Diagnostic imaging in patients with vertebral compression fractures. / Diagnóstico radiográfico de pacientes con fracturas por compresión vertebral.

Vertebral compression fractures by osteoporosis (OVF) is usually a diagnostic problem and coincides on the age group of metastatic vertebral compression fractures (MVF). Although radiography is the first diagnostic technique, generally is not accurate for depicting demineralization and soft tissue lesions. Magnetic resonance (MRI) is the diagnostic vertebral deformities without edema and older age. Among the most relevant findings for diagnosis MVF are soft tissue mass and pedicle intensity signal asymmetries. However, reproducibility of these findings in clinical practice is moderate.

Volumetric analysis: Rethinking brain metastases response assessment.

Background: The Response Assessment in Neuro-Oncology for Brain Metastases (RANO-BM) criteria are the gold standard for assessing brain metastases (BMs) treatment response. However, they are limited by their reliance on 1D, despite the routine use of high-resolution T1-weighted MRI scans for BMs, which allows for 3D measurements. Our study aimed to investigate whether volumetric measurements could improve the response assessment in patients with BMs. Methods: We retrospectively evaluated a dataset comprising 783 BMs and analyzed the response of 185 of them from 132 patients who underwent stereotactic radiotherapy between 2007 and 2021 at 5 hospitals. We used T1-weighted MRIs to compute the volume of the lesions. For the volumetric criteria, progressive disease was defined as at least a 30% increase in volume, and partial response was characterized by a 20% volume reduction. Results: Our study showed that the proposed volumetric criteria outperformed the RANO-BM criteria in several aspects: (1) Evaluating every lesion, while RANO-BM failed to evaluate 9.2% of them. (2) Classifying response effectively in 140 lesions, compared to only 72 lesions classified by RANO-BM. (3) Identifying BM recurrences a median of 3.3 months earlier than RANO-BM criteria. Conclusions: Our study demonstrates the superiority of volumetric criteria in improving the response assessment of BMs compared to the RANO-BM criteria. Our proposed criteria allow for evaluation of every lesion, regardless of its size or shape, better classification, and enable earlier identification of progressive disease. Volumetric criteria provide a standardized, reliable, and objective tool for assessing treatment response.

Growth exponents reflect evolutionary processes and treatment response in brain metastases.

Tumor growth is the result of the interplay of complex biological processes in huge numbers of individual cells living in changing environments. Effective simple mathematical laws have been shown to describe tumor growth in vitro, or simple animal models with bounded-growth dynamics accurately. However, results for the growth of human cancers in patients are scarce. Our study mined a large dataset of 1133 brain metastases (BMs) with longitudinal imaging follow-up to find growth laws for untreated BMs and recurrent treated BMs. Untreated BMs showed high growth exponents, most likely related to the underlying evolutionary dynamics, with experimental tumors in mice resembling accurately the disease. Recurrent BMs growth exponents were smaller, most probably due to a reduction in tumor heterogeneity after treatment, which may limit the tumor evolutionary capabilities. In silico simulations using a stochastic discrete mesoscopic model with basic evolutionary dynamics led to results in line with the observed data.

The Management of Metastatic Spinal Cord Compression in Routine Clinical Practice.

(1) Background: Whether clinical management of spinal metastatic disease (SMD) matches evidence-based recommendations is largely unknown. (2) Patients and Methods: A questionnaire was distributed through Spanish Medical Societies, exploring routine practice, interpretation of the SINS and ESCC scores and agreement with items in the Tokuhashi and SINS scales, and NICE guideline recommendations. Questionnaires were completed voluntarily and anonymously, without compensation. (3) Results: Eighty specialists participated in the study. A protocol for patients with SMD existed in 33.7% of the hospitals, a specific multidisciplinary board in 33.7%, 40% of radiological reports included the ESCC score, and a prognostic scoring method was used in 73.7%. While 77.5% of the participants were familiar with SINS, only 60% used it. The different SINS and ESCC scores were interpreted correctly by 57.5-70.0% and 30.0-37.5% of the participants, respectively. Over 70% agreed with the items included in the SINS and Tokuhashi scores and with the recommendations from the NICE guideline. Differences were found across private/public sectors, hospital complexity, number of years of experience, number of patients with SMD seen annually and especially across specialties. (4) Conclusions: Most specialists know and agree with features defining the gold standard treatment for patients with SCC, but many do not apply them.

