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Neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision is the standard treatment for locally advanced rectal cancer (LARC). A noninvasive preoperative prediction method should greatly assist in the evaluation of response to nCRT and for the development of a personalized strategy for patients with LARC. Assessment of nCRT relies on imaging and radiomics can extract valuable quantitative data from medical images. In this review, we examined the status of radiomic application for assessing response to nCRT in patients with LARC and indicated a potential direction for future research.

PURPOSE: Recent initial findings suggest that radiation therapy improves blood perfusion and cellular chemotherapy uptake in mice with leukemia. However, the ability of radiation therapy to influence drug accumulation in the extracellular bone marrow tissue is unknown, due in part to a lack of methodology. This study developed longitudinal quantitative multiphoton microscopy (L-QMPM) to characterize the bone marrow vasculature (BMV) and drug accumulation in the extracellular bone marrow tissue before and after radiation therapy in mice bearing leukemia. METHODS AND MATERIALS: We developed a longitudinal window implant for L-QMPM imaging of the calvarium BMV before, 2 days after, and 5 days after total body irradiation (TBI). Live time-lapsed images of a fluorescent drug surrogate were used to obtain measurements, including tissue wash-in slope (WIS(tissue)) to measure extracellular drug accumulation. We performed L-QMPM imaging on healthy C57BL/6 (WT) mice, as well as mice bearing acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). RESULTS: Implants had no effects on calvarium dose, and parameters for wild-type untreated mice were stable during imaging. We observed decreased vessel diameter, vessel blood flow, and WIS(tissue) with the onset of AML and ALL. Two to 10 Gy TBI increased WIS(tissue) and vessel diameter 2 days after radiation therapy in all 3 groups of mice and increased single-vessel blood flow in mice bearing ALL and AML. Increased WIS(tissue) was observed 5 days after 10 Gy TBI or 4 Gy split-dose TBI (2 treatments of 2 Gy spaced 3 days apart). CONCLUSIONS: L-QMPM provides stable functional assessments of the BMV. Nonmyeloablative and myeloablative TBI increases extracellular drug accumulation in the leukemic bone marrow 2 to 5 days posttreatment, likely through improved blood perfusion and drug exchange from the BMV to the extravascular tissue. Our data show that neo-adjuvant TBI at doses from 2 Gy to 10 Gy conditions the BMV to improve drug transport to the bone marrow.

Chemotherapy may impair cognition and contribute to accelerated aging. The purpose of this study was to assess the effects of chemotherapy on the connectivity of the default mode network (DMN) in older women with breast cancer. This prospective longitudinal study enrolled women aged ≥ 60 years with stage I–III breast cancer (CTx group) and matched healthy controls (HC group). Study assessments, consisting of resting-state functional MRI (rs-fMRI) and the Picture Sequence Memory (psm) test for episodic memory from the NIH Toolbox for Cognition, were obtained at baseline and within one month after the completion of chemotherapy for the CTx group and at matched intervals for the HC group. Two-sample t-test and FDR multiple comparison were used for statistical inference. Our analysis of the CTx group (N = 19; 60–82 years of age, mean = 66.6, SD = 5.24) compared to the HC group (N = 14; 60–78 years of age, mean = 68.1, SD = 5.69) revealed weaker DMN subnetwork connectivity in the anterior brain but stronger connectivity in the posterior brain at baseline. After chemotherapy, this pattern was reversed, with stronger anterior connectivity and weaker posterior connectivity. In addition, the meta-level functional network connectivity (FNC) among the DMN subnetworks after chemotherapy was consistently weaker than the baseline FNC as seen in the couplings between anterior cingulate cortex (ACC) and retrosplenial (rSplenia) region, with ΔFNC(‘ACC’,’rSplenia’)=-0.14, t value=-2.44, 95 %CI=[-0.27,-0.10], pFDR

Assessment of adrenal incidentaloma relies on imaging analysis and evaluation of adrenal function. Radiomics as a tool for quantitative image analysis is useful for evaluation of adrenal incidentaloma. In this review, we examined radiomic literature on adrenal incidentaloma including both adrenal functional assessment and structural differentiation of benign versus malignant adrenal tumors. In this review, we summarized the status of radiomic application on adrenal incidentaloma and suggested potential direction for future research.

