Prognostic relevance of radiological findings on early postoperative MRI for 187 consecutive glioblastoma patients receiving standard therapy.

Several prognostic factors are known to influence survival for patients treated with IDH-wildtype glioblastoma, but unknown factors may remain. We aimed to investigate the prognostic implications of early postoperative MRI findings. A total of 187 glioblastoma patients treated with standard therapy were consecutively included. Patients either underwent a biopsy or surgery followed by an early postoperative MRI. Progression-free survival (PFS) and overall survival (OS) were analysed for known prognostic factors and MRI-derived candidate factors: resection status as defined by the response assessment in neuro-oncology (RANO)-working group (no contrast-enhancing residual tumour, non-measurable contrast-enhancing residual tumour, or measurable contrast-enhancing residual tumour) with biopsy as reference, contrast enhancement patterns (no enhancement, thin linear, thick linear, diffuse, nodular), and the presence of distant tumours. In the multivariate analysis, patients with no contrast-enhancing residual tumour or non-measurable contrast-enhancing residual tumour on the early postoperative MRI displayed a significantly improved progression-free survival compared with patients receiving only a biopsy. Only patients with non-measurable contrast-enhancing residual tumour showed improved overall survival in the multivariate analysis. Contrast enhancement patterns were not associated with survival. The presence of distant tumours was significantly associated with both poor progression-free survival and overall survival and should be considered incorporated into prognostic models.

Radiomics for residual tumour detection and prognosis in newly diagnosed glioblastoma based on postoperative [11C] methionine PET and T1c-w MRI.

Personalized treatment strategies based on non-invasive biomarkers have potential to improve patient management in patients with newly diagnosed glioblastoma (GBM). The residual tumour burden after surgery in GBM patients is a prognostic imaging biomarker. However, in clinical patient management, its assessment is a manual and time-consuming process that is at risk of inter-rater variability. Furthermore, the prediction of patient outcome prior to radiotherapy may identify patient subgroups that could benefit from escalated radiotherapy doses. Therefore, in this study, we investigate the capabilities of traditional radiomics and 3D convolutional neural networks for automatic detection of the residual tumour status and to prognosticate time-to-recurrence (TTR) and overall survival (OS) in GBM using postoperative [11C] methionine positron emission tomography (MET-PET) and gadolinium-enhanced T1-w magnetic resonance imaging (MRI). On the independent test data, the 3D-DenseNet model based on MET-PET achieved the best performance for residual tumour detection, while the logistic regression model with conventional radiomics features performed best for T1c-w MRI (AUC: MET-PET 0.95, T1c-w MRI 0.78). For the prognosis of TTR and OS, the 3D-DenseNet model based on MET-PET integrated with age and MGMT status achieved the best performance (Concordance-Index: TTR 0.68, OS 0.65). In conclusion, we showed that both deep-learning and conventional radiomics have potential value for supporting image-based assessment and prognosis in GBM. After prospective validation, these models may be considered for treatment personalization.

Dynamic contrast-enhanced magnetic resonance imaging assessment of residual tumor angiogenesis after insufficient microwave ablation and donafenib adjuvant therapy.

To analyze the correlation between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) permeability parameters and serum vascular endothelial growth factor (VEGF) levels in a rabbit VX2 liver cancer model with insufficient microwave ablation (MWA), to observe the dynamic changes in residual tumor angiogenesis in the short term after MWA, and to assess the effectiveness of donafenib as adjuvant therapy. Forty rabbits with VX2 liver tumors were randomly divided into three groups: an insufficient MWA group (n = 15), a combined treatment group (n = 15) and a control group (n = 10). The dynamic changes in VEGF expression after MWA and the effectiveness of donafenib as adjuvant therapy were evaluated by DCE-MRI and serum VEGF levels before surgery and 1, 3, 7, and 14 days after surgery. The correlation between the volume translate constant (Ktrans) of DCE-MRI parameters and serum VEGF levels fluctuated after ablation, but the coefficient was always positive (all p < 0.001). Repeated-measures ANOVA revealed significant changes in the serum VEGF concentration (F = 40.905, p < 0.001; partial η2 = 0.689), Ktrans (F = 13.388, p < 0.001; partial η2 = 0.420), and tumor diameter in each group (F = 34.065, p < 0.001; partial η2 = 0.648) at all five time points. Pairwise comparisons showed that the serum VEGF level, Ktrans value and tumor diameter in the insufficient MWA group and combined treatment group were significantly lower at 1 d than in the control group, but these values gradually increased over time (all p < 0.05). Ktrans and tumor diameter were significantly greater in the insufficient MWA group than in the control group at 14 days (all p < 0.05). The serum VEGF concentration, Ktrans, and tumor diameter were significantly lower in the combined treatment group than in the other two groups at 3, 7, and 14 days (all p < 0.05). Ktrans is positively correlated with the serum VEGF concentration. Ktrans and the serum VEGF concentration changed significantly after treatment with insufficient ablation or in combination with donafenib, and Ktrans may change faster. Insufficient MWA promotes the progression of residual tumors. Adjuvant treatment with donafenib is effective.