A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data.

Brain metastasis (BM) is one of the main complications of many cancers, and the most frequent malignancy of the central nervous system. Imaging studies of BMs are routinely used for diagnosis of disease, treatment planning and follow-up. Artificial Intelligence (AI) has great potential to provide automated tools to assist in the management of disease. However, AI methods require large datasets for training and validation, and to date there have been just one publicly available imaging dataset of 156 BMs. This paper publishes 637 high-resolution imaging studies of 75 patients harboring 260 BM lesions, and their respective clinical data. It also includes semi-automatic segmentations of 593 BMs, including pre- and post-treatment T1-weighted cases, and a set of morphological and radiomic features for the cases segmented. This data-sharing initiative is expected to enable research into and performance evaluation of automatic BM detection, lesion segmentation, disease status evaluation and treatment planning methods for BMs, as well as the development and validation of predictive and prognostic tools with clinical applicability.

Growth dynamics of brain metastases differentiate radiation necrosis from recurrence.

Background: Radiation necrosis (RN) is a frequent adverse event after fractionated stereotactic radiotherapy (FSRT) or single-session stereotactic radiosurgery (SRS) treatment of brain metastases (BMs). It is difficult to distinguish RN from progressive disease (PD) due to their similarities in the magnetic resonance images. Previous theoretical studies have hypothesized that RN could have faster, although transient, growth dynamics after FSRT/SRS, but no study has proven that hypothesis using patient data. Thus, we hypothesized that lesion size time dynamics obtained from growth laws fitted with data from sequential volumetric measurements on magnetic resonance images may help in discriminating recurrent BMs from RN events. Methods: A total of 101 BMs from different institutions, growing after FSRT/SRS (60 PDs and 41 RNs) in 86 patients, displaying growth for at least 3 consecutive MRI follow-ups were selected for the study from a database of 1031 BMs. The 3 parameters of the Von Bertalanffy growth law were determined for each BM and used to discriminate statistically PDs from RNs. Results: Growth exponents in patients with RNs were found to be substantially larger than those of PD, due to the faster, although transient, dynamics of inflammatory processes. Statistically significant differences (P < .001) were found between both groups. The receiver operating characteristic curve (AUC = 0.76) supported the ability of the growth law exponent to classify the events. Conclusions: Growth law exponents obtained from sequential longitudinal magnetic resonance images after FSRT/SRS can be used as a complementary tool in the differential diagnosis between RN and PD.

Lung Cancer Screening: Survival in an Extensive Early Detection Program in Spain (I-ELCAP). / Cribado de cáncer de pulmón: Supervivencia en un amplio programa de detección precoz en España (I-ELCAP).

INTRODUCTION: Lung cancer (LC) is usually diagnosed at advanced stages with only a 12% 5-year survival. Trials as NLST and NELSON show a mortality decrease, which justifies implementation of lung cancer screening in risk population. Our objective was to show survival results of the largest LC screening program in Spain with low dosage computed tomography (LDCT). METHODS: Clinical records from International Early Lung Cancer Detection Program (IELCAP) at Valencia, Spain were analysed. This program recruited volunteers, ever-smokers aged 40-80 years, since 2008. Results are compared to those from other similar sizeable programs. RESULTS: A total of 8278 participants were screened with at least two-rounds until November 2020. A mean of 6 annual screening rounds were performed. We detected 239 tumours along 12-year follow-up. Adenocarcinoma was the most common histology, being 61.3% at stage I. The lung cancer prevalence and incidence proportion was 1.5% and 1.4%, respectively with an annual detection rate of 0.17. One-year survival and 10-year survival were 90% and 80.1%, respectively. Adherence was 96.84%. CONCLUSION: Largest lung cancer screening in Spain shows that survival is improved when is performed in multidisciplinary team experienced in management of LC, and is comparable to similar screening programs.

