Evaluation of static and dynamic MRI for assessing response of bone sarcomas to preoperative chemotherapy: Correlation with histological necrosis

Non-invasive intravoxel incoherent motion MRI in prediction of histopathological response to neoadjuvant chemotherapy and survival outcome in osteosarcoma at the time of diagnosis

BACKGROUND

Early prediction of response to neoadjuvant chemotherapy (NACT) is important to aid personalized treatment in osteosarcoma. Diffusion-weighted Intravoxel Incoherent Motion (IVIM) MRI was used to evaluate the predictive value for response to NACT and survival outcome in osteosarcoma.

METHODS

Total fifty-five patients with biopsy-proven osteosarcoma were recruited prospectively, among them 35 patients were further analysed. Patients underwent 3 cycles of NACT (Cisplatin + Doxorubicin) followed by surgery and response adapted adjuvant chemotherapy. Treatment outcomes were histopathological response to NACT (good-response ≥ 50% necrosis and poor-response < 50% necrosis) and survival outcome (event-free survival (EFS) and overall survival (OS)). IVIM MRI was acquired at 1.5T at baseline (t0), after 1-cycle (t1) and after 3-cycles (t2) of NACT. Quantitative IVIM parameters (D, D*, f & D*.f) were estimated using advanced state-of-the-art spatial penalty based IVIM analysis method bi-exponential model with total-variation penalty function (BETV) at 3 time-points and histogram analysis was performed.

RESULTS

Good-responders: Poor-responders ratio was 13 (37%):22 (63%). EFS and OS were 31% and 69% with 16.27 and 25.9 months of median duration respectively. For predicting poor-response to NACT, IVIM parameters showed AUC = 0.87, Sensitivity = 86%, Specificity = 77% at t0, and AUC = 0.96, Sensitivity = 86%, Specificity = 100% at t1. Multivariate Cox regression analysis showed smaller tumour volume (HR = 1.002, p = 0.001) higher ADC-25th-percentile (HR = 0.047, p = 0.005) & D-Mean (HR = 0.1, p = 0.023) and lower D*-Mean (HR = 1.052, p = 0.039) were independent predictors of longer EFS (log-rank p-values: 0.054, 0.0034, 0.0017, 0.0019 respectively) and non-metastatic disease (HR = 4.33, p < 10-3), smaller tumour-volume (HR = 1.001, p = 0.042), lower D*-Mean (HR = 1.045, p = 0.056) and higher D*.f-skewness (HR = 0.544, p = 0.048) were independent predictors of longer OS (log-rank p-values: < 10-3, 0.07, < 10-3, 0.019 respectively).

CONCLUSION

IVIM parameters obtained with a 1.5T scanner along with novel BETV method and their histogram analysis indicating tumour heterogeneity were informative in characterizing NACT response and survival outcome in osteosarcoma.

A Scoring System for Predicting Neoadjuvant Chemotherapy Response in Primary High-Grade Bone Sarcomas: A Multicenter Study

Objective

Currently, there is a lack of good clinical tools for evaluating the effect of chemotherapy preoperatively on primary high‐grade bone sarcomas. Our goal was to investigate the predictive value of the clinical findings and establish a scoring system to predict chemotherapy response.

Methods

We conducted a retrospective multicenter cohort study and reviewed 322 patients with primary high‐grade bone sarcomas. Patients who routinely received neoadjuvant chemotherapy and underwent primary tumor resection with an assessment of tumor necrosis rate (TNR) were enrolled in this study. The medical records of patients were collected from November 1, 2011, to March 1, 2018, at Peking University People's Hospital (PKUPH) and Peking University Shougang Hospital (PKUSH). The mean age of the patients was 16.2 years (range 3–52 years), of whom 65.5% were male. The clinical data collected before and after neoadjuvant chemotherapy included the degree of pain, laboratory inspection, X‐ray, CT, contrast‐enhanced magnetic resonance (MR), and positron emission tomography‐computed tomography (PET‐CT). Several machine learning models, including logistic regression, decision trees, support vector machines, and neural networks, were used to classify the chemotherapy responses. Area under the curve (AUC) of the scoring system to predict chemotherapy response is the primary outcome measure.

