[Radiation therapy and medical imaging]

Tumor-targeted Gd-doped mesoporous Fe3O4 nanoparticles for T1/T2 MR imaging guided synergistic cancer therapy

In this study, a novel intelligent nanoplatform to integrate multiple imaging and therapeutic functions for targeted cancer theranostics. The nanoplatform, DOX@Gd-MFe₃O₄ NPs, was constructed Gd-doped mesoporous Fe₃O₄ nanoparticles following with the doxorubicin (DOX) loading in the mesopores of the NPs. The DOX@Gd-MFe₃O₄ NPs exhibited good properties in colloidal dispersity, photothermal conversion, NIR triggered drug release, and high T₁/T₂ relaxicity rate (r₁=9.64 mM⁻¹s⁻¹, r₂= 177.71 mM⁻¹s⁻¹). Benefiting from the high MR contrast, DOX@Gd-MFe₃O₄ NPs enabled simultaneous T₁/T₂ dual-modal MR imagining on 4T1 bearing mice in vivo and the MR contrast effect was further strengthened by external magnetic field. In addition, the DOX@Gd-MFe₃O₄ NPs revealed the strongest inhibition to the growth of 4T1 in vitro and in vivo under NIR irradiation and guidance of external magnetic field. Moreover, biosafety was also validated by in vitro and in vivo tests. Thus, the prepared DOX@Gd-MFe₃O₄ NPs would provide a promising intelligent nanoplatform for dual-modal MR imagining guided synergistic therapy in cancer theranostics.

A multiobjective Bayesian networks approach for joint prediction of tumor local control and radiation pneumonitis in nonsmall-cell lung cancer (NSCLC) for response-adapted radiotherapy

PURPOSE

Individualization of therapeutic outcomes in NSCLC radiotherapy is likely to be compromised by the lack of proper balance of biophysical factors affecting both tumor local control (LC) and side effects such as radiation pneumonitis (RP), which are likely to be intertwined. Here, we compare the performance of separate and joint outcomes predictions for response-adapted personalized treatment planning.

METHODS

A total of 118 NSCLC patients treated on prospective protocols with 32 cases of local progression and 20 cases of RP grade 2 or higher (RP2) were studied. Sixty-eight patients with 297 features before and during radiotherapy were used for discovery and 50 patients were reserved for independent testing. A multiobjective Bayesian network (MO-BN) approach was developed to identify important features for joint LC/RP2 prediction using extended Markov blankets as inputs to develop a BN predictive structure. Cross-validation (CV) was used to guide the MO-BN structure learning. Area under the free-response receiver operating characteristic (AU-FROC) curve was used to evaluate joint prediction performance.

RESULTS

Important features including single nucleotide polymorphisms (SNPs), micro RNAs, pretreatment cytokines, pretreatment PET radiomics together with lung and tumor gEUDs were selected and their biophysical inter-relationships with radiation outcomes (LC and RP2) were identified in a pretreatment MO-BN. The joint LC/RP2 prediction yielded an AU-FROC of 0.80 (95% CI: 0.70-0.86) upon internal CV. This improved to 0.85 (0.75-0.91) with additional two SNPs, changes in one cytokine and two radiomics PET image features through the course of radiotherapy in a during-treatment MO-BN. This MO-BN model outperformed combined single-objective Bayesian networks (SO-BNs) during-treatment [0.78 (0.67-0.84)]. AU-FROC values in the evaluation of the MO-BN and individual SO-BNs on the testing dataset were 0.77 and 0.68 for pretreatment, and 0.79 and 0.71 for during-treatment, respectively.

CONCLUSIONS

MO-BNs can reveal possible biophysical cross-talks between competing radiotherapy clinical endpoints. The prediction is improved by providing additional during-treatment information. The developed MO-BNs can be an important component of decision support systems for personalized response-adapted radiotherapy.

[Past, present and near future of techniques in radiation oncology]

Since the discovery of X-rays, the goal of radiotherapy has been to deliver an optimal dose in the target volume and the lowest possible dose in the normal tissues. The history of radiotherapy can be divided in three periods. The Kilovoltage era (1900-1939) where only superficial and radiosensitive tumours could be controlled, the Megavoltage era (1950-1995) where Telecobalt and linear accelerators could deliver high doses in all parts of the body. Radiotherapy has since been playing an important curative and conservative role for most cancers. The Computer-Assisted Radiotherapy era (1995-2010) now provides the capacity to optimise the dose distribution in three dimensions. Dose is better conformed to the target volume and organ at risk are better preserved. intensity modulated radio-therapy (IMRT) allows to "shape" concave isodoses and to spare the parotids when irradiating oropharyngeal tumours. Moving targets (lung, liver etc.) are efficiently irradiated using "on-line tracking" and "image-guided radiotherapy". Stereotactic irradiation, first initiated for brain lesions, is now performed for extra-cranial tumours and due to its millimetric precision opens the way back to hypo-fractionated treatments. The next period, already ongoing, is Hadrontherapy with protons and soon helium or carbon ions techniques. In a multidisciplinary strategy, progress in radiotherapy is based on a global approach of the patient and tailored/personalized well targeted treatment of the tumour.