Ki-67 immunoexpression and radiological assessment of necrosis improves accuracy of conventional and modified core biopsy systems in predicting the final grade assigned to adult-soft tissue sarcomas. An international collaborative study.

Based on the French Federation Nationale des Centers de Lutte Contre le Cancer (FNCLCC) grading system, this study assesses the accuracy of conventional and modified core biopsy (CB) systems in predicting the final grade (low vs high) assigned to the resected specimen. Substituting Ki-67 immunoexpression for mitotic count, and radiological for histological assessment of necrosis, we used two modified FNCLCC CB grading systems: (1) Ki-67 immunoexpression alone, and (2) Ki-67 plus radiological assessment of necrosis. We graded 199 soft tissue sarcomas (STS) from nine centers, and compared the results for the conventional (obtained from local histopathology reports) and modified CB systems with the final FNCLCC grading of the corresponding resected specimens. Due to insufficient sample quality or lack of available radiologic data, five cases were not evaluated for Ki67 or radiological assessment of necrosis. The conventional FNCLCC CB grading system accurately identified 109 of the 130 high-grade cases (83.8%). The CB grading matched the final FNCLCC grading (low vs high) in 175 (87.9%) of the 199 resected tumors; overestimating the final grade in three cases and underestimating in 21 cases. Modified system 1 (Ki-67) accurately identified 117 of the 130 high-grade cases (90.0%). The CB grading matched the final FNCLCC grading (low vs high) in 175 (89.7%) of the 195 evaluated cases; overestimating seven and underestimating 13 cases. Modified system 2 (Ki-67 plus radiological necrosis) accurately identified 120 of the 130 high-grade cases (92.3%). This last matched the final FNCLCC grading (low vs high) in 177 (91.2%) of the 194 evaluated cases; overestimating seven and underestimating 10 cases. Modified system 2 obtained highest area under ROC curves, although not statistically significant. Underestimated CB grades did not correlate with histological subtypes, although many of the discrepant cases were myxoid tumors (myxofibrosarcomas or myxoid liposarcomas), leiomyosarcomas or undifferentiated pleomorphic/spindle cell sarcomas. Using modified FNCLCC CB grading systems to replace conventional mitotic count and histologic assessment of necrosis may improve the distinction between low and high-grade STS on CB. Our study confirms that classifying grade 1 as low grade and grades 2 and 3 as high grade improves correlation between CB and final grade by up to 21%, irrespective of CB system used. A higher than expected Ki-67 score in a low-grade sarcoma diagnosed on CB should raise concern that a higher-grade component may not have been sampled. Furthermore, correlation of all clinicopathological and radiological findings at multidisciplinary meetings is essential to assess the histological grade on CB as accurately as possible.

Usefulness of ultrasound in dermatofibrosarcoma protuberans and correlation with histopathological findings: A series of 30 cases.

BACKGROUND: Small series of ultrasound findings in dermatofibrosarcoma protuberans (DFSP) have been published, but the usefulness of this technique as a preoperative planning tool for tumor resection has not been studied. MATERIALS AND METHODS: We retrospectively reviewed patients with DFSP at our hospital that underwent ultrasound examination. Depth of invasion was evaluated by ultrasound and histopathology. Accuracy of ultrasound for assessing depth of tumor invasion was estimated. RESULTS: Thirty histopathologically confirmed DFSPs were studied. Classic finger-like projections were observed in 73.3% of cases. A posterior hyperechoic area extending deep into the subcutaneous tissue correlated with the honeycomb DFSP pattern and was observed in 53.3% of patients. Concordance between ultrasound and histopathologic depth measurements was excellent. Lateral tumor extension and Doppler activity were not evaluated in our series. CONCLUSION: Ultrasound showed excellent prediction of depth of invasion. Further studies are required to define the usefulness of ultrasound for determining lateral tumor extension.

