Application of a machine learning method to whole brain white matter injury after radiotherapy for nasopharyngeal carcinoma

Non-complete recovery of temporal lobe white matter diffusion metrics at one year Post-Radiotherapy: Implications for Radiation-Induced necrosis risk

BACKGROUND

Temporal lobe (TL) white matter (WM) injuries are often seen early after radiotherapy (RT) in nasopharyngeal carcinoma patients (NPCs), which fail to fully recover in later stages, exhibiting a "non-complete recovery pattern". Herein, we explored the correlation between non-complete recovery WM injuries and TL necrosis (TLN), identifying dosimetric predictors for TLN-related high-risk WM injuries.

METHODS

We longitudinally examined 161 NPCs and 19 healthy controls employing multi-shell diffusion MRI. Automated fiber-tract quantification quantified diffusion metrics within TL WM tract segments. ANOVA identified non-complete recovery WM tract segments one-year post-RT. Cox regression models discerned TLN risk factors utilizing non-complete recovery diffusion metrics. Normal tissue complication probability (NTCP) models and dose-response analysis further scrutinized RT-related toxicity to high-risk WM tract segments.

RESULTS

Seven TL WM tract segments exhibited a "non-complete recovery pattern". Cox regression analysis identified mean diffusivity of the left uncinate fasciculus segment 1, neurite density index (NDI) of the left cingulum hippocampus segment 1, and NDI of the right inferior longitudinal fasciculus segment 1 as TLN risk predictors (hazard ratios [HRs] with confidence interval [CIs]: 1.45 [1.17-1.81], 1.07 [1.00-1.15], and 1.15 [1.03-1.30], respectively; all P-values < 0.05). In NTCP models, D10cc.L, D20cc.L and D10cc.R demonstrated superior performance, with TD50 of 37.22 Gy, 24.96 Gy and 37.28 Gy, respectively.

CONCLUSIONS

Our findings underscore the significance of the "non-complete recovery pattern" in TL WM tract segment injuries during TLN development. Understanding TLN-related high-risk WM tract segments and their tolerance doses could facilitate early intervention in TLN and improve RT protocols.

Preclinical Development and Validation of Translational Temperature Sensitive Iodized Oil Emulsion Mediated Transcatheter Arterial Chemo-Immuno-Embolization for the Treatment of Hepatocellular Carcinoma

Herein a practical strategy for augmenting immune activation in transcatheter arterial chemoembolization (TACE) of hepatocellular carcinoma (HCC) is presented. Pluronic F127 (PF127) is incorporated with Lipiodol (LPD) to achieve safe and effective delivery of therapeutic agents during transcatheter intra-arterial (IA) local delivery. Enhanced emulsion stability, IA infusion, embolic effect, safety, pharmacokinetics, and tumor response of Doxorubicin loaded PF127-LPD (Dox-PF127-LPD) for TACE in both in vitro and in vivo preclinical VX2 liver cancer rabbit model and N1S1 HCC rat model are demonstrated. Then, transcatheter arterial chemo-immuno-embolization (TACIE) combining TACE and local delivery of immune adjuvant (TLR9 agonist CpG oligodeoxynucleotide) is successfully performed using CpG-loaded Dox-PF127-LPD. Concurrent and safe local delivery of CpG and TACE during TACIE demonstrate leveraged TACE-induced immunogenic tumor microenvironment and augment systemic anti-tumor immunity in syngeneic N1S1 HCC rat model. Finally, the broad utility and enhanced therapeutic efficacy of TACIE are validated in the diethylnitrosamine-induced rat HCC model. TACIE using clinically established protocols and materials shall be a convenient and powerful therapeutic approach that can be translated to patients with HCC. The robust anti-cancer immunity and tumor regression of TACIE, along with its favorable safety profile, indicate its potential as a novel localized combination immunotherapy for HCC treatment.