Background and PurposeComputerized tomography (CT) scans are commonly performed to assist in diagnosis and treatment of locally advanced rectal cancer (LARC). This study assessed the usefulness of pretreatment CT-based radiomics for predicting pathological complete response (pCR) of LARC to neoadjuvant chemoradiotherapy (nCRT).Materials and MethodsPatients with LARC who underwent nCRT followed by total mesorectal excision surgery from July 2010 to December 2018 were enrolled in this retrospective study. A total of 340 radiomic features were extracted from pretreatment contrast-enhanced CT images. The most relevant features to pCR were selected using the least absolute shrinkage and selection operator (LASSO) method and a radiomic signature was generated. Predictive models were built with radiomic features and clinico-pathological variables. Model performance was assessed with decision curve analysis and was validated in an independent cohort.ResultsThe pCR was achieved in 44 of the 216 consecutive patients (20.4%) in this study. The model with the best performance used both radiomics and clinical variables including radiomic signatures, distance to anal verge, lymphocyte-to-monocyte ratio, and carcinoembryonic antigen. This combined model discriminated between patients with and without pCR with an area under the curve of 0.926 and 0.872 in the training and the validation cohorts, respectively. The combined model also showed better performance than models built with radiomic or clinical variables alone.ConclusionOur combined predictive model was robust in differentiating patients with and without response to nCRT.

Early detection of non-response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced colorectal cancer (LARC) remains challenging. We aimed to assess whether pretreatment radiotherapy planning computed tomography (CT) radiomics could distinguish the patients with no response or no downstaging after nCRT from those with response and downstaging after nCRT.Patients with LARC who were treated with nCRT were retrospectively enrolled between March 2009 and March 2019. Traditional radiological characteristics were analyzed by visual inspection and radiomic features were analyzed through computational methods from the pretreatment radiotherapy planning CT images. Differentiation models were constructed using radiomic methods and clinicopathological characteristics for predicting non-response to nCRT. Model performance was assessed for classification efficiency, calibration, discrimination, and clinical application.This study enrolled a total of 215 patients, including 151 patients in the training cohort (50 non-responders and 101 responders) and 64 patients in the validation cohort (21 non-responders and 43 responders). For predicting non-response, the model constructed with an ensemble machine learning method had higher performance with area under the curve (AUC) values of 0.92 and 0.89 as compared to the model constructed with the logistic regression method (AUC: 0.72 and 0.71 for the training and validation cohorts, respectively). Both decision curve and calibration curve analyses confirmed that the ensemble machine learning model had higher prediction performance.Pretreatment CT radiomics achieved satisfying performance in predicting non-response to nCRT and could be helpful to assist in treatment planning for patients with LARC.

Lung cancer metastases comprise most of all brain metastases in adults and most brain metastases are diagnosed by magnetic resonance (MR) scans. The purpose of this study was to conduct an MR imaging-based radiomic analysis of brain metastatic lesions from patients with primary lung cancer to classify mutational status of the metastatic disease. We retrospectively identified lung cancer patients with brain metastases treated at our institution between 2009 and 2017 who underwent genotype testing of their primary lung cancer. Brain MR Images were used for segmentation of enhancing tumors and peritumoral edema, and for radiomic feature extraction. The most relevant radiomic features were identified and used with clinical data to train random forest classifiers to classify the mutation status. Of 110 patients in the study cohort (mean age 57.51 ± 12.32 years; M: F = 37:73), 75 had an EGFR mutation, 21 had an ALK translocation, and 15 had a KRAS mutation. One patient had both ALK translocation and EGFR mutation. Majority of radiomic features most relevant for mutation classification were textural. Model building using both radiomic features and clinical data yielded more accurate classifications than using either alone. For classification of EGFR, ALK, and KRAS mutation status, the model built with both radiomic features and clinical data resulted in area-under-the-curve (AUC) values based on cross-validation of 0.912, 0.915, and 0.985, respectively. Our study demonstrated that MR imaging-based radiomic analysis of brain metastases in patients with primary lung cancer may be used to classify mutation status. This approach may be useful for devising treatment strategies and informing prognosis.

Objectives Oncologists can be one of the major barriers to older adult's participation in research. Multiple studies have described academic clinicians' concerns for not enrolling older adults onto trials. Although the majority of older adults receive their cancer care in the community, few studies have examined the unique challenges that community oncologists face and how they differ from oncologist-related barriers in academia. Methods Semi-structured interviews were conducted by telephone or face-to-face with 44 medical oncologists (24 academic-based and 20 community-based) at City of Hope from March to June 2018. Interviews explored oncologists' perceptions of barriers to clinical trial enrollment of older adults with cancer. Data were analyzed using qualitative content analysis. Results Of the 44 participants, 36% were women and 68% were in practice for >10 years. Among the entire sample, stringent eligibility criteria (n = 20) and oncologist concerns for treatment toxicities (n = 15) were the most commonly cited barriers. Compared to academic oncologists, community oncologists more often cited patient attitudes, beliefs, and understanding (n = 9 vs. n = 2) and caregiver burden (n = 6 vs. n = 0). In contrast, compared to community oncologists, academic oncologists more often cited oncologist bias (n = 10 vs. n = 3) and insufficient time/support (n = 4 vs. n = 1). Conclusions Differences in perceptions among academic and community oncologists about trials suggest that barriers are multifaceted, complex, and vary by practice setting. Interventions to increase trial accrual among older adults with cancer may benefit from being tailored to address the unique barriers of different practice settings.