Non-navigated 2D intraoperative ultrasound: An unsophisticated surgical tool to achieve high standards of care in glioma surgery.

PURPOSE: In an era characterized by rapid progression in neurosurgical technologies, traditional tools such as the non-navigated two-dimensional intraoperative ultrasound (nn-2D-IOUS) risk being overshadowed. Against this backdrop, this study endeavors to provide a comprehensive assessment of the clinical efficacy and surgical relevance of nn-2D-IOUS, specifically in the context of glioma resections. METHODS: This retrospective study undertaken at a single center evaluated 99 consecutive, non-selected patients diagnosed with both high-grade and low-grade gliomas. The primary objective was to assess the proficiency of nn-2D-IOUS in generating satisfactory image quality, identifying residual tumor tissue, and its influence on the extent of resection. To validate these results, early postoperative MRI data served as the reference standard. RESULTS: The nn-2D-IOUS exhibited a high level of effectiveness, successfully generating good quality images in 79% of the patients evaluated. With a sensitivity rate of 68% and a perfect specificity of 100%, nn-2D-IOUS unequivocally demonstrated its utility in intraoperative residual tumor detection. Notably, when total tumor removal was the surgical objective, a resection exceeding 95% of the initial tumor volume was achieved in 86% of patients. Additionally, patients in whom residual tumor was not detected by nn-2D-IOUS, the mean volume of undetected tumor tissue was remarkably minimal, averaging at 0.29 cm3. CONCLUSION: Our study supports nn-2D-IOUS's invaluable role in glioma surgery. The results highlight the utility of traditional technologies for enhanced surgical outcomes, even when compared to advanced alternatives. This is particularly relevant for resource-constrained settings and emphasizes optimizing existing tools for efficient patient care. NCT05873946 - 24/05/2023 - Retrospectively registered.

The Role of MRI in Assessing Residual Breast Cancer After Neoadjuvant Chemotherapy.

INTRODUCTION: Breast cancer is the most common malignancy among women in the world. The role of neoadjuvant chemotherapy (NAC) in the management of breast cancer is increasing. The decision about the management after NAC depends mainly on the tumor response to NAC. OBJECTIVES: The role of the current study is to evaluate the role of the MRI scan in assessing the residual disease after NAC, which would help in decision making regarding the best treatment plan for the patient. PATIENTS AND METHODS: We did this retrospective review for all patients who were diagnosed with breast cancer in our center and had NAC over four years. All patients in our study had a post-NAC magnetic resonance imaging (MRI) scan to assess the residual tumor size. A 2×2 table was used to calculate the diagnostic accuracy, and SPSS software version 25 was used to get the correlation coefficients between the post-NAC MRI measurements and pathological size. RESULTS: 28 female patients were included in our study. The average age was 45.25 ± 10 years. We utilized the tumor size on histology as the standard for comparison. We calculated MRI sensitivity, specificity, PPV, and NPV rates of 90.9%, 100%, 100%, and 94.4%, respectively. The correlation coefficient was strong (r = 0.859, P = 0.01). CONCLUSION: Magnetic resonance imaging is a good test to assess the residual tumor disease after NAC in breast cancer patients. However, cases of under- and overestimation are still seen, which require more caution when making a decision regarding the management of such cases.

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks.

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.

Performance of baseline FDG-PET/CT radiomics for prediction of bone marrow minimal residual disease status in the LyMa-101 trial.

