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The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. In this Perspective, we discuss the next generation of challenges in clinical decision-making that AI tools can solve using radiology images, such as prognostication of outcome across multiple cancers, prediction of response to various treatment modalities, discrimination of benign treatment confounders from true progression, identification of unusual response patterns and prediction of the mutational and molecular profile of tumours. We describe the evolution of and opportunities for AI in oncology imaging, focusing on hand-crafted radiomic approaches and deep learning-derived representations, with examples of their application for decision support. We also address the challenges faced on the path to clinical adoption, including data curation and annotation, interpretability, and regulatory and reimbursement issues. We hope to demystify AI in radiology for clinicians by helping them to understand its limitations and challenges, as well as the opportunities it provides as a decision-support tool in cancer management.Prognostication of outcome across multiple cancers and prediction of response to various treatment modalities are among the next generation of challenges that artificial intelligence (AI) tools can solve using radiology images. The authors of this Perspective describe the evolution of AI-based approaches in oncology imaging and address the path to their adoption as decision-support tools in the clinic.

Sepsis-associated acute kidney injury (SA-AKI) is common in critically ill patients and is strongly associated with adverse outcomes, including an increased risk of chronic kidney disease, cardiovascular events and death. The pathophysiology of SA-AKI remains elusive, although microcirculatory dysfunction, cellular metabolic reprogramming and dysregulated inflammatory responses have been implicated in preclinical studies. SA-AKI is best defined as the occurrence of AKI within 7 days of sepsis onset (diagnosed according to Kidney Disease Improving Global Outcome criteria and Sepsis 3 criteria, respectively). Improving outcomes in SA-AKI is challenging, as patients can present with either clinical or subclinical AKI. Early identification of patients at risk of AKI, or at risk of progressing to severe and/or persistent AKI, is crucial to the timely initiation of adequate supportive measures, including limiting further insults to the kidney. Accordingly, the discovery of biomarkers associated with AKI that can aid in early diagnosis is an area of intensive investigation. Additionally, high-quality evidence on best-practice care of patients with AKI, sepsis and SA-AKI has continued to accrue. Although specific therapeutic options are limited, several clinical trials have evaluated the use of care bundles and extracorporeal techniques as potential therapeutic approaches. Here we provide graded recommendations for managing SA-AKI and highlight priorities for future research.Sepsis-associated acute kidney injury (SA-AKI) is linked with poor outcomes in critically ill patients. This Consensus Statement from the Acute Disease Quality Initiative discusses the definition, epidemiology and pathophysiology of SA-AKI, fluid, resuscitation and extracorporeal therapies, and the role of biomarkers in risk stratification and diagnosis.

N6-Methyladenosine (m6A), the most prevalent internal modification in eukaryotic mRNA, has been extensively and increasingly studied over the past decade. Dysregulation of RNA m6A modification and its associated machinery, including writers, erasers and readers, is frequently observed in various cancer types, and the dysregulation profiles might serve as diagnostic, prognostic and/or predictive biomarkers. Dysregulated m6A modifiers have been shown to function as oncoproteins or tumour suppressors with essential roles in cancer initiation, progression, metastasis, metabolism, therapy resistance and immune evasion as well as in cancer stem cell self-renewal and the tumour microenvironment, highlighting the therapeutic potential of targeting the dysregulated m6A machinery for cancer treatment. In this Review, we discuss the mechanisms by which m6A modifiers determine the fate of target RNAs and thereby influence protein expression, molecular pathways and cell phenotypes. We also describe the state-of-the-art methodologies for mapping global m6A epitranscriptomes in cancer. We further summarize discoveries regarding the dysregulation of m6A modifiers and modifications in cancer, their pathological roles, and the underlying molecular mechanisms. Finally, we discuss m6A-related prognostic and predictive molecular biomarkers in cancer as well as the development of small-molecule inhibitors targeting oncogenic m6A modifiers and their activity in preclinical models.Dysregulation of N6-methyladenosine (m6A), the most prevalent internal modification in eukaryotic mRNA, is common in various cancer types. The authors of this Review provide an overview of the mechanisms of m6A-dependent RNA regulation, summarize current knowledge of their pathological effects and potential utility as biomarkers in cancer, and describe ongoing efforts to develop small-molecule inhibitors of oncogenic m6A modifiers.