PurposeEarly recurrence of glioblastoma after standard treatment makes patient care challenging. This study aimed to assess preoperative magnetic resonance imaging (MRI) radiomics for predicting early recurrence of glioblastoma.Patients and MethodsA total of 122 patients (training cohort: n = 86; validation cohort: n = 36) with pathologically confirmed glioblastoma were included in this retrospective study. Preoperative brain MRI images were analyzed for both radiomics and the Visually Accessible Rembrandt Image (VASARI) features of glioblastoma. Models incorporating MRI radiomics, the VASARI parameters, and clinical variables were developed and presented in a nomogram. Performance was assessed based on calibration, discrimination, and clinical usefulness.ResultsThe nomogram consisting of the radiomic signatures, the VASARI parameters, and blood urea nitrogen (BUN) values showed good discrimination between the patients with early recurrence and those with later recurrence, with an area under the curve of 0.85 (95% CI, 0.77-0.94) in the training cohort and 0.84 [95% CI, 0.71-0.97] in the validation cohort. Decision curve analysis demonstrated favorable clinical application of the nomogram.ConclusionThis study showed the potential usefulness of preoperative brain MRI radiomics in predicting the early recurrence of glioblastoma, which should be helpful in personalized management of glioblastoma.

BackgroundIt is prudent to identify the risk for progressive disease (PD) in patients with non-small-cell lung cancer (NSCLC) who undergo platinum-based chemotherapy. The present study aimed to develop a CT imaging-based sarcopenic nomogram for predicting the risk of PD prior to chemotherapy treatment.MethodsWe retrospectively enrolled patients with NSCLC who underwent platinum-based chemotherapy. Imaging-based body composition parameters such as skeletal muscle index (SMI) for assessment of sarcopenia were obtained from pre-chemotherapy chest CT images at the level of the eleventh thoracic vertebral body (T11). Sarcopenic nomogram was constructed using multivariate logistic regression and performance of the nomogram was evaluated by discrimination, calibration curve, and decision curve.ResultsSixty (14.7%) of the 408 patients in the study cohort developed PD during chemotherapy. The prediction nomogram for developing PD achieved a moderate efficiency with an area under the curve (AUC) of 0.75 (95% CI: 0.69-0.80) for the training cohort, and 0.76 (95%CI: 0.68-0.84) for the validation cohort, as well as a good performance of consistence (bootstrap for training cohort: 0.75 ± 0.02; validation cohort: 0.74 ± 0.06). Favorable clinical application was observed in the decision curve analysis.ConclusionOur CT-based sarcopenic nomogram showed the potential for an individualized prediction of progression for patients with NSCLC receiving platinum-based chemotherapy.

Background: Brain metastases are associated with poor survival. Molecular genetic testing informs on targeted therapy and survival. The purpose of this study was to perform a MR imaging-based radiomic analysis of brain metastases from non-small cell lung cancer (NSCLC) to identify radiomic features that were important for predicting survival duration.Methods: We retrospectively identified our study cohort via an institutional database search for patients with brain metastases from EGFR, ALK, and/or KRAS mutation-positive NSCLC. We segmented the brain metastatic tumors on the brain MR images, extracted radiomic features, constructed radiomic scores from significant radiomic features based on multivariate Cox regression analysis (p < 0.05), and built predictive models for survival duration.Result: Of the 110 patients in the cohort (mean age 57.51 ± 12.32 years; range: 22–85 years, M:F = 37:73), 75, 26, and 15 had NSCLC with EGFR, ALK, and KRAS mutations, respectively. Predictive modeling of survival duration using both clinical and radiomic features yielded areas under the receiver operative characteristic curve of 0.977, 0.905, and 0.947 for the EGFR, ALK, and KRAS mutation-positive groups, respectively. Radiomic scores enabled the separation of each mutation-positive group into two subgroups with significantly different survival durations, i.e., shorter vs. longer duration when comparing to the median survival duration of the group.Conclusion: Our data supports the use of radiomic scores, based on MR imaging of brain metastases from NSCLC, as non-invasive biomarkers for survival duration. Future research with a larger sample size and external cohorts is needed to validate our results.

