Assessment Of Tumor Volume Research Articles

Cyclanoline Reverses Cisplatin Resistance in Bladder Cancer Cells by Inhibiting the JAK2/STAT3 Pathway.

Cisplatin is a key therapeutic agent for bladder cancer, yet the emergence of cisplatin resistance presents a significant clinical challenge. This study aims to investigate the potential and mechanisms of cyclanoline (Cyc) in overcoming cisplatin resistance. Cisplatin-resistant T24 and BIU-87 cell models (T24/DR and BIU-87/DR) were established by increasing gradual concentration. Western Blot (WB) assessed the phosphorylation of STAT3, JAK2, and JAK3. T24/DR and BIU-87/DR cell lines were treated with selective STAT3 phosphorylation modulators, and cell viability was evaluated by CCK-8. Cells were subjected to cisplatin, Cyc, or their combination. Immunofluorescence (IHC) examined p-STAT3 expression. Protein and mRNA levels of apoptosis-related and cell cycle-related factors were measured. Changes in proliferation, invasion, migration, apoptosis, and cell cycle were monitored. In vivo, subcutaneous tumor transplantation models in nude mice were established, assessing tumor volume and weight. Changes in bladder cancer tissues were observed through HE staining, and the p-STAT3 was assessed via WB and IHC. Cisplatin-resistant cell lines were successfully established, demonstrating increased phosphorylation of STAT3, JAK2, and JAK3. Cisplatin or Cyc treatment decreased p-STAT3, inhibited invasion and migration, and induced apoptosis and cell cycle arrest in the G0/G1 phase in vitro. In vivo, tumor growth was significantly suppressed, with extensive tumor cell death. IHC and WB consistently showed a substantial downregulation of STAT3 phosphorylation. These changes were more pronounced when cisplatin and Cyc were administered in combination. Cyc reverses cisplatin resistance via JAK/STAT3 inhibition in bladder cancer, offering a potential clinical strategy to enhance cisplatin efficacy in treating bladder cancer.

Convolutional Neural Networks for Segmentation of Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance).

The purpose of this study was to evaluate the impact of probability map threshold on pleural mesothelioma (PM) tumor delineations generated using a convolutional neural network (CNN). One hundred eighty-six CT scans from 48 PM patients were segmented by a VGG16/U-Net CNN. A radiologist modified the contours generated at a 0.5 probability threshold. Percent difference of tumor volume and overlap using the Dice Similarity Coefficient (DSC) were compared between the reference standard provided by the radiologist and CNN outputs for thresholds ranging from 0.001 to 0.9. CNN-derived contours consistently yielded smaller tumor volumes than radiologist contours. Reducing the probability threshold from 0.5 to 0.01 decreased the absolute percent volume difference, on average, from 42.93% to 26.60%. Median and mean DSC ranged from 0.57 to 0.59, with a peak at a threshold of 0.2; no distinct threshold was found for percent volume difference. The CNN exhibited deficiencies with specific disease presentations, such as severe pleural effusion or disease in the pleural fissure. No single output threshold in the CNN probability maps was optimal for both tumor volume and DSC. This study emphasized the importance of considering both figures of merit when evaluating deep learning-based tumor segmentations across probability thresholds. This work underscores the need to simultaneously assess tumor volume and spatial overlap when evaluating CNN performance. While automated segmentations may yield comparable tumor volumes to that of the reference standard, the spatial region delineated by the CNN at a specific threshold is equally important.

Revisiting oligodendroglioma grading in the 2021 WHO classification: calcification and larger contrast-enhancing tumor volume may predict higher oligodendroglioma grade.