The prognostic value of 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) at baseline or the predictive value of minimal residual disease (MRD) detection appear as potential tools to improve mantle cell lymphoma (MCL) patients' management. The LyMa-101, a phase 2 trial of the LYSA group (ClinicalTrials.gov:NCT02896582) reported induction therapy with obinutuzumab, a CD20 monoclonal antibody. Herein, we investigated the added prognostic value of radiomic features (RF) derived from FDG-PET/CT at diagnosis for MRD value prediction. FDG-PET/CT of 59 MCL patients included in the LyMa-101 trial have been independently, blindly and centrally reviewed. RF were extracted from the disease area with the highest uptake and from the total metabolic tumor volume (TMTV). Two models of machine learning were used to compare several combinations for prediction of MRD before autologous stem cell transplant consolidation (ASCT). Each algorithm was generated with or without constrained feature selections for clinical and laboratory parameters. Both algorithms showed better discrimination performances for negative vs positive MRD in the lesion with the highest uptake than in the TMTV. The constrained use of clinical and biological features showed a clear loss in sensitivity for the prediction of MRD status before ASCT, regardless of the machine learning model. These data plead for the importance of FDG-PET/CT RF compared to clinical and laboratory parameters and also reinforced the previously made hypothesis that the prognosis of the disease in MCL patients is linked to the most aggressive contingent, within the lesion with the highest uptake.

How to evaluate extent of resection in diffuse gliomas: from standards to new methods.

PURPOSE OF REVIEW: Maximal safe tumor resection represents the current standard of care for patients with newly diagnosed diffuse gliomas. Recent efforts have highlighted the prognostic value of extent of resection measured as residual tumor volume in patients with isocitrate dehydrogenase (IDH)-wildtype and -mutant gliomas. Accurate assessment of such information therefore appears essential in the context of clinical trials as well as patient management. RECENT FINDINGS: Current recommendations for evaluation of extent of resection rest upon standardized postoperative MRI including contrast-enhanced T1-weighted sequences, T2-weighted/fluid-attenuated-inversion-recovery sequences, and diffusion-weighted imaging to differentiate postoperative tumor volumes from ischemia and nonspecific imaging findings. In this context, correct timing of postoperative imaging within the postoperative period is of utmost importance. Advanced MRI techniques including perfusion-weighted MRI and MR-spectroscopy may add further insight when evaluating residual tumor remnants. Positron emission tomography (PET) using amino acid tracers proves beneficial in identifying metabolically active tumor beyond anatomical findings on conventional MRI. SUMMARY: Future efforts will have to refine recommendations on postoperative assessment of residual tumor burden in respect to differences between IDH-wildtype and -mutant gliomas, and incorporate the emerging role of advanced imaging modalities like amino acid PET.

Detecting residual soft tissue sarcoma after unplanned excision; model-free analysis of dynamic contrast-enhanced MRI at short-term follow-up.

OBJECTIVES: To evaluate diagnostic utility of additional DCE-MRI for detecting residual soft tissue sarcomas (STS) after unplanned excision (UPE). METHODS: We retrospectively evaluated 32 patients with UPE of STS, followed by conventional MRI with DCE-MRI and wide excision (WE), between November 2019 and January 2022. Residual tumors on conventional MRI were categorized into three groups: Lesion-type-0, no abnormal enhancement, Lesion-type-1, an indeterminate lesion, and Lesion-type-2, a definite enhancing nodule. On DCE-MRI, ROIs were manually placed on enhancing areas of suspected residual tumor. The mean and 95th percentile values of AUC of time-intensity curve were calculated at 60, 90, and 120 s of Enhancement-cycle-1 and -2. Optimal DCE parameters were identified by ROC analysis. Diagnostic performance of conventional MRI and DCE-MRI was compared using McNemar's test. RESULTS: On WE, residual tumor was present in 23 (71.9%) of 32 patients. On MRI, Lesion-type-1 was found in 16/32 (50%) patients and Lesion-type-2 in 16/32 (50%). The optimal DCE parameter was the 95th percentile value of AUC at 120s of Enhancement-cycle-2. The sensitivity, specificity, and AUC were as follows: 65.2% (95% CI, 45.8-85.7%), 88.9% (CI, 68.4-100%), and 0.77 (CI, 0.62-0.92) for conventional MRI, and 100%, 55.6% (CI, 23.1-88.0%), and 0.78 (CI, 0.61-0.95) for combined conventional and DCE-MRI. CONCLUSIONS: Additional DCE-MRI aided in detecting residual STS after UPE, particularly in cases without definite soft tissue nodular enhancement. ADVANCES IN KNOWLEDGE: Close follow up may be suggested for patients showing abnormality in DCE-MRI, with more suspicion of residual tumor.