Purpose Total marrow irradiation (TMI) has significantly advanced radiation conditioning for hematopoietic cell transplantation in hematologic malignancies by reducing conditioning-induced toxicities and improving survival outcomes in relapsed/refractory patients. However, the relapse rate remains high, and the lack of a preclinical TMI model has hindered scientific advancements. To accelerate TMI translation to the clinic, we developed a TMI delivery system in preclinical models. Methods and Materials A Precision X-RAD SmART irradiator was used for TMI model development. Images acquired with whole-body contrast-enhanced computed tomography (CT) were used to reconstruct and delineate targets and vital organs for each mouse. Multiple beam and CT-guided Monte Carlo–based plans were performed to optimize doses to the targets and to vary doses to the vital organs. Long-term engraftment and reconstitution potential were evaluated by a congenic bone marrow transplantation (BMT) model and serial secondary BMT, respectively. Donor cell engraftment was measured using noninvasive bioluminescence imaging and flow cytometry. Results Multimodal imaging enabled identification of targets (skeleton and spleen) and vital organs (eg, lungs, gut, liver). In contrast to total body irradiation (TBI), TMI treatment allowed variation of radiation dose exposure to organs relative to the target dose. Dose reduction mirrored that in clinical TMI studies. Similar to TBI, mice treated with different TMI regimens showed full long-term donor engraftment in primary BMT and second serial BMT. The TBI-treated mice showed acute gut damage, which was minimized in mice treated with TMI. Conclusions A novel multimodal image guided preclinical TMI model is reported here. TMI conditioning maintained long-term engraftment with reconstitution potential and reduced organ damage. Therefore, this TMI model provides a unique opportunity to study the therapeutic benefit of reduced organ damage and BM dose escalation to optimize treatment regimens in BMT and hematologic malignancies.

Objective Early detection of platinum resistance for ovarian cancer treatment remains challenging. This study aims to develop a machine learning model incorporating genomic data such as Single-Nucleotide Polymorphisms (SNPs) of Human Sulfatase 1 (SULF1) with a CT radiomic model based on pre-treatment CT images, to predict platinum resistance for ovarian cancer (OC) treatment. Methods A cohort of 102 patients with pathologically confirmed OC was retrospectively enrolled into this study from January 2006 to February 2018. All patients had platinum-based chemotherapy after maximal cyto-reductive surgery. This cohort was separated into two groups according to treatment response, i.e., the group with platinum-resistant disease (PR group) and the group with platinum-sensitive disease (PS group). We genotyped 12 SNPs of SULF1 for all OC patients using Mass Array Method. Radiomic features, SNP data and clinicopathological data of the 102 patients were used to build the differentiation models. The study participants were divided into two cohorts: the training cohort (n = 71) and the validation cohort (n = 31). Feature selection and predictive modeling were performed using least absolute shrinkage and selection operator (LASSO), Random Forest Classifier and Support Vector Machine methods. Model performance for predicting platinum resistance was assessed with respect to its calibration, discrimination, and clinical application. Results For prediction of platinum resistance, the approach combining the radiomics, clinicopathological data and SNP data demonstrated higher classification efficiency, with an AUC value of 0.993 (95 % CI: 0.83 to 0.98) in the training cohort and 0.967 (95 % CI: 0.83 to 0.98) in validation cohort, than the performance with only the SNPs of SULF1 model (AUC: training, 0.843 [95 %CI: 0.738-0.948]; validation, 0.815 [0.601-1.000]), or with only the radiomic model (AUC: training, 0.874 [95 %CI: 0.789-0.960]; validation, 0.832 [95 %CI: 0.687-0.976]). This integrated approach also showed good calibration and favorable clinical utility. Conclusions A predictive model combining pretreatment CT radiomics with genomic data such as SNPs of SULF1 could potentially help to predict platinum resistance in ovarian cancer treatment.