Application value of 3D pCASL in early assessment of potential radiation encephalopathy in nasopharyngeal carcinoma patients undergoing radiotherapy

OBJECTIVE

The study explores the application value of three-dimensional arterial spin labeling magnetic resonance imaging (3D pCASL) in early assessment of radiation encephalopathy (REP) in patients with nasopharyngeal carcinoma (NPC).

METHODS

A retrospective analysis of 39 cases of NPC was performed. Routine enhanced MRI scan and 3D pCASL imaging were used to examine the apparent diffusion coefficient (ADC) and brain blood flow (CBF) before and after treatment with intensity-modulated radiotherapy (IMRT). Dosimetric analysis of irradiation was performed. Receiver operating characteristic curve (ROC) was used to analyze diagnostic performance of two imaging methods.

RESULTS

There was no statistically significant difference between the two methods for the measurement of temporal white matter ADC, but statistically significant difference was found in CBF. 3D pCASL imaging showed more sensitivity, specificity and higher accuracy than conventional MRI enhanced scan in showing REP. The maximum dose of the temporal lobe was at the enhanced area.

CONCLUSION

The present study demonstrates that 3D pCASL scan at month 3 can reflect blood flow perfusion differences in NPC patients after IMRT and can accurately assess the possibility of REP at early stage. Enhanced areas have a higher probability of REP than the surrounding areas.

ADVANCES IN KNOWLEDGE

There is few magnetic resonance angiography studies used to evaluate arterial circulation on its application on potential REP after radiotherapy for NPC. In our study, we evaluate the application value of 3D pCASL in the early assessment of potential REP in patients with NPC after radiotherapy. The study was to provide an improved understanding of the early specific characteristics on MRI imaging and evolution of potential radiation encephalopathy using 3D pCASL technique, which can quantitatively evaluate the changes of blood flow in tissues at early stage and help to diagnose and treat potential radiation encephalopathy as early as possible.

Radiomics Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Preoperative Differentiation of Combined Hepatocellular-Cholangiocarcinoma from Hepatocellular Carcinoma: A Multi-Center Study

Purpose

To explore whether texture features based on magnetic resonance can distinguish diseases combined hepatocellular-cholangiocarcinoma (cHCC-CC) from hepatocellular carcinoma (HCC) before operation.

Methods

The clinical baseline data and MRI information of 342 patients with pathologically diagnosed cHCC-CC and HCC in two medical centers were collected. The data were divided into the training set and the test set at a ratio of 7:3. MRI images of tumors were segmented with ITK-SNAP software, and python open-source platform was used for texture analysis. Logistic regression as the base model, mutual information (MI) and Least Absolute Shrinkage and Selection Operator (LASSO) regression were used to select the most favorable features. The clinical, radiomics, and clinic-radiomics model were constructed based on logistic regression. The model's effectiveness was comprehensively evaluated by the receiver operating characteristic (ROC) curve, area under the curve (AUC), sensitivity, specificity, and Youden index which is the main, and the model results were exported by SHapley Additive exPlanations (SHAP).

Results

A total of 23 features were included. Among all models, the arterial phase-based clinic-radiomics model showed the best performance in differentiating cHCC-CC from HCC before an operation, with the AUC of the test set being 0.863 (95% CI: 0.782 to 0.923), the specificity and sensitivity being 0.918 (95% CI: 0.819 to 0.973) and 0.738 (95% CI: 0.580 to 0.861), respectively. SHAP value results showed that the RMS was the most important feature affecting the model.

Conclusion

Clinic-radiomics model based on DCE-MRI may be useful to distinguish cHCC-CC from HCC in a preoperative setting, especially in the arterial phase, and RMS has the greatest impact.

