914.

Daily low-dose carboplatin plus concurrent thoracic radiotherapy is the standard treatment for elderly patients with unresectable clinical stage (c-Stage) III non-small cell lung cancer (NSCLC) in Japan. However, a phase I study by Omori et al. suggests that weekly carboplatin and nab-paclitaxel plus concurrent thoracic radiotherapy have comparable efficacy outcomes with more manageable adverse events. In December 2020, we initiated a randomized controlled trial in Japan to confirm whether the weekly carboplatin plus nab-paclitaxel regimen is noninferior to the daily low-dose carboplatin regimen for concurrent chemoradiotherapy in elderly patients with unresectable c-Stage III NSCLC. We plan to enroll 166 patients from 50 institutions in 3.5 years. The primary endpoint is overall survival. The secondary endpoints are progression-free survival, response rate, proportion of patients starting maintenance durvalumab therapy, adverse events, site of progression, Functional Assessment of Cancer Therapy-Trial Outcome Index deterioration and Instrumental Activities of Daily Living deterioration.

Study protocol for a multi-institutional randomized phase III study comparing combined everolimus plus lanreotide therapy and everolimus monotherapy in patients with unresectable or recurrent gastroenteropancreatic neuroendocrine tumors; Japan Clinical Oncology Group Study JCOG1901 (STARTER-NET study).

Everolimus is recognized as one of the standard drugs for the treatment of unresectable or recurrent gastroenteropancreatic neuroendocrine tumors (NET). However, recent evidence has suggested that addition of somatostatin analogs to everolimus may yield better survival outcomes as compared to everolimus alone. In April 2020, we have initiated a randomized phase III trial in Japan, to confirm the superiority of combined everolimus plus lanreotide therapy over everolimus monotherapy in patients with unresectable or recurrent gastroenteropancreatic NETs with poor prognostic factors (Ki-67 labeling index: LI 5%-20% or Ki-67 LI < 5% with diffuse liver metastases). We plan to enroll a total of 250 patients from 76 institutions over an accrual period of 5 years. The primary endpoint is progression-free survival. The key secondary endpoint is overall survival, with response rate, disease control rate, and proportion of patients with adverse events as the other secondary endpoints. This trial is registered with the Japan Registry of Clinical Trials as jRCT1031200023 [https://jrct.niph.go.jp/en-latest-detail/jRCT1031200023].

A multi-institutional randomized phase III trial comparing anatomical segmentectomy and wedge resection for clinical stage IA non-small cell lung cancer in high-risk operable patients: Japan Clinical Oncology Group Study JCOG1909 (ANSWER study).

Anatomical segmentectomy or wedge resection is recommended for high-risk operable patients with clinical stage IA non-small cell lung cancer in guidelines of the National Comprehensive Cancer Network and the Japanese Lung Cancer Society. However, there is no clear evidence comparing the sublobar resections. The less invasive and more generally performed is wedge resection but anatomical segmentectomy may have better survival benefits than wedge resection owing to its superiority in locoregional control. In April 2020, we have initiated a randomized phase III trial in Japan to confirm the superiority of anatomical segmentectomy over wedge resection in high-risk operable patients with clinical stage IA non-small cell lung cancer. We plan to enroll a total of 370 patients from 47 institutions over a period of 5 years. The primary endpoint is overall survival; the secondary endpoints are adverse events, postoperative respiratory function, relapse-free survival, proportion of local recurrence, operative time and blood loss.

[Recurrent Gastric Cancer with Tumor Bleeding from a Metastatic Lymph Node Invading the Duodenal Stump Successfully Controlled by Palliative Radiotherapy-A Case Report].