Predicting the response to neoadjuvant chemotherapy. Can the addition of tomosynthesis improve the accuracy of contrast-enhanced spectral mammography? A comparison with breast MRI.

OBJECTIVES: Image monitoring is essential to monitor response to neoadjuvant chemotherapy (NACT). Whilst breast MRI is the gold-standard technique, evidence suggests contrast-enhanced spectral mammography (CESM) is comparable. We investigate whether the addition of digital breast tomosynthesis (DBT) to CESM increases the accuracy of response prediction. METHODS: Women receiving NACT for breast cancer were included. Imaging with CESM+DBT and MRI was performed post-NACT. Imaging appearance was compared with pathological specimens. Accuracy for predicting pathological complete response (pCR) and concordance with size of residual disease was calculated. RESULTS: Sixteen cancers in 14 patients were included, 10 demonstrated pCR. Greatest accuracy for predicting pCR was with CESM enhancement (accuracy: 81.3%, sensitivity: 100%, specificity: 57.1%), followed by MRI (accuracy: 62.5%, sensitivity: 44.4%, specificity: 85.7%). Concordance with invasive tumour size was greater for CESM enhancement than MRI, concordance-coefficients 0.70 vs 0.66 respectively. MRI demonstrated greatest concordance with whole tumour size followed by CESM+microcalcification, concordance coefficients 0.86 vs 0.69. DBT did not improve accuracy for prediction of pCR or residual disease size. CESM+DBT underestimated size of residual disease, MRI overestimated but no significant differences were seen (p>0.05). CONCLUSIONS: CESM is similar to MRI for predicting residual disease post-NACT. Size of enhancement alone demonstrates best concordance with invasive disease. Inclusion of residual microcalcification improves concordance with ductal carcinoma in situ. The addition of DBT to CESM does not improve accuracy. ADVANCES IN KNOWLEDGE: The addition ofDBT to CESM does not improve NACT response prediction. CESM enhancement has greatest accuracy for residual invasive disease, CESM+calcification has greater accuracy for residual in situ disease.

Quantitative analysis with multiphase contrast-enhanced computed tomography to evaluate residual tumor activity of hepatocellular carcinoma after DEB-TACE.

Most hepatocellular carcinomas (HCC) treated by transcatheter arterial chemoembolization with drug-eluting beads (DEB-TACE) are characterized by coagulation necrosis; therefore, it is often difficult to distinguish enhancement in the arterial phase that would lead to false negative evaluation. This study aimed to evaluate the specificity and sensitivity of the difference value of multiphase contrast-enhanced computed tomography (CECT) in predicting residual tumor activity in HCC lesions after DEB-TACE. This retrospective diagnostic study analyzed CECT images of 73 HCC lesions in 57 patients 20 to 40 days (average 28 days) after DEB-TACE treatment at our Hospital from January to December 2019. Postoperative pathology or digital subtraction angiography images were used as references. Residual tumor activity after the first intervention was determined based on the presence of tumor staining in digital subtraction angiography or the postoperative pathological discovery of HCC tumor cells. A significant difference was observed between the active and inactive residual groups in ∆ HU difference between CT values of arterial phase and non-contrast scans (AN, P = .000), difference between CT values of venous phase and non-contrast scans (VN, P = .000), difference between CT values of delay phase and non-contrast scans (DN, P = .000), (difference between CT values of venous and arterial phase scans, P = .001), and (difference between CT values of delay and arterial phase scans, P = .005). No statistically significant difference was observed between the delayed and venous phases (difference between CT values of delay and venous phase scans, P = .361). The area under the curve (AUC) of the ROC curve showed that the diagnostic efficacies in difference in CT value of AN (AUC = 0.976), VN (AUC = 0.927), and DN (AUC = 0.924) were higher, and their cutoff values were 4.86, 12.065, 20.19 HU with their sensitivities of 93.3%, 84.4%, 77.8% and specificities of 100%, 96.4%, and 100%, respectively. difference in CT value values of AN, VN, DN, difference between CT values of venous and arterial phase scans and difference between CT values of delay and arterial phase scans can sensitively detect residual tumor activity 20-40 days after DEB-TACE. Thus, more sensitive active residual foci were detected using all 3 enhanced phases rather than only the arterial phase. Quantitative analysis of multiphase CECT can detect residual tumor activity in an early and noninvasive manner, which can provide time for patients to receive early follow-up treatment.