Pancreatic ductal adenocarcinoma (PDAC) has a rising incidence and is one of the most lethal human malignancies. Much is known regarding the biology and pathophysiology of PDAC, but translating this knowledge to the clinic to improve patient outcomes has been challenging. In this Review, we discuss advances and practice-changing trials for PDAC. We briefly review therapeutic failures as well as ongoing research to refine the standard of care, including novel biomarkers and clinical trial designs. In addition, we highlight contemporary areas of research, including poly(ADP-ribose) polymerase inhibitors, KRAS-targeted therapies and immunotherapies. Finally, we discuss the future of pancreatic cancer research and areas for improvement in the next decade. The incidence of pancreatic ductal adenocarcinoma (PDAC) is rising. In this Review, Hu and O’Reilly summarize the latest advances and clinical trials for the treatment of PDAC, including biomarkers, targeted therapies and immunotherapy as well as novel clinical trial designs. They also discuss emerging therapeutic options for this disease. Key points Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy, with cytotoxic chemotherapy remaining the mainstay of treatment for most patients. Ongoing trials are evaluating ‘classical’ versus ‘basal-like’ expression signatures to inform the therapeutic selection of standard cytotoxic regimens. Small subsets of patients, including those with mutations in BRCA1 , BRCA2 , PALB2 and KRA S G12C , rare fusions ( NRG1 , NTRK ), and mismatch repair deficiency, benefit from targeted therapies. KRAS is a critical target for therapeutic evaluation in PDAC and proof-of-principle approaches have validated targeting in KRAS G12C ; ongoing trials are evaluating allele-specific inhibitors and pan-(K)RAS inhibitors against the more common allele variants G12D, G12V and G12R. Immune-checkpoint inhibitor blockade and other immune therapies have not had utility for most patients with PDAC; however, select rare individuals beyond mismatch repair deficiency have seen major benefit, leading to optimism that these results can be expanded to a broader patient population. Clinical trial design strategies are changing and PDAC is a prototypic disease to investigate novel drug development paradigms; innovation in clinical trial design has led to the integration of platform-type studies that incorporate novel statistical design, leading to expediencies in drug development, timelines, cost and other resources. Platform studies enable parallel investigation of multiple novel agents, with early signal adjudication of success or failure to allow efficient signal seeking.

An unresolved question for the understanding of Alzheimer’s disease (AD) pathophysiology is why a significant percentage of amyloid-β (Aβ)-positive cognitively unimpaired (CU) individuals do not develop detectable downstream tau pathology and, consequently, clinical deterioration. In vitro evidence suggests that reactive astrocytes unleash Aβ effects in pathological tau phosphorylation. Here, in a biomarker study across three cohorts ( n  = 1,016), we tested whether astrocyte reactivity modulates the association of Aβ with tau phosphorylation in CU individuals. We found that Aβ was associated with increased plasma phosphorylated tau only in individuals positive for astrocyte reactivity (Ast + ). Cross-sectional and longitudinal tau–positron emission tomography analyses revealed an AD-like pattern of tau tangle accumulation as a function of Aβ only in CU Ast + individuals. Our findings suggest astrocyte reactivity as an important upstream event linking Aβ with initial tau pathology, which may have implications for the biological definition of preclinical AD and for selecting CU individuals for clinical trials. Cross-sectional and longitudinal analyses of tau pathology in preclinical Alzheimer’s disease reveal that tau tangles accumulate as a function of amyloid-β burden only in individuals positive for an astrocyte reactivity biomarker.

Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients’ clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464–0.9778), 0.9760 (0.9613–0.9906), and 0.9246 (0.8763–0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients. Methods to stratify patients according to mortality risk are essential to allocate limited heath resources during the COVID-19 crisis. Here, using machine learning methods, the authors present a mortality risk prediction model for COVID-19 that uses patients’ clinical data on admission to stratify patients by mortality risk.