An 82-year-old man receiving oral administration of warfarin for atrial fibrillation underwent distal gastrectomy for advanced gastric cancer. The postoperative diagnosis was pT3(SS)N2M1H1P0CY0, pStage Ⅳ,(HER2, score 3+)gastric cancer. He received chemotherapy for the treatment of multiple liver metastases, following which, he developed lymph node metastases. Grade 3 anemia was observed at 46 months after initiation of chemotherapy when he was treated with third-line irinotecan plus cisplatin. Abdominal CT showed that CR for liver metastases and SD for lymph node metastases were maintained. Esophagogastroduodenoscopy and colonoscopy showed no intraluminal bleeding. As the anemia progressed, blood transfusion was required repeatedly instead of withdrawal of chemotherapy and replacement therapy of iron and vitamin B12. Double- balloon endoscopy revealed hemorrhagic tumor at duodenal stump. We diagnosed tumor bleeding from metastatic lymph node around pancreatic head invading to duodenum. Palliative radiotherapy(40 Gy/20 Fr)for hemostasis was performed. Finally, hemostasis and tumor shrinkage were achieved.

Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology.

With the completion of the International Human Genome Project, we have entered what is known as the post-genome era, and efforts to apply genomic information to medicine have become more active. In particular, with the announcement of the Precision Medicine Initiative by U.S. President Barack Obama in his State of the Union address at the beginning of 2015, "precision medicine," which aims to divide patients and potential patients into subgroups with respect to disease susceptibility, has become the focus of worldwide attention. The field of oncology is also actively adopting the precision oncology approach, which is based on molecular profiling, such as genomic information, to select the appropriate treatment. However, the current precision oncology is dominated by a method called targeted-gene panel (TGP), which uses next-generation sequencing (NGS) to analyze a limited number of specific cancer-related genes and suggest optimal treatments, but this method causes the problem that the number of patients who benefit from it is limited. In order to steadily develop precision oncology, it is necessary to integrate and analyze more detailed omics data, such as whole genome data and epigenome data. On the other hand, with the advancement of analysis technologies such as NGS, the amount of data obtained by omics analysis has become enormous, and artificial intelligence (AI) technologies, mainly machine learning (ML) technologies, are being actively used to make more efficient and accurate predictions. In this review, we will focus on whole genome sequencing (WGS) analysis and epigenome analysis, introduce the latest results of omics analysis using ML technologies for the development of precision oncology, and discuss the future prospects.

Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics.

The coronavirus disease 2019 (COVID-19) pandemic began at the end of December 2019, giving rise to a high rate of infections and causing COVID-19-associated deaths worldwide. It was first reported in Wuhan, China, and since then, not only global leaders, organizations, and pharmaceutical/biotech companies, but also researchers, have directed their efforts toward overcoming this threat. The use of artificial intelligence (AI) has recently surged internationally and has been applied to diverse aspects of many problems. The benefits of using AI are now widely accepted, and many studies have shown great success in medical research on tasks, such as the classification, detection, and prediction of disease, or even patient outcome. In fact, AI technology has been actively employed in various ways in COVID-19 research, and several clinical applications of AI-equipped medical devices for the diagnosis of COVID-19 have already been reported. Hence, in this review, we summarize the latest studies that focus on medical imaging analysis, drug discovery, and therapeutics such as vaccine development and public health decision-making using AI. This survey clarifies the advantages of using AI in the fight against COVID-19 and provides future directions for tackling the COVID-19 pandemic using AI techniques.

Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data.

Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide. With increasing mortality figures, the accurate prediction of prognosis has become essential. In recent years, multi-omics analysis has emerged as a useful survival prediction tool. However, the methodology relevant to multi-omics analysis has not yet been fully established and further improvements are required for clinical applications. In this study, we developed a novel method to accurately predict the survival of patients with lung cancer using multi-omics data. With unsupervised learning techniques, survival-associated subtypes in non-small cell lung cancer were first detected using the multi-omics datasets from six categories in The Cancer Genome Atlas (TCGA). The new subtypes, referred to as integration survival subtypes, clearly divided patients into longer and shorter-surviving groups (log-rank test: p = 0.003) and we confirmed that this is independent of histopathological classification (Chi-square test of independence: p = 0.94). Next, an attempt was made to detect the integration survival subtypes using only one categorical dataset. Our machine learning model that was only trained on the reverse phase protein array (RPPA) could accurately predict the integration survival subtypes (AUC = 0.99). The predicted subtypes could also distinguish between high and low risk patients (log-rank test: p = 0.012). Overall, this study explores novel potentials of multi-omics analysis to accurately predict the prognosis of patients with lung cancer.

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine.

In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, "precision medicine," a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.