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.

Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is ‘clinical acceptability’? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of ‘clinical acceptability’ and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools.

Evidence suggests hyperglycemia is associated with worse outcomes in glioblastoma (GB). This study aims to confirm the association between glycemia during radiotherapy (RT) and temozolomide (TMZ) treatment and overall survival (OS) in patients with newly diagnosed GB. This retrospective study included GB patients treated with RT and TMZ from 2004 to 2011, randomly divided into independent derivation and validation datasets. Time-weighted mean (TWM) glucose and dexamethasone dose were collected from start of RT to 4 weeks after RT. Univariate (UVA) and multivariable (MVA) analyses investigated the association of TWM glucose and other prognostic factors with overall survival (OS). In total, 393 patients with median follow-up of 14 months were analyzed. In the derivation set (n = 196) the median OS was 15 months and median TWM glucose was 6.3 mmol/L. For patients with a TWM glucose ≤6.3 and >6.3 mmol/L, median OS was 16 and 13 months, respectively (p = 0.03). On UVA, TWM glucose, TWM dexamethasone, age, extent of surgery, and performance status were associated with OS. On MVA, TWM glucose remained an independent predictor of OS (p = 0.03) along with TWM dexamethasone, age, and surgery. The validation set (n = 197), with similar baseline characteristics, confirmed that TWM glucose ≤6.3 mmol/L was independently associated with longer OS (p = 0.005). This study demonstrates and validates that glycemia is an independent predictor for survival in GB patients treated with RT and TMZ.

Treatment for glioblastoma (GBM) includes surgical resection and adjuvant radiotherapy (RT) and chemotherapy. The optimal time interval between surgery and RT remains unclear. The National Cancer Database (NCDB) was queried for patients with GBM. Overall survival (OS) was estimated using Kaplan-Meier and log-rank tests. Univariate (UVA) and multivariable Cox regression (MVA) modeling was used to determine predictors of OS. A total of 45,942 patients were included. On MVA: younger age, female gender, black ethnicity, higher KPS, obtaining a gross total resection (GTR), MGMT promoter-methylated gene status, unifocal disease, higher RT dose, and RT delay of 4–8 weeks had improved OS. Patients who underwent a subtotal resection (STR) had worsened survival with RT delay ≤4 weeks and patients with GTR had worsened survival when RT was delayed >8 weeks. This analysis suggests that an interval of 4–8 weeks between resection and RT results in better survival. Delays >8 weeks in patients with a GTR and delays <4 weeks in patients with a STR/biopsy resulted in worse survival. This impact of time delay from surgery to RT, in conjunction with extent of resection, should be considered in the clinical management of patients and future designs of clinical trials.

Background: Leptomeningeal disease (LMD), also known as neoplastic meningitis, leptomeningeal carcinomatosis, or carcinomatous meningitis, is a rare cancer complication occurring in ~5% of cases and ultimately leads to significant morbidity and mortality. In the modern era, incidence of this condition continues to rise with longer survival of patients with advanced and even metastatic disease due to continued improvements in systemic therapies that are providing prolonged control of distant disease, but with limited effect in the central nervous system (CNS). Typical treatment strategies include optimal systemic therapy for the primary disease, as well as neuroaxis directed therapies, which may include intrathecal chemotherapy (ITC) or radiotherapy (RT). Methods: A systematic review of radiotherapy for LMD was performed. Medline, EMBASE, and Cochrane databases were searched from 1946 to 2018 for clinical trials, retrospective/prospective reviews, and case series with ≥2 human subjects that used radiation therapy techniques in the treatment of LMD. The outcome measures of interest included: characteristics of trial participants, inclusion/exclusion criteria, study type, number of participants, primary cancer histology, type of intervention for LMD, survival results if reported, length of follow up, and study conclusion. Results: Of 547 unique citations, 62 studies met the pre-specified eligibility criteria. These studies included 36 retrospective cohorts, 11 prospective series, 12 case series, and a single citation of guidelines, NCDB analysis, and a randomized control trial. Owing to study heterogeneity, meta-analyses of the endpoint data could not be performed. Conclusions: LMD is a devastating complication of cancer with reported survivals ranging from 2 to 4 months. Based on this systematic review, the recommendation for the treatment of LMD is for multimodality discussion of cases and treatment, including the use of radiotherapy, for LMD. However, with continued advances in systemic therapy as well as imaging advances, the landscape of LMD is evolving rapidly and the role of RT will likely also continue to evolve and advance. There is limited high-quality evidence to guide the optimal use of RT for the treatment of LMD, and there is a great need for prospective, histology specific investigation of the role of radiotherapy for LMD in the era of modern systemic therapies.