Results

For patients without events, a minimum follow‐up of 24 months was achieved. The median follow‐up time was 43.3 months, and it ranged from 24 to 84 months. The 5 years progression‐free survival (PFS) of the included patients was 54.1%. The 5 years PFS rate was 39.7% for poor responders and 74.9% for good responders. Features such as longest diameter reduction ratio (up to three points), clear bone boundary formation (up to two points), tumor necrosis measured by magnetic resonance (up to two points), maximum standard uptake value (SUV_max) decrease (up to three points), and significant alkaline phosphatase decrease (up to 1 point) were identified as significant predictors of good histological response and constituted the scoring system. A score ≥4 predicts a good response to chemotherapy. The scoring system based on the above factors performed well, achieving an AUC of 0.893. For nonmeasurable lesions (classified by the revised Response Evaluation Criteria in Solid Tumors [RECIST 1.1]), the AUC was 0.901.

Conclusion

We first devised a well‐performing comprehensive scoring system to predict the response to neoadjuvant chemotherapy in primary high‐grade bone sarcomas.

The safe surgical margin in Ewing's sarcoma

INTRODUCTION

There is recent evidence advocating the use of post-chemotherapy MRI for safe resection of primary bone sarcoma in providing a clear margin of disease. This paper challenges this view by providing evidence obtained from comparing surgical resection margin measurements calculated off the pre- and post-chemotherapy MRIs to the post-operative histological analysis.

METHOD

A retrospective patho-radiological analysis of 10 patients treated for long bone Ewing's sarcoma. The pre- and post-chemotherapy MRI radiological measurements were correlated with the post-operative gross and microscopic histological specimens with the principle aim of determining which represented the accurate histological extent of disease and therefore which image set should be used in determining the surgical resection margins.

RESULTS

In the 10 cases there was a discrepancy in the extent of pathological disease on comparative histological and radiological assessment. The mean age at presentation was 19.5years [4-45 years], with a female bias (n = 7/10). The lower limbs were more commonly involved than the upper limb (femur n = 2/10, tibia n = 3/10, fibula n = 2/10, humerus n = 2/10, ulna n = 1/10). There was no correlation between the percentage reduction in measured volume/length of pathological bone on sequential MRI and the percentage necrosis on histology. The average discrepancy between the length of pathological bone on histology and on MRI was 2.7 cm. Using the baseline pre-chemotherapy MRI to plan the surgical resection margins, 7/10 patients had their surgical resection margins through radiologically clear but pathologically abnormal bone (without histological evidence of residual tumour). Had the post-chemotherapy MRI been used a further 3 patients would have had their tumour resected through pathologically abnormal bone. No patients had or would have had tumour resected through residual tumour.

CONCLUSIONS

The use of the post-chemotherapy MRI for planning surgical resection margins increases the risk of resecting through histologically abnormal bone that may have been previously contaminated by tumour, without significant benefit of preserving sufficient bone to facilitate joint or physeal sparing surgery. The evidence from this study supports current guidelines recommending the use of pre-chemotherapy MRI to plan safe surgical resection margins in Ewing's sarcoma.

Correlation of histopathology and multi-modal magnetic resonance imaging in childhood osteosarcoma: Predicting tumor response to chemotherapy

Background

Osteosarcoma, which is the most common malignant pediatric bone cancer, remains dependent on an imprecise systemic treatment largely unchanged in 30 years. In this study, we correlated histopathology with magnetic resonance imaging (MRI), used the correlation to extract MRI-specific features representative of tumor necrosis, and subsequently developed a novel classification model for predicting tumor response to neoadjuvant chemotherapy in pediatric patients with osteosarcoma using multi-modal MRI. The model could ultimately serve as a testable biomarker for a high-risk malignancy without successful precision treatments.