Measurements of cerebral microvascular blood flow, oxygenation, and morphology in a mouse model of whole-brain irradiation-induced cognitive impairment by two-photon microscopy and optical coherence tomography: evidence for microvascular injury in the cerebral white matter

Whole-brain irradiation (WBI, also known as whole-brain radiation therapy) is a mainstay treatment modality for patients with multiple brain metastases. It is also used as a prophylactic treatment for microscopic tumors that cannot be detected by magnetic resonance imaging. WBI induces a progressive cognitive decline in ~ 50% of the patients surviving over 6 months, significantly compromising the quality of life. There is increasing preclinical evidence that radiation-induced injury to the cerebral microvasculature and accelerated neurovascular senescence plays a central role in this side effect of WBI. To better understand this side effect, male C57BL/6 mice were first subjected to a clinically relevant protocol of fractionated WBI (5 Gy, two doses per week, for 4 weeks). Nine months post the WBI treatment, we applied two-photon microscopy and Doppler optical coherence tomography to measure capillary red-blood-cell (RBC) flux, capillary morphology, and microvascular oxygen partial pressure (PO2) in the cerebral somatosensory cortex in the awake, head-restrained, WPI-treated mice and their age-matched controls, through a cover-glass-sealed chronic cranial window. Thanks to the extended penetration depth with the fluorophore - Alexa680, measurements of capillary blood flow properties (e.g., RBC flux, speed, and linear density) in the cerebral subcortical white matter were enabled. We found that the WBI-treated mice exhibited a significantly decreased capillary RBC flux in the white matter. WBI also caused a significant reduction in capillary diameter, as well as a large (although insignificant) reduction in segment density at the deeper cortical layers (e.g., 600-700 μm), while the other morphological properties (e.g., segment length and tortuosity) were not obviously affected. In addition, we found that PO2 measured in the arterioles and venules, as well as the calculated oxygen saturation and oxygen extraction fraction, were not obviously affected by WBI. Lastly, WBI was associated with a significant increase in the erythrocyte-associated transients of PO2, while the changes of other cerebral capillary PO2 properties (e.g., capillary mean-PO2, RBC-PO2, and InterRBC-PO2) were not significant. Collectively, our findings support the notion that WBI results in persistent cerebral white matter microvascular impairment, which likely contributes to the WBI-induced brain injury and cognitive decline. Further studies are warranted to assess the WBI-induced changes in brain tissue oxygenation and malfunction of the white matter microvasculature as well.

Structural network alterations in patients with nasopharyngeal carcinoma after radiotherapy: A 1-year longitudinal study

This longitudinal study explored the changed patterns of structural brain network after radiotherapy (RT) in patients with nasopharyngeal carcinoma (NPC). Diffusion tensor imaging (DTI) data were gathered from 35 patients with NPC at four time points: before RT (baseline), 0∼3 (acute), 6 (early delayed), and 12 months (late-delayed) after RT. The graph theory was used to characterize the dynamic topological properties after RT and the significant changes were detected over time at the global, regional and modular levels. Significantly altered regional metrics (nodal efficiency and degree centrality) were distributed in the prefrontal, temporal, parietal, frontal, and subcortical regions. The module, that exhibited a significantly altered within-module connectivity, had a high overlap with the default mode network (DMN). In addition, the global, regional and modular metrics showed a tendency of progressive decrease at the acute and early delayed stages, and a partial/full recovery at the late-delayed stage. This changed pattern illustrated that the radiation-induced brain damage began at the acute reaction stage and were aggravated at the early-delayed stage, and then partially recovered at the late-delayed stage. Furthermore, the spearman’s correlations between the abnormal nodal metrics and temporal dose were calculated and high correlations were found at the temporal (MTG.R and HES.L), subcortical (INS.R), prefrontal (ORBinf.L and ACG.L), and parietal (IPL.R) indicating that these regions were more sensitive to dose and should be mainly considered in radiotherapy treatment plan.