PET-CT detection of local residual laryngeal carcinoma after definitive (chemo)radiotherapy.

BACKGROUND: Positron emission tomography and computed tomography (PET-CT) is currently recommended in evaluating the treatment response after (chemo)radiotherapy ([C]RT). In the larynx, post-treatment changes and physiological uptake make image interpretation more challenging compared to other head and neck sites. Previous research has not addressed imaging factors specifically in the larynx that would help in distinguishing the residual disease and explain the unique challenges of that anatomic area. The study cohorts are small and heterogenous. Our objective was to investigate the ability of PET-CT in diagnosing local residual laryngeal carcinoma, and to uncover imaging factors that could be used in differentiating the residual disease from post-treatment and physiological changes. In the same study cohort, we also aimed to uncover prognostic factors for local residual or recurrent disease. METHODS: Our retrospective study cohort included 73 patients with T2-T4 laryngeal carcinoma undergoing (C)RT with curative intention, and post-treatment non-contrast-enhanced PET-CT at 2-6 months. Findings were compared between local residual and non-residual disease. Local residual disease was defined as a persistent tumor growth with no evidence of remission in between, confirmed by biopsy, and evident within 6 months from the end of RT. PET-CT was evaluated using a 3-step scale: negative, equivocal, and positive. RESULTS: Nine (12%) had a local residual tumor and 11 (15%) developed local recurrence, based on the biopsy. The median follow-up of surviving patients was 64 months (range, 28-174). In univariate analysis, primary tumor diameter greater than 2.4 cm (median value), and vocal cord fixation were prognostic for local residual or recurrent disease. Sensitivity, specificity, PPV, and NPV were 100%, 75%, 36%, and 100%, respectively, when the equivocal interpretation was grouped with the positive interpretation. All local residuals, and 28% (18/64) non-residuals, had a primary tumor area SUVmax of over 4.0 (p < 0.001). CT showed a persistent mass at the primary tumor area in 56% of residuals, and in 23% of non-residuals (p > 0.05). By combining SUVmax>4.0 and mass, specificity improved to 91%. CONCLUSIONS: NPV of post-treatment PET-CT in laryngeal carcinoma is high, but equivocal and positive results have low PPV and require further diagnostics. All local residuals had SUVmax over 4.0. The combination of SUVmax over 4.0 and mass on CT increased specificity, but the sensitivity was low.

Deep Learning-Based Segmentation of Locally Advanced Breast Cancer on MRI in Relation to Residual Cancer Burden: A Multi-Institutional Cohort Study.