BackgroundClear cell renal cell carcinoma (ccRCC) is the most common renal cancer and it has the worst prognosis among all renal cancers. However, traditional radiological characteristics on computed tomography (CT) scans of ccRCC have been insufficient to predict the pathological grade of ccRCC before surgery.MethodsPatients with ccRCC were retrospectively enrolled into this study and were separated into two groups according to the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading system, i.e., low-grade (Grade I and II) group and high-grade (Grade III and IV) group. Traditional CT radiological characteristics such as tumor size, pre- and post-enhancing CT densities were assessed. In addition, radiomic texture analysis based on the CT imaging of the ccRCC were also performed. A CT-based machine learning method combining the traditional radiological characteristics and radiomic features was used in the predictive modeling for differentiating the low-grade from the high-grade ccRCC. Model performance was evaluated with the receiver operating characteristic curve (ROC) analysis.ResultsA total of 264 patients with pathologically confirmed ccRCC were included in this study. In this cohort, 206 patients had the low-grade tumors and 58 had the high-grade tumors. The model built with traditional radiological characteristics achieved an area under the curve (AUC) of 0.9175 (95% CI: 0.8765–0.9585) and 0.8088 (95% CI: 0.7064–0.9113) in differentiating the low-grade from the high-grade ccRCC for the training cohort and the validation cohort respectively. The model built with the radiomic textural features yielded an AUC value of 0.8170 (95% CI: 0.7353–0.8987) and 0.8017 (95% CI: 0.6878–0.9157) for the training cohort and the validation cohort, respectively. The combined model integrating both the traditional radiological characteristics and the radiomic textural features achieved the highest efficacy, with an AUC of 0.9235 (95% CI: 0.8646–0.9824) and an AUC of 0.9099 (95% CI: 0.8324–0.9873) for the training cohort and validation cohort, respectively.ConclusionWe developed a machine learning radiomic model achieving a satisfying performance in differentiating the low-grade from the high-grade ccRCC. Our study presented a potentially useful non-invasive imaging-focused method to predict the pathological grade of renal cancers prior to surgery.

Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model. The purpose of this study was to use CT radiomics to differentiate SCLC from NSCLC adenocarcinoma. Patients with primary lung cancer, either SCLC or NSCLC adenocarcinoma, were retrospectively identified. The post-diagnosis pre-treatment lung CT images were used to segment the lung cancers. Radiomic features were extracted from histogram-based statistics, textural analysis of tumor images and their wavelet transforms. A minimal-redundancy-maximal-relevance method was used for feature selection. The predictive model was constructed with a multilayer artificial neural network. The performance of the SCLC/NSCLC adenocarcinoma classifier was evaluated by the area under the receiver operating characteristic curve (AUC). Our study cohort consisted of 69 primary lung cancer patients with SCLC (n = 35; age mean ± SD = 66.91± 9.75 years), and NSCLC adenocarcinoma (n = 34; age mean ± SD = 58.55 ± 11.94 years). The SCLC group had more male patients and smokers than the NSCLC group (P < 0.05). Our SCLC/NSCLC classifier achieved an overall performance of AUC of 0.93 (95% confidence interval = [0.85, 0.97]), sensitivity = 0.85, and specificity = 0.85). Adding clinical data such as smoking history could improve the performance slightly. The top ranking radiomic features were mostly textural features. Our results showed that CT radiomics could quantitatively represent tumor heterogeneity and therefore could be used to differentiate primary lung cancer subtypes with satisfying results. CT image processing with the wavelet transformation technique enhanced the radiomic features for SCLC/NSCLC classification. Our pilot study should motivate further investigation of radiomics as a non-invasive approach for early diagnosis and treatment of lung cancer.

Purpose The purpose of this study was to evaluate longitudinal changes in brain gray matter density (GMD) before and after adjuvant chemotherapy in older women with breast cancer. Methods We recruited 16 women aged ≥ 60 years with stage I–III breast cancers receiving adjuvant chemotherapy (CT) and 15 age- and sex-matched healthy controls (HC). The CT group underwent brain MRI and the NIH Toolbox for Cognition testing prior to adjuvant chemotherapy (time point 1, TP1) and within 1 month after chemotherapy (time point 2, TP2). The HC group underwent the same assessments at matched intervals. GMD was evaluated with the voxel-based morphometry. Results The mean age was 67 years in the CT group and 68.5 years in the HC group. There was significant GMD reduction within the chemotherapy group from TP1 to TP2. Compared to the HC group, the CT group displayed statistically significantly greater GMD reductions from TP1 to TP2 in the brain regions involving the left anterior cingulate gyrus, right insula, and left middle temporal gyrus (pFWE(family-wise error)-corrected pFWE-corrected pFWE-corrected Conclusions Our findings indicate that GMD reductions were associated with adjuvant chemotherapy in older women with breast cancer. Future studies are needed to understand the clinical significance of the neuroimaging findings. This study is registered on ClinicalTrials.gov (NCT01992432).