Methods

Patients with newly diagnosed high-grade appendicular osteosarcoma were enrolled in a single-center observational study, wherein patients underwent pre-surgical evaluation using both conventional MRI (post-contrast T1-weighted with fat saturation, pre-contrast T1-weighted, and short inversion-time inversion recovery (STIR)) and advanced MRI (diffusion weighted (DW) and dynamic contrast enhanced (DCE)). A classification model was established based on a direct correlation between histopathology and MRI, which was achieved through histologic-MR image co-registration and subsequent extraction of MR image features for identifying histologic tumor necrosis. By operating on the MR image features, tumor necrosis was estimated from different combinations of MR images using a multi-feature fuzzy clustering technique together with a weighted majority ruling. Tumor necrosis calculated from MR images, for either an MRI plane of interest or whole tumor volume, was compared to pathologist-estimated necrosis and necrosis quantified from digitized histologic section images using a previously described deep learning classification method.

Results

15 patients were enrolled, of whom two withdrew, one became ineligible, and two were subjected to inadequate pre-surgical imaging. MRI sequences of n = 10 patients were subsequently used for classification model development. Different MR image features, depending on the modality of MRI, were shown to be significant in distinguishing necrosis from viable tumor. The scales at which MR image features optimally signified tumor necrosis were different as well depending on the MR image type. Conventional MRI was shown capable of differentiating necrosis from viable tumor with an accuracy averaging above 90%. Conventional MRI was equally effective as DWI in distinguishing necrotic from viable tumor regions. The accuracy of tumor necrosis prediction by conventional MRI improved to above 95% when DCE-MRI was added into consideration. Volume-based tumor necrosis estimations tended to be lower than those evaluated on an MRI plane of interest.

Conclusions

The study has shown a proof-of-principle model for interpreting chemotherapeutic response using multi-modal MRI for patients with high-grade osteosarcoma. The model will continue to be evaluated as MR image features indicative of tumor response are now computable for the disease prior to surgery.

Pediatric Osteosarcoma: Pearls and Pitfalls

Osteosarcoma is a malignant bone tumor most commonly presenting in children. It has a bimodal distribution with a peak incidence occurring during the ages of 10-14 years old and in adults greater than age 65. The first peak of osteosarcoma correlates with the increased proliferation of bone during the pubertal growth period. Osteosarcoma most frequently presents with localized bone pain, swelling, and an antalgic gait. The patient may attribute symptoms to trauma or strenuous exercise, causing the patient to be managed conservatively. In these cases, the pain persists and eventually leads to further evaluation. The most common type of osteosarcoma is the conventional high-grade osteosarcoma. For conventional osteosarcoma, the diagnosis is typically made or strongly suggested based upon the initial radiographic appearance. Other types of osteosarcomas include low grade central, telangiectatic, small-cell, surface and intracortical. Consequently, it is important for radiologists to be aware of these subtypes and the imaging features that differentiate them from other etiologies to prevent a delay in treatment.

The clinical value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) semi-quantitative parameters in monitoring neoadjuvant chemotherapy response of osteosarcoma

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a non-invasive technique which could monitor tumor morphology, blood vessel dynamics, and micro-environmental changes.

PURPOSE

To evaluate the value of DCE-MRI semi-quantitative parameters in monitoring the neoadjuvant chemotherapy (NAC) response of osteosarcoma.

MATERIAL AND METHODS

Twenty-five patients pathologically confirmed as osteosarcoma received four cycles of NAC followed by surgery. All patients underwent conventional and dynamic MRI twice, before starting chemotherapy and before surgical treatment. With a reference standard of histological response (tumor necrosis rate), semi-quantitative parameters were compared between good response group (TNR ≥ 90%) and non-response group (TNR < 90%). The differences between intra- and inter-group parameters before and after NAC were analyzed by Mann-Whitney U test. Receiver operating characteristic (ROC) analysis was generated to assess the parameters' efficacy in predicting the outcome of NAC.