The long-term physical and mental sequelae of COVID-19 are a growing public health concern, yet there is considerable uncertainty about their prevalence, persistence and predictors. We conducted a comprehensive, up-to-date meta-analysis of survivors’ health consequences and sequelae for COVID-19. PubMed, Embase and the Cochrane Library were searched through Sep 30th, 2021. Observational studies that reported the prevalence of sequelae of COVID-19 were included. Two reviewers independently undertook the data extraction and quality assessment. Of the 36,625 records identified, a total of 151 studies were included involving 1,285,407 participants from thirty-two countries. At least one sequelae symptom occurred in 50.1% (95% CI 45.4-54.8) of COVID-19 survivors for up to 12 months after infection. The most common investigation findings included abnormalities on lung CT (56.9%, 95% CI 46.2–67.3) and abnormal pulmonary function tests (45.6%, 95% CI 36.3–55.0), followed by generalized symptoms, such as fatigue (28.7%, 95% CI 21.0–37.0), psychiatric symptoms (19.7%, 95% CI 16.1–23.6) mainly depression (18.3%, 95% CI 13.3–23.8) and PTSD (17.9%, 95% CI 11.6–25.3), and neurological symptoms (18.7%, 95% CI 16.2–21.4), such as cognitive deficits (19.7%, 95% CI 8.8–33.4) and memory impairment (17.5%, 95% CI 8.1–29.6). Subgroup analysis showed that participants with a higher risk of long-term sequelae were older, mostly male, living in a high-income country, with more severe status at acute infection. Individuals with severe infection suffered more from PTSD, sleep disturbance, cognitive deficits, concentration impairment, and gustatory dysfunction. Survivors with mild infection had high burden of anxiety and memory impairment after recovery. Our findings suggest that after recovery from acute COVID-19, half of survivors still have a high burden of either physical or mental sequelae up to at least 12 months. It is important to provide urgent and appropriate prevention and intervention management to preclude persistent or emerging long-term sequelae and to promote the physical and psychiatric wellbeing of COVID-19 survivors.

Worldwide, lung cancer is the most common cause of cancer-related deaths. Molecular targeted therapies and immunotherapies for non-small-cell lung cancer (NSCLC) have improved outcomes markedly over the past two decades. However, the vast majority of advanced NSCLCs become resistant to current treatments and eventually progress. In this Perspective, we discuss some of the recent breakthrough therapies developed for NSCLC, focusing on immunotherapies and targeted therapies. We highlight our current understanding of mechanisms of resistance and the importance of incorporating genomic analyses into clinical studies to decipher these further. We underscore the future role of neoadjuvant and maintenance combination therapy approaches to potentially cure early disease. A major challenge to successful development of rational combination therapies will be the application of robust predictive biomarkers for clear-cut patient stratification, and we provide our views on clinical research areas that could influence how NSCLC will be managed over the coming decade. This Perspective discusses recent developments in NSCLC immunotherapy and targeted therapy, and highlights the key challenges and future directions for NSCLC management.

Biomarkers for neurodegenerative diseases are needed to improve the diagnostic workup in the clinic but also to facilitate the development and monitoring of effective disease-modifying therapies. Positron emission tomography methods detecting amyloid-β and tau pathology in Alzheimer’s disease have been increasingly used to improve the design of clinical trials and observational studies. In recent years, easily accessible and cost-effective blood-based biomarkers detecting the same Alzheimer’s disease pathologies have been developed, which might revolutionize the diagnostic workup of Alzheimer’s disease globally. Relevant biomarkers for α-synuclein pathology in Parkinson’s disease are also emerging, as well as blood-based markers of general neurodegeneration and glial activation. This review presents an overview of the latest advances in the field of biomarkers for neurodegenerative diseases. Future directions are discussed regarding implementation of novel biomarkers in clinical practice and trials. As the development of biomarkers for neurodegenerative diseases advances, new opportunities arise for their implementation in clinical practice and trials.

Despite standard-of-care treatment, more than 30% of patients with resectable colorectal cancer (CRC) relapse. Circulating tumor DNA (ctDNA) analysis may enable postsurgical risk stratification and adjuvant chemotherapy (ACT) treatment decision-making. We report results from GALAXY, which is an observational arm of the ongoing CIRCULATE-Japan study (UMIN000039205) that analyzed presurgical and postsurgical ctDNA in patients with stage II–IV resectable CRC ( n  = 1,039). In this cohort, with a median follow-up of 16.74 months (range 0.49–24.83 months), postsurgical ctDNA positivity (at 4 weeks after surgery) was associated with higher recurrence risk (hazard ratio (HR) 10.0, P  < 0.0001) and was the most significant prognostic factor associated with recurrence risk in patients with stage II or III CRC (HR 10.82, P  < 0.001). Furthermore, postsurgical ctDNA positivity identified patients with stage II or III CRC who derived benefit from ACT (HR 6.59, P  < 0.0001). The results of our study, a large and comprehensive prospective analysis of ctDNA in resectable CRC, support the use of ctDNA testing to identify patients who are at increased risk of recurrence and are likely to benefit from ACT. In the observational GALAXY arm of the CIRCULATE-Japan study, among 1,049 patients with stage II–IV colorectal cancer, those with presence of circulating tumor DNA (ctDNA) after tumor resection have a high recurrence risk and are likely to benefit from adjuvant chemotherapy.