BACKGROUND: While several methods have been proposed for automated assessment of breast-cancer response to neoadjuvant chemotherapy on breast MRI, limited information is available about their performance across multiple institutions. PURPOSE: To assess the value and robustness of deep learning-derived volumes of locally advanced breast cancer (LABC) on MRI to infer the presence of residual disease after neoadjuvant chemotherapy. STUDY TYPE: Retrospective. SUBJECTS: Training cohort: 102 consecutive female patients with LABC scheduled for neoadjuvant chemotherapy (NAC) from a single institution (age: 25-73 years). Independent testing cohort: 55 consecutive female patients with LABC from four institutions (age: 25-72 years). FIELD STRENGTH/SEQUENCE: Training cohort: single vendor 1.5 T or 3.0 T. Testing cohort: multivendor 3.0 T. Gradient echo dynamic contrast-enhanced sequences. ASSESSMENT: A convolutional neural network (nnU-Net) was trained to segment LABC. Based on resulting tumor volumes, an extremely randomized tree model was trained to assess residual cancer burden (RCB)-0/I vs. RCB-II/III. An independent model was developed using functional tumor volume (FTV). Models were tested on an independent testing cohort and response assessment performance and robustness across multiple institutions were assessed. STATISTICAL TESTS: The receiver operating characteristic (ROC) was used to calculate the area under the ROC curve (AUC). DeLong's method was used to compare AUCs. Correlations were calculated using Pearson's method. P values <0.05 were considered significant. RESULTS: Automated segmentation resulted in a median (interquartile range [IQR]) Dice score of 0.87 (0.62-0.93), with similar volumetric measurements (R = 0.95, P < 0.05). Automated volumetric measurements were significantly correlated with FTV (R = 0.80). Tumor volume-derived from deep learning of DCE-MRI was associated with RCB, yielding an AUC of 0.76 to discriminate between RCB-0/I and RCB-II/III, performing similar to the FTV-based model (AUC = 0.77, P = 0.66). Performance was comparable across institutions (IQR AUC: 0.71-0.84). DATA CONCLUSION: Deep learning-based segmentation estimates changes in tumor load on DCE-MRI that are associated with RCB after NAC and is robust against variations between institutions. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 4.

Comparison of automated breast volume scanning with conventional ultrasonography, mammography, and MRI to assess residual breast cancer after neoadjuvant therapy by molecular type.

AIM: To compare the accuracy of hand-held ultrasonography (US), mammography (MG), magnetic resonance imaging (MRI), and automated breast volume scanning (ABVS) in defining residual breast cancer tumour size after neoadjuvant therapy (NAT). MATERIALS AND METHODS: Patients diagnosed breast cancer and who received NAT at the Breast Center, Peking University People's Hospital, were enrolled prospectively. Imaging was performed after the last cycle of NAT. The residual tumour size, intraclass correlation coefficients (ICCs), and receiver operating characteristic (ROC) to predict pathological complete response (pCR) were analysed. RESULTS: A total of 156 patients with 159 tumours were analysed. ABVS had a moderate correlation with histopathology residual tumour size (ICC = 0.666), and showed high agreement among triple-positive tumours (ICC = 0.797). With 5 mm as the threshold, the coincidence rate reached 64.7% between ABVS and pathological size, which was significantly higher than that between US, MG, MRI, and pathological size (50%, 45.1%, 41.4%; p=0.009, p=0.001, p<0.001, respectively). For ROC analysis, ABVS demonstrated a higher area under the ROC curve, but with no statistical difference, except for MG (0.855, 0.816, 0.819, and 0.788, respectively; p=0.183 for US, p=0.044 for MG, and p=0.397 for MRI, with ABVS as the reference). CONCLUSIONS: The longest tumour diameter on ABVS had a moderate correlation with pathological residual invasive tumour size. ABVS was shown to have good ability to predict pCR and would appear to be a potential useful tool for the assessment after NAT for breast cancer.

Accuracy of Preoperative Contrast-enhanced Cone Beam Breast CT in Assessment of Residual Tumor after Neoadjuvant Chemotherapy: A Comparative Study with Breast MRI.

RATIONALE AND OBJECTIVES: To compare the accuracy of preoperative contrast-enhanced cone beam breast CT (CE-CBBCT) and MRI in assessment of residual tumor after neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS: Residual tumor assessments in 91 female patients were performed on preoperative CE-CBBCT and MRI images after NAC. The agreements of tumor size between imaging and pathology were tested by Intraclass Correlation Coefficient (ICC). Subgroup analyses were set according to ductal carcinoma in situ (DCIS), calcifications and molecular subtypes. Correlated-samples Wilcoxon Signed-rank test was used to analyze the difference between imaging and pathology in total and subgroups. AUC, sensitivity, specificity, PPV, and NPV were calculated to compare the performance of CE-CBBCT and MRI in predicting pathological complete response (pCR). RESULTS: Comparing with pathology, the agreement on CE-CBBCT was good (ICC = 0.64, 95% CI, 0.35-0.78), whereas on MRI was moderate (ICC = 0.59, 95% CI, 0.36-0.77), and overestimation on CE-CBBCT was less than that on MRI (median (interquartile range, IQR): 0.24 [0.00, 1.31] cm vs. 0.67 [0.00, 1.81] cm; p = 0.000). In subgroup analysis, CE-CBBCT showed superior accuracy than MRI when residual DCIS (p = 0.000) and calcifications (p = 0.000) contained, as well as luminal A (p = 0.043) and luminal B (p = 0.009) breast cancer. CE-CBBCT and MRI performed comparable in predicting pCR, AUCs were 0.749 and 0.733 respectively (p > 0.05). CONCLUSION: CE-CBBCT showed superior accuracy in assessment of residual tumor compared with MRI, especially when residual DCIS or calcifications contained and luminal subtype. The performance of preoperative CE-CBBCT in predicting pCR was comparable to MRI. CE-CBBCT could be an alternative method used for preoperative assessment after NAC.