Administration of iodinated contrast: What is the risk in cancer patients?

OBJECTIVE: Despite the association between intravenous contrast and kidney injury, few studies exist in oncology. Our objective was to estimate kidney outcomes following iodinated contrast-enhanced computed tomography (CECT) in cancer patients, and to evaluate whether self-assessment questionnaires can identify kidney injury risk factors. METHODS: This prospective observational study included 289 patients who underwent a CECT scan between March and May 2017 in a hospital setting. All patients completed the modified European Society of Urogenital Radiology (ESUR) questionnaire and had an estimated glomerular filtration rate (eGFR) >30 ml/min/1.73 m2 on the day of the examination. Outcomes were followed for 4 months. Univariate and logistic regression analyses were carried out. RESULTS: In the logistic regression analysis, the only variables statistically associated with deterioration in the eGFR were age, (odds ratio (OR) = 1.091, p = 0.003), female sex, (OR 0.22, p = 0.020) and arterial hypertension (AH), (OR = 3.57, p = 0.019). Regarding exitus, only the group with a worse eGFR was close to predictive statistical significance (OR = 2.48, p = 0.09). CONCLUSIONS: The administration of iodinated contrast in cancer patients was not associated with an increase in kidney outcomes. Risk factors in these patients were age, sex and AH.

Universal scaling laws rule explosive growth in human cancers.

Most physical and other natural systems are complex entities composed of a large number of interacting individual elements. It is a surprising fact that they often obey the so-called scaling laws relating an observable quantity with a measure of the size of the system. Here we describe the discovery of universal superlinear metabolic scaling laws in human cancers. This dependence underpins increasing tumour aggressiveness, due to evolutionary dynamics, which leads to an explosive growth as the disease progresses. We validated this dynamic using longitudinal volumetric data of different histologies from large cohorts of cancer patients. To explain our observations we put forward increasingly-complex biologically-inspired mathematical models that captured the key processes governing tumor growth. Our models predicted that the emergence of superlinear allometric scaling laws is an inherently three-dimensional phenomenon. Moreover, the scaling laws thereby identified allowed us to define a set of metabolic metrics with prognostic value, thus providing added clinical utility to the base findings.

Metastatic Versus Osteoporotic Vertebral Fractures on MRI: A Blinded, Multicenter, and Multispecialty Observer Agreement Evaluation.

BACKGROUND: MRI is assumed to be valid for distinguishing metastatic vertebral fractures (MVFs) from osteoporotic vertebral fractures (OVFs). This study assessed (1) concordance between the image-based diagnosis of MVF versus OVF and the reference (biopsy or follow-up of >6 months), (2) interobserver and intraobserver agreement on key imaging findings and the diagnosis of MVF versus OVF, and (3) whether disclosing a patient's history of cancer leads to variations in diagnosis, concordance, or agreement. PATIENTS AND METHODS: This retrospective cohort study included clinical data and imaging from 203 patients with confirmed MVF or OVF provided to 25 clinicians (neurosurgeons, radiologists, orthopedic surgeons, and radiation oncologists). From January 2018 through October 2018, the clinicians interpreted images in conditions as close as possible to routine practice. Each specialist assessed data twice, with a minimum 6-week interval, blinded to assessments made by other clinicians and to their own previous assessments. The kappa statistic was used to assess interobserver and intraobserver agreement on key imaging findings, diagnosis (MVF vs OVF), and concordance with the reference. Subgroup analyses were based on clinicians' specialty, years of experience, and complexity of the hospital where they worked. RESULTS: For diagnosis of MVF versus OVF, interobserver agreement was fair, whereas intraobserver agreement was substantial. Only the latter improved to almost perfect when a patient's history of cancer was disclosed. Interobserver agreement for key imaging findings was fair or moderate, whereas intraobserver agreement on key imaging findings was moderate or substantial. Concordance between the diagnosis of MVF versus OVF and the reference was moderate. Results were similar regardless of clinicians' specialty, experience, and hospital category. CONCLUSIONS: When MRI is used to distinguish MVF versus OVF, interobserver agreement and concordance with the reference were moderate. These results cast doubt on the reliability of basing such a diagnosis on MRI in routine practice.