Interaction of chemotherapy and radiotherapy in altering the shape of subcortical structures in patients with nasopharyngeal carcinoma

Neuroimaging studies have found significant structural alterations of the cerebral cortex in patients with nasopharyngeal carcinoma (NPC) following radiotherapy (RT) or concomitant chemoradiotherapy (CCRT), while their effects on the shape of subcortical structures remain largely unknown. In this study, we investigated the subcortical shape alterations between three groups: 56 untreated NPC patients (pre-RT group), 37 RT-treated NPC patients (post-RT group), and 108 CCRT-treated NPC patients (post-CCRT group). Using FSL-FIRST, we found that, compared with the pre-RT group, the post-CCRT group exhibited significant inward atrophy in the bilateral thalamus, bilateral putamen, left pallidum, and left caudate and outward inflation in the left caudate, while the post-RT group only exhibited inward atrophy in the bilateral thalamus. In addition, greater maximum dosage of RT for temporal lobes was associated with more severe inward atrophy of the bilateral thalamus in treated NPC patients. These results indicated that there may be an interaction between RT and CT that can cause subcortical damage.

Machine Learning for Head and Neck Cancer: A Safe Bet?-A Clinically Oriented Systematic Review for the Radiation Oncologist

BACKGROUND AND PURPOSE

Machine learning (ML) is emerging as a feasible approach to optimize patients' care path in Radiation Oncology. Applications include autosegmentation, treatment planning optimization, and prediction of oncological and toxicity outcomes. The purpose of this clinically oriented systematic review is to illustrate the potential and limitations of the most commonly used ML models in solving everyday clinical issues in head and neck cancer (HNC) radiotherapy (RT).

MATERIALS AND METHODS

Electronic databases were screened up to May 2021. Studies dealing with ML and radiomics were considered eligible. The quality of the included studies was rated by an adapted version of the qualitative checklist originally developed by Luo et al. All statistical analyses were performed using R version 3.6.1.

RESULTS

Forty-eight studies (21 on autosegmentation, four on treatment planning, 12 on oncological outcome prediction, 10 on toxicity prediction, and one on determinants of postoperative RT) were included in the analysis. The most common imaging modality was computed tomography (CT) (40%) followed by magnetic resonance (MR) (10%). Quantitative image features were considered in nine studies (19%). No significant differences were identified in global and methodological scores when works were stratified per their task (i.e., autosegmentation).

DISCUSSION AND CONCLUSION

The range of possible applications of ML in the field of HN Radiation Oncology is wide, albeit this area of research is relatively young. Overall, if not safe yet, ML is most probably a bet worth making.

Current Omics Trends in Personalised Head and Neck Cancer Chemoradiotherapy

Chemoradiotherapy remains the most common management of locally advanced head and neck cancer. While both treatment components have greatly developed over the years, the quality of life and long-term survival of patients undergoing treatment for head and neck malignancies are still poor. Research in head and neck oncology is equally focused on the improvement of tumour response to treatment and on the limitation of normal tissue toxicity. In this regard, personalised therapy through a multi-omics approach targeting patient management from diagnosis to treatment shows promising results. The aim of this paper is to discuss the latest results regarding the personalised approach to chemoradiotherapy of head and neck cancer by gathering the findings of the newest omics, involving radiotherapy (dosiomics), chemotherapy (pharmacomics), and medical imaging for treatment monitoring (radiomics). The incorporation of these omics into head and neck cancer management offers multiple viewpoints to treatment that represent the foundation of personalised therapy.

Functional Connectivity Density for Radiation Encephalopathy Prediction in Nasopharyngeal Carcinoma

The diagnostic efficiency of radiation encephalopathy (RE) remains heterogeneous, and prediction of RE is difficult at the pre-symptomatic stage. We aimed to analyze the whole-brain resting-state functional connectivity density (FCD) of individuals with pre-symptomatic RE using multivariate pattern analysis (MVPA) and explore its prediction efficiency. Resting data from NPC patients with nasopharyngeal carcinoma (NPC; consisting of 20 pre-symptomatic RE subjects and 26 non-RE controls) were collected in this study. We used MVPA to classify pre-symptomatic RE subjects from non-RE controls based on FCD maps. Classifier performances were evaluated by accuracy, sensitivity, specificity, and area under the characteristic operator curve. Permutation tests and leave-one-out cross-validation were applied for assessing classifier performance. MVPA was able to differentiate pre-symptomatic RE subjects from non-RE controls using global FCD as a feature, with a total accuracy of 89.13%. The temporal lobe as well as regions involved in the visual processing system, the somatosensory system, and the default mode network (DMN) revealed robust discrimination during classification. Our findings suggest a good classification efficiency of global FCD for the individual prediction of RE at a pre-symptomatic stage. Moreover, the discriminating regions may contribute to the underlying mechanisms of sensory and cognitive disturbances in RE.

