Figure 2 Multiscale modelling in oncology

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Optimization of personalized therapies for anticancer treatment Alexei Vazquez The Cancer Institute of New Jersey.

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Nat. Rev. Clin. Oncol. doi: /nrclinonc

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Figure 1 CTLA-4 and PD-1–PD-L1 immune checkpoints

Figure 2 Underreporting by physicians of specific treatment-associated symptoms by physicians in the TORCH trial Figure 2 | Underreporting by physicians.

Nat. Rev. Clin. Oncol. doi: /nrclinonc

Figure 1 Concept of the therapeutic index

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Figure 4 Example of PK/PD simulation to optimize a vinorelbine treatment regimen Figure 4 | Example of PK/PD simulation to optimize a vinorelbine treatment.

Figure 5 Schematic illustration of different clinical trial designs

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Figure 1 Generations of cancer vaccine antigens

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Nat. Rev. Clin. Oncol. doi: /nrclinonc

Nat. Rev. Clin. Oncol. doi: /nrclinonc

Nat. Rev. Clin. Oncol. doi: /nrclinonc

Figure 1 Underreporting of treatment-related toxicities by physicians, relative to patients with either advanced-stage lung cancer, or early-stage breast.

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Figure 4 Possible combination therapies CDK4/6 inhibitors

Figure 2 Therapeutic targeting of the PI3K/AKT/mTOR pathway

Figure 1 Proposed treatment algorithm for advanced gastroesophageal cancer based on publish recommendations Figure 1 | Proposed treatment algorithm for.

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Figure 1 Biosimilar development process

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Figure 4 Example of a patients with CUP

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Figure 2 Examples of histopathological validation

Figure 1 Cost of one month of treatment with

Figure 2 The association between CD8+ T‑cell density of the tumour

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Figure 4 Macrophage-targeting antitumour treatment approaches

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Nat. Rev. Clin. Oncol. doi: /nrclinonc

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Figure 3 Drug cycling with collateral sensitivity

Figure 2 Differences between MC and AC

Figure 3 Possible modalities for reconciliation of patient's and physician's report of symptomatic treatment-associated toxicities Figure 3 | Possible.

Figure 1 A schematic representation of the role

Nat. Rev. Clin. Oncol. doi: /nrclinonc

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Figure 1 NIR fluorescence is more suitable for

Figure 1 The dynamic nature of resistance mechanisms can be

Figure 1 Morphological and physiological changes

Figure 1 Critical signalling pathways involved in PDAC pathogenesis

Figure 3 Summary of overall survival by Kaplan–Meier

Figure 3 Clinical trial design in charged-particle therapy (CPT)

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Figure 3 The yin and yang of tumour-associated

Figure 2 Median monthly launch price of a new anticancer drug,

Figure 2 The patterns of epithelial-to-mesenchymal

Figure 1 Translocations involved in multiple myeloma

Figure 1 Simplified representation of the physiological

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Figure 2 Key features of gastric cancer subtypes according to The Cancer Genome Atlas (TCGA) Figure 2 | Key features of gastric cancer subtypes according.

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Nat. Rev. Clin. Oncol. doi: /nrclinonc

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Figure 2 Frequency and overlap of alterations

Figure 3 Tumours secrete factors that cause systemic immunosuppression

Figure 2 Effect of chromosomal instability tolerance

Figure 5 Schematic overview of a clinical decision-support

Nat. Rev. Clin. Oncol. doi: /nrclinonc

Figure 7 Overview of the methodological processes for

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Figure 4 Radiogenomics analysis can reveal relationships

Figure 2 Radiomics in cardiology

Figure 1 Overview of the imaging biomarker roadmap

Figure 3 Determination of the primary site

Figure 4 Molecular signalling and immunological

Figure 1 Gene-expression quantification methods

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Figure 2 Multiscale modelling in oncology Figure 2 | Multiscale modelling in oncology. Multiscale modelling can be used to simulate physical or physiological processes related to cancer development. Simulations can be performed at the level of molecules and their interactions, at the cellular level, the tissue level, the organ level, and/or the ultimately at the whole-body level, over different time frames. Then, the effects of several inputs, such as molecular or genetic profiling, or grading and staging of the tumours, can be modelled. Characteristics of the anticancer agents alone or combination can also be computed via a PK/PD model. The model can eventually predict different kinds of outcome at each level (survival, clinical and biological toxicities, decreases in tumour volume, target inhibition, and molecular pathway inhibition) and at different time points. Barbolosi, D. et al. (2015) Computational oncology — mathematical modelling of drug regimens for precision medicine Nat. Rev. Clin. Oncol. doi:10.1038/nrclinonc.2015.204