Prognostic models based on imaging findings in glioblastoma: Human versus Machine.

Many studies have built machine-learning (ML)-based prognostic models for glioblastoma (GBM) based on radiological features. We wished to compare the predictive performance of these methods to human knowledge-based approaches. 404 GBM patients were included (311 discovery and 93 validation). 16 morphological and 28 textural descriptors were obtained from pretreatment volumetric postcontrast T1-weighted magnetic resonance images. Different prognostic ML methods were developed. An optimized linear prognostic model (OLPM) was also built using the four significant non-correlated parameters with individual prognosis value. OLPM achieved high prognostic value (validation c-index = 0.817) and outperformed ML models based on either the same parameter set or on the full set of 44 attributes considered. Neural networks with cross-validation-optimized attribute selection achieved comparable results (validation c-index = 0.825). ML models using only the four outstanding parameters obtained better results than their counterparts based on all the attributes, which presented overfitting. In conclusion, OLPM and ML methods studied here provided the most accurate survival predictors for glioblastoma to date, due to a combination of the strength of the methodology, the quality and volume of the data used and the careful attribute selection. The ML methods studied suffered overfitting and lost prognostic value when the number of parameters was increased.

Morphological MRI-based features provide pretreatment survival prediction in glioblastoma.

OBJECTIVES: We wished to determine whether tumor morphology descriptors obtained from pretreatment magnetic resonance images and clinical variables could predict survival for glioblastoma patients. METHODS: A cohort of 404 glioblastoma patients (311 discoveries and 93 validations) was used in the study. Pretreatment volumetric postcontrast T1-weighted magnetic resonance images were segmented to obtain the relevant morphological measures. Kaplan-Meier, Cox proportional hazards, correlations, and Harrell's concordance indexes (c-indexes) were used for the statistical analysis. RESULTS: A linear prognostic model based on the outstanding variables (age, contrast-enhanced (CE) rim width, and surface regularity) identified a group of patients with significantly better survival (p < 0.001, HR = 2.57) with high accuracy (discovery c-index = 0.74; validation c-index = 0.77). A similar model applied to totally resected patients was also able to predict survival (p < 0.001, HR = 3.43) with high predictive value (discovery c-index = 0.81; validation c-index = 0.92). Biopsied patients with better survival were well identified (p < 0.001, HR = 7.25) by a model including age and CE volume (c-index = 0.87). CONCLUSIONS: Simple linear models based on small sets of meaningful MRI-based pretreatment morphological features and age predicted survival of glioblastoma patients to a high degree of accuracy. The partition of the population using the extent of resection improved the prognostic value of those measures. KEY POINTS: • A combination of two MRI-based morphological features (CE rim width and surface regularity) and patients' age outperformed previous prognosis scores for glioblastoma. • Prognosis models for homogeneous surgical procedure groups led to even more accurate survival prediction based on Kaplan-Meier analysis and concordance indexes.

Correction to: Morphological MRI-based features provide pretreatment survival prediction in glioblastoma.

The original version of this article, published on 15 October 2018, unfortunately contained a mistake. The following correction has therefore been made in the original: The name of Mariano Amo-Salas and the affiliation of Ismael Herruzo were presented incorrectly.

Tumor Surface Regularity at MR Imaging Predicts Survival and Response to Surgery in Patients with Glioblastoma.