Investigation of Radiation-Induced Toxicity in Head and Neck Cancer Patients through Radiomics and Machine Learning: A Systematic Review

Background. Radiation-induced toxicity represents a crucial concern in oncological treatments of patients affected by head and neck neoplasms, due to its impact on survivors' quality of life. Published reports suggested the potential of radiomics combined with machine learning methods in the prediction and assessment of radiation-induced toxicities, supporting a tailored radiation treatment management. In this paper, we present an update of the current knowledge concerning these modern approaches.

MATERIALS AND METHODS

A systematic review according to PICO-PRISMA methodology was conducted in MEDLINE/PubMed and EMBASE databases until June 2019. Studies assessing the use of radiomics combined with machine learning in predicting radiation-induced toxicity in head and neck cancer patients were specifically included. Four authors (two independently and two in concordance) assessed the methodological quality of the included studies using the Radiomic Quality Score (RQS). The overall score for each analyzed study was obtained by the sum of the single RQS items; the average and standard deviation values of the authors' RQS were calculated and reported.

RESULTS

Eight included papers, presenting data on parotid glands, cochlea, masticatory muscles, and white brain matter, were specifically analyzed in this review. Only one study had an average RQS was ≤ 30% (50%), while 3 studies obtained a RQS almost ≤ 25%. Potential variability in the interpretations of specific RQS items could have influenced the inter-rater agreement in specific cases.

CONCLUSIONS

Published radiomic studies provide encouraging but still limited and preliminary data that require further validation to improve the decision-making processes in preventing and managing radiation-induced toxicities.

Standard radiotherapy for patients with nasopharyngeal carcinoma results in progressive tract-specific brain white matter alterations: A one-year follow-up via diffusion tensor imaging

BACKGROUND AND PURPOSE

Radiation therapy (RT)-induced neurocognitive disability may be mediated by brain tissue damage. The aim of the present study was to investigate the effects of standard RT on normal brain tissue via in vivo neuroimaging in patients with nasopharyngeal carcinoma (NPC).

MATERIALS AND METHODS

A total of 146 newly diagnosed NPC patients who were treated with standard RT were longitudinally followed up at multiple time points during the first year post-RT, with 19 comparable healthy controls followed up in parallel serving as normal age-related benchmarks. Magnetic resonance diffusion tensor imaging was used to evaluate longitudinal brain white matter tract changes in NPC patients. The relationships between RT-related white matter changes, hippocampal atrophy, and cognitive impairment were also assessed.

RESULTS

Bilateral cingulate angular bundle (CAB) fibers had progressive diffusion reduction [radial diffusivity (RD) and mean diffusivity] over time (P < 0.05, corrected for multiple comparisons) in NPC patients during the first year after RT. RT-associated progressive RD reduction in the left CAB correlated with longitudinal atrophy of the ipsilateral hippocampus (P = 0.033). Additionally, RT-associated progressive RD reduction in the left CAB correlated with progressive cognitive impairment in NPC patients post-RT (P = 0.048).

CONCLUSION

We present evidence of progressive RT-associated changes in the bilateral CAB in NPC patients, which may underlie RT-related cognitive impairment. These findings illustrate that the use of white matter tract alterations as potential biomarkers to detect RT-related brain injury in NPC patients may be useful for better understanding the pathogenesis of RT-induced cognitive decline.