RESULTS

The changes were statistically significant on slope, maximum signal intensity (SI_max), time to peak (TTP), signal enhanced extent (SEE), peak percent enhancement (PPE), washout rate (WOR), and enhancement rate (ER) in the good response group (P < 0.05), while only SI_max and SEE were different in the non-response group after NAC. The changes in Slope, SI_max, TTP, SEE, WOR, and ER were markedly different (P < 0.05) between the two groups after NAC. Also, at the threshold values of 3.2%/s, 175 s, and 5.4% (slope, TTP, and ER), the sensitivity and specificity for predicting good response to chemotherapy were 83.3% and 92.3%, 91.7% and 69.2%, 84.6% and 75.0%, respectively.

CONCLUSION

Slope, TTP, and ER values could be used to evaluate and predict the response to NAC in osteosarcoma.

Computational analysis of MRIs predicts osteosarcoma chemoresponsiveness

Aim: This study aimed to improve osteosarcoma chemoresponsiveness prediction by optimization of computational analysis of MRIs. Patients & methods: Our retrospective predictive model involved osteosarcoma patients with MRI scans performed before OsteoSa MAP neoadjuvant cytotoxic chemotherapy. Results: We found that several monofractal and multifractal algorithms were able to classify tumors according to their chemoresponsiveness. The predictive clues were defined as morphological complexity, homogeneity and fractality. The monofractal feature CV for Λ'(G) provided the best predictive association (area under the ROC curve = 0.88; p <0.001), followed by   Y-axis intersection of the regression line  for  box fractal dimension, r²  for  FD_M and tumor circularity. Conclusion: This is the first full-scale study to indicate that computational analysis of pretreatment MRIs could provide imaging biomarkers for the classification of osteosarcoma according to their chemoresponsiveness.

Texture analysis for chemotherapy response evaluation in osteosarcoma using MR imaging

The efficacy of MRI-based statistical texture analysis (TA) in predicting chemotherapy response among patients with osteosarcoma was assessed. Forty patients (male: female = 31:9; age = 17.2 ± 5.7 years) with biopsy-proven osteosarcoma were analyzed in this prospective study. Patients were scheduled for three cycles of neoadjuvant chemotherapy (NACT) and diffusion-weighted MRI acquisition at three time points: at baseline (t0), after the first NACT (t1) and after the third NACT (t2) using a 1.5 T scanner. Eight patients (nonsurvivors) died during NACT while 34 patients (survivors) completed the NACT regimen followed by surgery. Histopathological evaluation was performed in the resected tumor to assess NACT response (responder [≤50% viable tumor] and nonresponder [>50% viable tumor]) and revealed nonresponder: responder = 20:12. Apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) parameters, diffusion coefficient (D), perfusion coefficient (D*) and perfusion fraction (f) were evaluated. A total of 25 textural features were evaluated on ADC, D, D* and f parametric maps and structural T1-weighted (T1W) and T2-weighted (T2W) images in the entire tumor volume using 3D TA methods gray-level cooccurrence matrix (GLCM), neighborhood gray-tone-difference matrix (NGTDM) and run-length matrix (RLM). Receiver-operating-characteristic curve analysis was performed on the selected textural feature set to assess the role of TA features (a) as marker(s) of tumor aggressiveness leading to mortality at baseline and (b) in predicting the NACT response among survivors in the course of treatment. Findings showed that the NGTDM features coarseness, busyness and strength quantifying tumor heterogeneity in D, D* and f maps and T1W and T2W images were useful markers of tumor aggressiveness in identifying the nonsurvivor group (area-under-the-curve [AUC] = 0.82-0.88) at baseline. The GLCM features contrast and correlation, NGTDM features contrast and complexity and RLM feature short-run-low-gray-level-emphasis quantifying homogeneity/terogeneity in tumor were effective markers for predicting chemotherapeutic response using D (AUC = 0.80), D* (AUC = 0.80) and T2W (AUC = 0.70) at t0, and D* (AUC = 0.80) and f (AUC = 0.70) at t1. 3D statistical TA features might be useful as imaging-based markers for characterizing tumor aggressiveness and predicting chemotherapeutic response in patients with osteosarcoma.