Quantitative Micro-Elastography Enables In Vivo Detection of Residual Cancer in the Surgical Cavity during Breast-Conserving Surgery.

Breast-conserving surgery (BCS) is commonly used for the treatment of early-stage breast cancer. Following BCS, approximately 20% to 30% of patients require reexcision because postoperative histopathology identifies cancer in the surgical margins of the excised specimen. Quantitative micro-elastography (QME) is an imaging technique that maps microscale tissue stiffness and has demonstrated a high diagnostic accuracy (96%) in detecting cancer in specimens excised during surgery. However, current QME methods, in common with most proposed intraoperative solutions, cannot image cancer directly in the patient, making their translation to clinical use challenging. In this proof-of-concept study, we aimed to determine whether a handheld QME probe, designed to interrogate the surgical cavity, can detect residual cancer directly in the breast cavity in vivo during BCS. In a first-in-human study, 21 BCS patients were scanned in vivo with the QME probe by five surgeons. For validation, protocols were developed to coregister in vivo QME with postoperative histopathology of the resected tissue to assess the capability of QME to identify residual cancer. In four cavity aspects presenting cancer and 21 cavity aspects presenting benign tissue, QME detected elevated stiffness in all four cancer cases, in contrast to low stiffness observed in 19 of the 21 benign cases. The results indicate that in vivo QME can identify residual cancer by directly imaging the surgical cavity, potentially providing a reliable intraoperative solution that can enable more complete cancer excision during BCS. SIGNIFICANCE: Optical imaging of microscale tissue stiffness enables the detection of residual breast cancer directly in the surgical cavity during breast-conserving surgery, which could potentially contribute to more complete cancer excision.

Accuracy of Posttreatment Imaging for Evaluation of Residual in Breast Disease After Neoadjuvant Endocrine Therapy.

BACKGROUND: Neoadjuvant endocrine therapy (NET) can help downstage certain breast cancers prior to surgical resection. This study measured the accuracy of conventional mammography (MMG), ultrasound (US), magnetic resonance imaging (MRI), and contrast-enhanced mammography (CEM) for assessing breast tumor size in response to NET. PATIENTS AND METHODS: Patients who underwent surgery after NET from 2013 to 2021 were identified. The maximal dimension of residual tumor on imaging was compared with the maximal dimension on final pathology. Lin's concordance correlation coefficient (rc) and Spearman's rank correlation coefficient (r) were used to assess agreement. RESULTS: In total, 119 patients with invasive breast cancer underwent NET, posttreatment imaging, and surgery. Tumor size reported on posttreatment CEM correlated with size on final pathology to within 1 cm in n = 42 (58%) of patients, equivalent to the accuracy of MRI (n = 35, 58%). Size was accurately predicted by US in 54% and in 48% of MMG. Posttreatment imaging tumor size was moderately correlated with final tumor size on pathology CEM (r = 0.49; rc = 0.38), MRI (r = 0.52; rc = 0.45), and US (r = 0.41; rc = 0.28). MMG was weakly correlated (r = 0.21; rc = 0.16). Similar findings were shown in subgroup analysis; in those who received all four post-NET imaging, CEM and MRI again performed comparably, with r = 0.36 and 0.41, respectively, US (r = 0.43) and MMG (r = 0.28). CONCLUSIONS: Compared with mammography and US, CEM and MRI had higher accuracy in estimating final tumor size for breast cancers treated with NET. Contrast-enhanced imaging is a helpful adjunct when response to preoperative therapy will impact clinical management.