Irradiation-related longitudinal white matter atrophy underlies cognitive impairment in patients with nasopharyngeal carcinoma

To longitudinally investigate alterations in cerebral white matter volume as a function of irradiation dose and time after standard radiotherapy in nasopharyngeal carcinoma patients and to determine how these alterations are related to radiotherapy-associated neurocognitive dysfunction.A total of 120 nasopharyngeal carcinoma patients were included in the present study. Longitudinal structural magnetic resonance imaging was performed at pre-radiotherapy and 1-3, 6, and 9-12 months post-radiotherapy. Twenty healthy controls were recruited and followed up with in parallel. Structural images were processed via FreeSurfer. The Montreal Cognitive Assessment was performed to evaluate cognitive function of the participants. Linear mixed models and general linear models were used to evaluate different trajectories and the relationship between white matter volume and cognition in patients and controls within approximately 12 months of follow-up.Selective and time-dependent white matter atrophy was observed in the right parahippocampal gyrus, right inferior temporal gyrus, right middle temporal gyrus, right fusiform gyrus, and left insular cortex in post-radiotherapy patients compared to the controls. Moreover, radiotherapy-associated white matter atrophy in the right parahippocampal gyrus exhibited a dose-dependent pattern, whereas radiotherapy-associated white matter atrophy in the right inferior temporal gyrus was correlated with progressive cognitive impairment in patients.Taken together, our findings illustrate that white matter volume alterations can be used as a potential biomarker to detect radiotherapy-related subtle brain injury in nasopharyngeal carcinoma patients, which may help further elucidate the pathogenesis of radiation-induced cognitive decline and facilitate studies on cognition-sparing radiotherapy.

Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma

Background

Early radiation-induced temporal lobe injury (RTLI) diagnosis in nasopharyngeal carcinoma (NPC) is clinically challenging, and prediction models of RTLI are lacking. Hence, we aimed to develop radiomic models for early detection of RTLI.

Methods

We retrospectively included a total of 242 NPC patients who underwent regular follow-up magnetic resonance imaging (MRI) examinations, including contrast-enhanced T1-weighted and T2-weighted imaging. For each MRI sequence, four non-texture and 10,320 texture features were extracted from medial temporal lobe, gray matter, and white matter, respectively. The relief and 0.632 + bootstrap algorithms were applied for initial and subsequent feature selection, respectively. Random forest method was used to construct the prediction model. Three models, 1, 2 and 3, were developed for predicting the results of the last three follow-up MRI scans at different times before RTLI onset, respectively. The area under the curve (AUC) was used to evaluate the performance of models.

Results

Of the 242 patients, 171 (70.7%) were men, and the mean age of all the patients was 48.5 ± 10.4 years. The median follow-up and latency from radiotherapy until RTLI were 46 and 41 months, respectively. In the testing cohort, models 1, 2, and 3, with 20 texture features derived from the medial temporal lobe, yielded mean AUCs of 0.830 (95% CI: 0.823–0.837), 0.773 (95% CI: 0.763–0.782), and 0.716 (95% CI: 0.699–0.733), respectively.

Conclusion

The three developed radiomic models can dynamically predict RTLI in advance, enabling early detection and allowing clinicians to take preventive measures to stop or slow down the deterioration of RTLI.

White Matter Segmentation Algorithm for DTI Images Based on Super-Pixel Full Convolutional Network

Diffusion tensor imaging (DTI) is a new imaging method that can be used to non-invasively measure the diffusion coefficient of water molecules in biological tissue structures in recent years. Since the DTI data is a tensor space, its segmentation is different from ordinary MRI images. Based on the existing deep learning model, an improved image semantic segmentation method based on super-pixels and conditional random field is proposed. Firstly, this paper uses the existing feature extraction model based on deep learning to obtain rough semantic segmentation results, including high-level semantic information of the image but lacking details of the image. In addition, the super-pixel segmentation algorithm is implemented to obtain super-pixels that carries more low-level information. Secondly, due to the lack of image details in rough segmentation results, the segmentation of the edge of the image is inaccurate. In this paper, a boundary optimization algorithm is proposed to optimize the edge segmentation accuracy of the rough results. Finally, the use of super-pixels for local boundary optimization can improve the segmentation accuracy. Experiments results show that this segment is a practical and effective method.