Personalising sarcoma care using quantitative multimodality imaging for response assessment

Over the last decades, technological developments in the field of radiology have resulted in a widespread use of imaging for personalising medicine in oncology, including patients with a sarcoma. New scanner hardware, imaging protocols, image reconstruction algorithms, radiotracers, and contrast media, enabled the assessment of the physical and biological properties of tumours associated with response to treatment. In this context, medical imaging has the potential to select sarcoma patients who do not benefit from (neo-)adjuvant treatment and facilitate treatment adaptation. Due to the biological heterogeneity in sarcomas, the challenge at hand is to acquire a practicable set of imaging features for specific sarcoma subtypes, allowing response assessment. This review provides a comprehensive overview of available clinical data on imaging-based response monitoring in sarcoma patients and future research directions. Eventually, it is expected that imaging-based response monitoring will help to achieve successful modification of (neo)adjuvant treatments and improve clinical care for these patients.

Multiparametric MRI with diffusion-weighted imaging in predicting response to chemotherapy in cases of osteosarcoma and Ewing's sarcoma

OBJECTIVE

To evaluate the multiparametric MRI in predicting chemotherapy response in pathologically proven cases of osteosarcoma and Ewing's sarcoma. Correlation between the tumor size changes and internal breakdown using RECIST 1.1, modified RECIST, quantitative apparent diffusion coefficient (ADC) and tumor volume as well as dynamic contrast-enhanced MRI (DCE-MRI).

METHODS

The study included 104 patients pathologically proved osteosarcoma (53) and Ewing`s sarcoma (51) underwent MRI examinations; before and after chemotherapy. All patients were assessed using the RECIST 1.1 criteria, m-RECIST, quantitative ADC, and tumor volume evaluation. 21 patients underwent DCE-MRI curve type with quantitative parameters. Correlation between the different evaluations was carried out. Results were correlated with the post-operative pathology in 42 patients who underwent surgery and for statistical evaluation, Those patients were classified into responders (≥90% necrosis) and non-responders (<90% necrosis).

RESULTS

The initial mean ADC of 104 patients of osteosarcoma and Ewing's sarcoma (0.90 ± 0.29) and (0.71 ± 0.16) respectively, differed significantly from that after treatment (1.62 ± 0.46) and (1.6 ± 0.39) respectively with (p<0.001).ADC variations (ADC%) in the non-progressive group were higher than those of the progressive group (128.3 ± 63.49 vs 36.34 ± 78.7) % with (p<0.001).ADC values and ADC variations were inversely correlated with morphologic changes, regardless of the effectiveness of chemotherapy expressed as changes in tumor size based on (RECIST 1.1, RECIST, and 3D volume). Linear regression analysis revealed a Pearson correlation coefficient of r=-0.427, -0.498 and -0.408, respectively with (p<0.001).An increase in the ADC value was not always associated with a reduction in tumor volume. The disease control rate (defined as the percentage of CR+PR+SD patients) was 89.4% and 93.9% according to RECIST 1.1 and m-RECIST respectively.42 out of the 104 patients had postsurgical histological evaluation as regards the chemotherapeutic response divided into two groups. ADC values showed a statistically significant difference between Group A and Group B being more evident with minimum ADC% (p<0.001).