Background: Conventional methods for predicting treatment response to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) are limited. Methods: This study retrospectively recruited 134 LARC patients who underwent standard nCRT followed by total mesorectal excision surgery in our institution. Based on pre-operative axial T2-weighted images, machine learning radiomics was performed. A receiver operating characteristic (ROC) curve was performed to test the efficiencies of the predictive model. Results: Among the 134 patients, 32 (23.9%) achieved pathological complete response (pCR), 69 (51.5%) achieved a good response, and 91 (67.9%) achieved down-staging. For prediction of pCR, good-response, and down-staging, the predictive model demonstrated high classification efficiencies, with an AUC value of 0.91 (95% CI: 0.83–0.98), 0.90 (95% CI: 0.83–0.97), and 0.93 (95% CI: 0.87–0.98), respectively. Conclusion: Our machine learning radiomics model showed promise for predicting response to nCRT in patients with LARC. Our predictive model based on the commonly used T2-weighted images on pelvic Magnetic Resonance Imaging (MRI) scans has the potential to be adapted in clinical practice. Novelty and Impact Statements: Methods for predicting the response of the locally advanced rectal cancer (LARC, T3-4, or N+) to neoadjuvant chemoradiotherapy (nCRT) is lacking. In the present study, we developed a new machine learning radiomics method based on T2-weighted images. As a non-invasive tool, this method facilitates prediction performance effectively. It achieves a satisfactory overall diagnostic accuracy for predicting of pCR, good response, and down-staging show an AUC of 0.908, 0.902, and 0.930 in LARC patients, respectively.

PURPOSE: Older cancer patients are at increased risk of cancer-related cognitive impairment. The purpose of this study was to assess the alterations in intrinsic brain activity associated with adjuvant chemotherapy in older women with breast cancer. METHODS: Chemotherapy treatment (CT) group included sixteen women aged ≥ 60 years (range 60–82 years) with stage I-III breast cancers, who underwent both resting-state functional magnetic resonance imaging (rs-fMRI) and neuropsychological testing with NIH Toolbox for Cognition before adjuvant chemotherapy, at time point 1 (TP1), and again within 1 month after completing chemotherapy, at time point 2 (TP2). Fourteen age- and sex-matched healthy controls (HC) underwent the same assessments at matched intervals. Three voxel-wise rs-fMRI parameters: amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo), were computed at each time point. The changes in rs-fMRI parameters from TP1 to TP2 for each group, the group differences in changes (the CT group vs. the HC group), and the group difference in the baseline rs-fMRI parameters were assessed. In addition, correlative analysis between the rs-fMRI parameters and neuropsychological testing scores was also performed. RESULTS: In the CT group, one brain region, which included parts of the bilateral subcallosal gyri and right anterior cingulate gyrus, displayed increased ALFF from TP1 to TP2 (cluster p-corrected=0.024); another brain region in the left precuneus displayed decreased fALFF from TP1 to TP2 (cluster level p-corrected=0.025). No significant changes in the rs-fMRI parameters from TP1 to TP2 were observed in the HC group. Although ALFF and fALFF alterations were observed only in the CT group, none of the between-group differences in rs-fMRI parameter changes reached statistical significance. CONCLUSIONS: Our study results of ALFF and fALFF alterations in the chemotherapy-treated women suggest that adjuvant chemotherapy may affect intrinsic brain activity in older women with breast cancer.

To predict the early identification of recurrence based on magnetic resonance imaging (MRI) in nasopharyngeal cancer (NPC) patients.The clinical and MRI data of 215 patients with local recurrent NPC were retrospectively reviewed. Logistic regression analysis was performed to distinguish the independent risk factors for the short-term (less than 24 months) local recurrence of NPC. The predictive score model was based on the regression coefficients of significant independent variables.Residual disease in the nasopharyngeal cavity (NC), masticator space invasion (MSI), skull base bone erosion (SBBE), and MRI-detected cranial nerve invasion (MDCNI) were all significant independent risk factors for the short-term recurrence of NPC (p 0.05). The receiver operating characteristic curve showed that the total score had a maximal AUC (area under the curve) value of 0.897, with a cutoff point of 10.50. The sensitivity and specificity were 79.4% and 80.5%, respectively.Residual lesions in NC, MSI, SBBE, and MDCNI are independent risk factors in predicting the short-term recurrence of NPC. The authors' findings suggest that patients with a score of more than 10.50 points should be hypervigilant regarding the possibility of short-term recurrence.