Purpose To evaluate the prognostic and predictive value of surface-derived imaging biomarkers obtained from contrast material-enhanced volumetric T1-weighted pretreatment magnetic resonance (MR) imaging sequences in patients with glioblastoma multiforme. Materials and Methods A discovery cohort from five local institutions (165 patients; mean age, 62 years ± 12 [standard deviation]; 43% women and 57% men) and an independent validation cohort (51 patients; mean age, 60 years ± 12; 39% women and 61% men) from The Cancer Imaging Archive with volumetric T1-weighted pretreatment contrast-enhanced MR imaging sequences were included in the study. Clinical variables such as age, treatment, and survival were collected. After tumor segmentation and image processing, tumor surface regularity, measuring how much the tumor surface deviates from a sphere of the same volume, was obtained. Kaplan-Meier, Cox proportional hazards, correlations, and concordance indexes were used to compare variables and patient subgroups. Results Surface regularity was a powerful predictor of survival in the discovery (P = .005, hazard ratio [HR] = 1.61) and validation groups (P = .05, HR = 1.84). Multivariate analysis selected age and surface regularity as significant variables in a combined prognostic model (P < .001, HR = 3.05). The model achieved concordance indexes of 0.76 and 0.74 for the discovery and validation cohorts, respectively. Tumor surface regularity was a predictor of survival for patients who underwent complete resection (P = .01, HR = 1.90). Tumors with irregular surfaces did not benefit from total over subtotal resections (P = .57, HR = 1.17), but those with regular surfaces did (P = .004, HR = 2.07). Conclusion The surface regularity obtained from high-resolution contrast-enhanced pretreatment volumetric T1-weighted MR images is a predictor of survival in patients with glioblastoma. It may help in classifying patients for surgery.

Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study.

OBJECTIVE: To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach. METHODS: Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotation-invariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one. RESULTS: In the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 ± 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (four features, AUC = 0.963 ± 0.054) and melanoma BM (eight features, AUC = 0.936 ± 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 ± 0.180). CONCLUSION: Volumetric MRI texture features can be useful to differentiate brain metastases from different primary cancers after quantizing the images with the proper number of gray-levels. KEY POINTS: • Texture analysis is a promising source of biomarkers for classifying brain neoplasms. • MRI texture features of brain metastases could help identifying the primary cancer. • Volumetric texture features are more discriminative than traditional 2D texture features.

2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution.

OBJECTIVE: To find structural differences between brain metastases of lung and breast cancer, computing their heterogeneity parameters by means of both 2D and 3D texture analysis (TA). MATERIALS AND METHODS: Patients with 58 brain metastases from breast (26) and lung cancer (32) were examined by MR imaging. Brain lesions were manually delineated by 2D ROIs on the slices of contrast-enhanced T1-weighted (CET1) images, and local binary patterns (LBP) maps were created from each region. Histogram-based (minimum, maximum, mean, standard deviation, and variance), and co-occurrence matrix-based (contrast, correlation, energy, entropy, and homogeneity) 2D, weighted average of the 2D slices, and true 3D TA were obtained on the CET1 images and LBP maps. RESULTS: For LBP maps and 2D TA contrast, correlation, energy, and homogeneity were identified as statistically different heterogeneity parameters (SDHPs) between lung and breast metastasis. The weighted 3D TA identified entropy as an additional SDHP. Only two texture indexes (TI) were significantly different with true 3D TA: entropy and energy. All these TIs discriminated between the two tumor types significantly by ROC analysis. For the CET1 images there was no SDHP at all by 3D TA. CONCLUSION: Our results indicate that the used textural analysis methods may help with discriminating between brain metastases of different primary tumors.

A radiomics evaluation of 2D and 3D MRI texture features to classify brain metastases from lung cancer and melanoma.

Brain metastases are occasionally detected before diagnosing their primary site of origin. In these cases, simple visual examination of medical images of the metastases is not enough to identify the primary cancer, so an extensive evaluation is needed. To avoid this procedure, a radiomics approach on magnetic resonance (MR) images of the metastatic lesions is proposed to classify two of the most frequent origins (lung cancer and melanoma). In this study, 50 T1-weighted MR images of brain metastases from 30 patients were analyzed: 27 of lung cancer and 23 of melanoma origin. A total of 43 statistical texture features were extracted from the segmented lesions in 2D and 3D. Five predictive models were evaluated using a nested cross-validation scheme. The best classification results were achieved using 3D texture features for all the models, obtaining an average AUC > 0.9 in all cases and an AUC = 0.947 ± 0.067 when using the best model (naïve Bayes).