CONCLUSION

Quantitative diffusion-weighted imaging with ADC mapping and ADC % after chemotherapy allows a detailed analysis of the treatment response in osteosarcoma and Ewing's sarcoma. The therapeutic response can be underestimated using RECIST 1.1, so the modified RECIST should be also considered.

ADVANCES IN KNOWLEDGE

Quantitative ADC especially ADC% provided an accurate non-invasive tool in the assessment of post-therapeutic cases of osteosarcoma and Ewing's sarcoma.

Current review of surgical management options for extremity bone sarcomas

Modern surgical management of extremity bone sarcomas is governed by limb-sparing surgery combined with adjuvant and neoadjuvant chemotherapy.

All the resection and reconstruction techniques have to achieve oncologic excision margins, with survival rates and functional results superior to amputation.

The main reconstruction techniques of bone defects resulted after resection are: modular endoprosthetic reconstruction; bone graft reconstruction; bone transport; resection arthrodesis; and rotationplasty.

Oncologic resection and modular endoprosthetic reconstruction are the generally approved surgical options adopted for the majority of cases in major specialized bone sarcoma centres.

Good basic principles, efficient multidisciplinary approach and sustained research in the field can provide a better future for the challenge posed by extremity bone sarcoma treatment.

Cite this article: EFORT Open Rev 2019;4:174-182. DOI: 10.1302/2058-5241.4.180048

The current status of MRI in the pre-operative assessment of intramedullary conventional appendicular osteosarcoma

Osteosarcoma is the commonest primary malignant bone tumour in children and adolescents, the majority of cases being conventional intra-medullary high-grade tumours affecting the appendicular skeleton. Treatment is typically with a combination of neo-adjuvant chemotherapy, tumour resection with limb reconstruction and post-operative chemotherapy. The current article reviews the role of magnetic resonance imaging (MRI) in the pre-operative assessment of high-grade central conventional osteosarcoma.

Surgical Innovation in Sarcoma Surgery

The field of orthopaedic oncology relies on innovative techniques to resect and reconstruct a bone or soft tissue tumour. This article reviews some of the most recent and important innovations in the field, including biological and implant reconstructions, together with computer-assisted surgery. It also looks at innovations in other fields of oncology to assess the impact and change that has been required by surgeons; topics including surgical margins, preoperative radiotherapy and future advances are discussed.

Diagnostic work up and recognition of primary bone tumours: a review

Benign abnormalities of bone are common, especially in children.

Malignant bone sarcomas are aggressive and have a poor outcome, particularly if treatment is delayed or initiated in a non-specialist centre.

Conversely, specialist tumour centres are overwhelmed with referrals for benign disease, a predictable outcome of an increasingly litigious medical environment. This review aims to arm the general orthopaedic surgeon or general practitioner with information to better discern a benign bone lesion from a malignant one, and explain the process of investigation and onward referral for those in whom malignant disease is suspected.

Cite this article: Plant J, Cannon S. Diagnostic work up and recognition of primary bone tumours: a review. EFORT Open Rev 2016;1:247-253. DOI: 10.1302/2058-5241.1.000035.

Indian data on bone and soft tissue sarcomas: A summary of published study results

Bone sarcomas are rare tumors, approximating 0.2% of all cancers, with osteosarcoma (OGS), chondrosarcoma, and Ewing sarcoma being the most common cancers in this subset. The formation of disease management groups/clinics focused on sarcomas has resulted in better understanding and management of these uncommon tumors. Multiple large-scale retrospective data from Tata Memorial Hospital (TMH) and All India Institute of Medical Sciences have reported outcomes comparable to Western data in the field of OGS and Ewing sarcoma, with interesting prognostic factors identified for further evaluation. Soft tissue sarcomas are a rare heterogeneous group of tumors, more than 50 different tumor entities. The common subtypes identified in India include Ewing sarcoma and synovial sarcoma. Valuable work regarding brachytherapy has been done by radiation oncologists from the TMH, especially in pediatric patients.