As the number of older adults in the U.S. increases, so too will the incidence of cancer and cancer-related cognitive impairment (CRCI). However, the exact underlying biological mechanism for CRCI is not yet well understood. We utilized susceptibility-weighted imaging with quantitative susceptibility mapping, a non-invasive MRI-based technique, to assess longitudinal iron deposition in subcortical gray matter structures and evaluate its association with cognitive performance in women age 60+ with breast cancer receiving adjuvant chemotherapy and age-matched women without breast cancer as controls. Brain MRI scans and neurocognitive scores from the NIH Toolbox for Cognition were obtained before chemotherapy (time point 1) and within one month after the last infusion of chemotherapy for the patients and at matched intervals for the controls (time point 2). There were 14 patients age 60+ with breast cancer (mean age 66.3 ± 5.3 years) and 13 controls (mean age 68.2 ± 6.1 years) included in this study. Brain iron increased as age increased. There were no significant between- or within- group differences in neurocognitive scores or iron deposition at time point 1 or between time points 1 and 2 (p 0.01). However, there was a negative correlation between iron in the globus pallidus and the fluid cognition composite scores in the control group at time point 1 (r = -0.71; p 0.01), but not in the chemotherapy group. Baseline iron in the putamen was negatively associated with changes in the oral reading recognition scores in the control group (r = 0.74, p 0.01), but not in the chemotherapy group. Brain iron assessment did not indicate cancer or chemotherapy related short-term differences, yet some associations with cognition were observed. Studies with larger samples and longer follow-up intervals are warranted.

Background Cognitive decline is among the most feared treatment-related outcomes of older adults with cancer. The majority of older patients with breast cancer self-report cognitive problems during and after chemotherapy. Prior neuroimaging research has been performed mostly in younger patients with cancer. The purpose of this study was to evaluate longitudinal changes in brain volumes and cognition in older women with breast cancer receiving adjuvant chemotherapy. Methods Women aged ≥ 60 years with stage I–III breast cancer receiving adjuvant chemotherapy and age-matched and sex-matched healthy controls were enrolled. All participants underwent neuropsychological testing with the US National Institutes of Health (NIH) Toolbox for Cognition and brain magnetic resonance imaging (MRI) prior to chemotherapy, and again around one month after the last infusion of chemotherapy. Brain volumes were measured using Neuroreader™ software. Longitudinal changes in brain volumes and neuropsychological scores were analyzed utilizing linear mixed models. Results A total of 16 patients with breast cancer (mean age 67.0, SD 5.39 years) and 14 age-matched and sex-matched healthy controls (mean age 67.8, SD 5.24 years) were included: 7 patients received docetaxel and cyclophosphamide (TC) and 9 received chemotherapy regimens other than TC (non-TC). There were no significant differences in segmented brain volumes between the healthy control group and the chemotherapy group pre-chemotherapy (p > 0.05). Exploratory hypothesis generating analyses focusing on the effect of the chemotherapy regimen demonstrated that the TC group had greater volume reduction in the temporal lobe (change = − 0.26) compared to the non-TC group (change = 0.04, p for interaction = 0.02) and healthy controls (change = 0.08, p for interaction = 0.004). Similarly, the TC group had a decrease in oral reading recognition scores (change = − 6.94) compared to the non-TC group (change = − 1.21, p for interaction = 0.07) and healthy controls (change = 0.09, p for interaction = 0.02). Conclusions There were no significant differences in segmented brain volumes between the healthy control group and the chemotherapy group; however, exploratory analyses demonstrated a reduction in both temporal lobe volume and oral reading recognition scores among patients on the TC regimen. These results suggest that different chemotherapy regimens may have differential effects on brain volume and cognition. Future, larger studies focusing on older adults with cancer on different treatment regimens are needed to confirm these findings. Trial registration ClinicalTrials.gov, NCT01992432. Registered on 25 November 2013. Retrospectively registered.

Objective: To evaluate the feasibility and accuracy of machine learning based texture analysis of unenhanced CT images in differentiating subclinical pheochromocytoma (sPHEO) from lipid-poor adenoma (LPA) in adrenal incidentaloma (AI). Methods: Seventy-nine patients with 80 LPA and 29 patients with 30 sPHEO were included in the study. Texture parameters were derived using imaging software (MaZda). Thirty texture features were selected and LPA was performed for the features selected. The number of positive features was used to predict results. Logistic multiple regression analysis was performed on the 30 texture features, and a predictive equation was created based on the coefficients obtained. Results: LPA yielded a misclassification rate of 19.39% in differentiating sPHEO from LPA. Our predictive model had an accuracy rate of 94.4% (102/108), with a sensitivity of 86.2% (25/29) and a specificity of 97.5% (77/79) for differentiation. When the number of positive features was greater than 8, the accuracy of prediction was 85.2% (92/108), with a sensitivity of 96.6% (28/29) and a specificity of 81% (64/79). Conclusions: Machine learning-based quantitative texture analysis of unenhanced CT may be a reliable quantitative method in differentiating sPHEO from LPA when AI is present.