Visual Evaluation of Ultrafast MRI in the Assessment of Residual Breast Cancer after Neoadjuvant Systemic Therapy: A Preliminary Study Association with Subtype.

The purpose of this study was to investigate the diagnostic performance of ultrafast DCE (UF-DCE) MRI after the completion of neoadjuvant systemic therapy (NST) in breast cancer. In this study, MR examinations of 55 post-NST breast cancers were retrospectively analyzed. Residual tumor sizes were measured in the 20th phase of UF-DCE MRI, early and delayed phases of conventional DCE MRI, and high spatial-resolution CE MRI (UF, early, delayed, and HR, respectively). The diagnostic performance for the detection of residual invasive cancer was calculated by ROC analysis. The size difference between MRI and pathological findings was analyzed using the Wilcoxon signed-rank test with the Bonferroni correction. The overall AUC was highest for UF (0.86 and 0.88 for readers 1 and 2, respectively). The difference in imaging and pathological sizes for UF (5.7 ± 8.2 mm) was significantly smaller than those for early, delayed, and HR (p < 0.01). For luminal subtype breast cancer, the size difference was significantly smaller for UF and early than for delayed (p < 0.01). UF-DCE MRI demonstrated higher AUC and specificity for the more accurate detection of residual cancer and the visualization of tumor extent than conventional DCE MRI.

Minimal residual disease and imaging-guided consolidation strategies in newly diagnosed and relapsed refractory multiple myeloma.

Measurement of minimal residual disease (MRD) by next-generation flow cytometry (NGF) is an important tool to define deep responses in multiple myeloma (MM). However, little is known about the value of combining NGF with functional imaging and its role for MRD-based consolidation strategies in clinical routine. In the present study, we report our experience investigating these issues with 102 patients with newly diagnosed (n = 57) and relapsed/refractory MM (n = 45). Imaging was performed using either positron emission tomography or diffusion-weighted magnetic resonance imaging. In all, 45% of patients achieved MRD-negativity on both NGF and imaging (double-negativity), and 8% and 40% of patients were negative on either NGF or imaging respectively. Thus, in a minority of patients imaging was the only technique to detect residual disease. Imaging-positivity despite negativity on NGF was more common in heavily pretreated disease (four or more previous lines) compared to newly diagnosed MM (p < 0.01). Among the 29 patients undergoing MRD-triggered consolidation, 51% responded with MRD conversion and 21% with improved serological response. MRD-triggered consolidation led to superior progression-free survival (PFS) when compared to standard treatment (p = 0.04). In conclusion, we show that combining NGF with imaging is helpful particularly in patients with heavily pretreated MM, and that MRD-based consolidation could lead to improved PFS.

Predictive value of 18F-FDG PET/CT indices on extensive residual cancer burden in breast cancer patients treated with neoadjuvant chemotherapy.

AIM: We investigated the correlation between 18F-FDG PET/CT indices and pathological response in breast cancer treated with neoadjuvant chemotherapy (NACT) which was scored with Residual Cancer Burden (RCB) system after surgery. Our aim is to detect extensive residual cancer burden earlier by using PET/CT indices. METHODS: Characteristics of patients were retrieved retrospectively. Baseline maximum Standart Uptake Value (SUVmax), Metabolic Tumor Volume (MTV) and Total Lesion Glycolysis (TLG) indices and reduction rate (RR) between baseline and interim evaluation were calculated with FDG PET/CT scan. All patients were evaluated according to RCB scores after surgery. Pathological responses and PET/CT measurement results were analyzed with demographic and clinical parameters. RESULTS: A total of 95 patients were included in the study. According to pathological responses, the distribution of RCB -0, -1, -2, -3 were 13 (13.7%), 11 (11.6%), 30 (31.6%), 41 (43.2%), respectively. Disease-free survival was significantly lower in the RCB3 group compared to the pathological responder group (p = 0.01). According to multivariate analysis, RR of SUVmax was determined as an independent variable predicting extensive residual cancer burden with an optimal cut-off value of 86% (p < 0.05). CONCLUSIONS: We determined RR of SUVmax as an independent factor for predicting extensive residual tumor burden. We believe that RR of SUVmax is sufficient to predict pathological response in daily practice. In addition, MTV and TLG measurements do not contribute additionally to SUVmax alone and can cause unnecessary labor loss.