Aim: We conducted a retrospective review of all brain imaging studies in the first year after allogeneic haematopoietic cell transplantation (HCT) to determine (a) the percentage of patients with CNS neurological complications based solely on undergoing brain imaging, (b) transplant-related risk factors of undergoing brain imaging, and (c) overall survival in the patients with neurological complications compared to those transplant patients who did not have brain imaging. Methods: Subjects were 543 consecutive recipients (August 2004-August 2007) of allogeneic HCT followed for overall survival for up to 6 years after HCT. Comparisons between patient groups with brain imaging and without brain imaging were tested using the Pearson chi-square test. Survival analyses with outcome time-to-brain-scan started at date of transplant and used Kaplan-Meier methods. Results: Of 543 HCT recipients, 128 patients (24%) underwent brain imaging during the first year after transplantation. There was a greater risk of brain imaging in unrelated donor transplants and in lymphoid as opposed to myeloid malignancies (respective hazard ratios 1.45 and 1.43, P=0.04). Overall survival was significantly worse in unrelated donor transplants (hazard ratio 1.42, P=0.003) and in cord blood transplants (hazard ratio 1.68, P=0.02). Landmark survival analysis of patients alive 1 year after HCT showed worse survival over the next 5 years in those who had brain imaging in the first post transplant year (P

This multicenter prospective phase II study examines the activity and tolerability of brentuximab vedotin as second-line therapy in patients with Hodgkin lymphoma that was relapsed or refractory after induction therapy. Brentuximab vedotin (1.8 mg/kg) was administered i.v. on day 1 of a 21-day cycle for a total of 4 cycles. Patients then proceeded to autologous hematopoietic cell transplantation (AHCT), if eligible, with or without additional salvage therapy, based on remission status after brentuximab vedotin. The primary endpoint was overall response rate (ORR). Secondary endpoints were safety, stem cell mobilization/collection, AHCT outcomes, and association of CD68+ with outcomes. Of 37 patients, the ORR was 68% (13 complete remission, 12 partial remission). The regimen was well tolerated with few grade 3/4 adverse events, including lymphopenia (1), neutropenia (3), rash (2), and hyperuricemia (1). Thirty-two patients (86%) were able to proceed to AHCT, with 24 patients (65%) in complete remission at time of AHCT. Thirteen patients in complete remission, 4 in partial remission, and 1 with stable disease (49%) received AHCT without salvage combination chemotherapy. CD68 expression did not correlate with response to brentuximab vedotin. The median number of stem cells mobilized was 6.0 × 106 (range, 2.6 to 34), and median number of days to obtain minimum collection (2 × 106) was 2 (range, 1 to 6). Brentuximab vedotin as second-line therapy is active, well tolerated, and allows adequate stem cell collection and engraftment. For Hodgkin lymphoma patients with relapsed/refractory disease after induction therapy, second-line brentuximab vedotin, followed by combination chemotherapy for residual disease, can effectively bridge patients to AHCT.

Brain colonization by metastatic tumor cells offers a unique opportunity to investigate microenvironmental influences on the neoplastic process. The bi-directional interplay of breast cancer cells (mesodermal origin) and brain cells (neuroectodermal origin) is poorly understood and rarely investigated. In our patients undergoing neurosurgical resection of breast-to-brain metastases, specimens from the tumor/brain interface exhibited increased active gliosis as previously described. In addition, our histological characterization revealed infiltration of neural progenitor cells (NPCs) both outside and inside the tumor margin, leading us to investigate the cellular and molecular interactions between NPCs and metastases. Since signaling by the TGF-β superfamily is involved in both developmental neurobiology and breast cancer pathogenesis, we examined the role of these proteins in the context of brain metastases. The brain-metastatic breast cancer cell line MDA-MB-231Br (231Br) expressed BMP-2 at significantly higher levels compared to its matched primary breast cancer cell line MDA-MB-231 (231). Co-culturing was used to examine bi-directional cellular effects and the relevance of BMP-2 overexpression. When co-cultured with NPCs, 231 (primary) tumor cells failed to proliferate over 15 days. However, 231Br (brain meta-static) tumor cells co-cultured with NPCs escaped growth inhibition after day 5 and proliferated, occurring in parallel with NPC differentiation into astrocytes. Using shRNA and gene knock-in, we then demonstrated BMP-2 secreted by 231Br cells mediated NPC differentiation into astrocytes and concomitant tumor cell proliferation in vitro. In xenografts, overexpression of BMP-2 in primary breast cancer cells significantly enhanced their ability to engraft and colonize the brain, thereby creating a metastatic phenotype. Conversely, BMP-2 knockdown in metastatic breast cancer cells significantly diminished engraftment and colonization. The results suggest metastatic tumor cells create a permissive neural niche by steering NPC differentiation toward astrocytes through paracrine BMP-2 signaling.