To investigate whether qualitative and quantitative imaging phenotypes can predict the grade of oligodendroglioma. Retrospective chart and imaging reviews were conducted on 180 adults with oligodendroglioma (IDH-mutant and 1p/19q codeleted) between 2005 and 2021. Qualitative imaging characteristics including tumor location, calcification, gliomatosis cerebri, cystic change, necrosis, and infiltrative pattern were analyzed. Quantitative imaging assessment was performed from the tumor mask via automatic segmentation to calculate total, contrast-enhancing (CE), non-enhancing (NE), and necrotic tumor volumes. Logistic analyses were conducted to determine predictors of oligodendroglioma grade. This study included 180 patients (84 [46.7%] with grade 2 and 96 [53.3%] with grade 3 oligodendrogliomas), with a median age of 42 years (range 23-76 years), comprising 91 females and 89 males. On univariable analysis, calcification (odds ratio [OR] = 6.00, P < 0.001), necrosis (OR = 21.84, P = 0.003), presence of CE tumor (OR = 7.86, P < 0.001), larger total (OR = 1.01, P < 0.001), larger CE (OR = 2.22, P = 0.010), and larger NE (OR = 1.01, P < 0.001) tumor volumes were predictors of grade 3 oligodendroglioma. On multivariable analysis, calcification (OR = 3.79, P < 0.001) and larger CE tumor volume (OR = 2.70, P = 0.043) remained as independent predictors of grade 3 oligodendroglioma. The multivariable model exhibited an AUC, accuracy, sensitivity, specificity of 0.78 (95% confidence interval 0.72-0.84), 72.8%, 79.2%, 69.1%, respectively. Presence of calcification and larger CE tumor volume may serve as useful imaging biomarkers for prediction of oligodendroglioma grade. Assessment of intratumoral calcification and CE tumor volume may facilitate accurate preoperative estimation of oligodendroglioma grade. Presence of intratumoral calcification and larger contrast-enhancing tumor volume were the significant predictors of higher grade oligodendroglioma based on the 2021 WHO classification.

Radical prostatectomy cancer grade and percentage of Gleason pattern 4 estimated by global vs individual tumor grading correlate differently with the risk of biochemical recurrence in Grade Group 2 and 3 cancers.

There are 2 grading approaches to radical prostatectomy (RP) in multifocal cancer: Grade Group (GG) and percentage of Gleason pattern 4 (GP4%). We investigated whether RP GG and GP4% generated by global vs individual tumor grading correlate differently with biochemical recurrence. We reviewed 531 RP specimens with GG2 or GG3 cancer. Each tumor was scored separately with assessment of tumor volume and GP4%. Global grade and GP4% were assigned by combining Gleason pattern 3 and 4 volumes for all tumors. Correlation of GG and GP4% generated by 2 methods with biochemical recurrence was assessed by Cox proportional hazard regression and receiver operating characteristic curves, with optimism adjustment using a bootstrap analysis. Median age was 63 (range, 42-79) years. Median prostate-specific antigen was 6.3 (range, 0.3-62.9) ng/mL. In total, the highest-grade tumor in 371 (36.9%) men was GG2 and in 160 (30.1%) men was GG3. Global grading was downgraded from GG3 to GG2 in 37 of 121 (30.6%) specimens with multifocal disease, and 145 of 404 (35.9%) specimens had GP4% decreased by at least 10%. Ninety-eight men experienced biochemical recurrence within a median of 13 (range, 3-119) months. Men without biochemical recurrence were followed up for a median of 47 (range, 12-205) months. Grade Group, GP4%, and margin status correlated with the risk of biochemical recurrence using highest-grade tumor and global grading, but the degrees of these correlations varied and were statistically significantly different between the 2 grading approaches. Grade Group, GP4%, and margin status derived by global vs individual tumor grading predict postoperative biochemical recurrence statistically significantly differently. This difference has important implications if results derived from cohorts graded using different methods are compared.

A reduction in tumor volume exceeding 65% predicts a good histological response to neoadjuvant chemotherapy in patients with Ewing sarcoma.

No consensus exists for tumor volume response criteria in patients with Ewing sarcoma. This study aimed to identify an optimal cutoff for predicting a good histological response by analyzing tumor volume changes and tumor necrosis after neoadjuvant chemotherapy. We performed a retrospective analysis of 184 Ewing sarcoma patients, analyzing tumor volume changes before and after neoadjuvant chemotherapy. Patients were divided into two groups based on histological response: good (tumor necrosis ≥ 95%) and poor (tumor necrosis < 95%) responders. The receiver operating characteristic (ROC) area under the curve (AUC) method was used to determine the optimal thresholds for predicting the histological response. Additionally, the prognostic value of this cutoff for relapse-free survival was assessed. Out of 184 patients, 83 (45%) had tumor necrosis ≥ 95%, while 101 (55%) had tumor necrosis < 95%. ROC analysis identified the optimal cutoff for a good histological response as over 65% tumor volume reduction (AUC = 0.69; p < 0.001). Patients with volume reduction of ≥ 65% had a higher likelihood of a good histological response than those with lesser reductions (p = 0.004; odds ratio = 2.61). Multivariable analysis indicated a correlation between poor histological response and reduced relapse-free survival (hazard ratio = 2.17; p = 0.01), while tumor volume reduction itself did not impact survival. We reported that a tumor volume reduction of ≥ 65% was able to predict a good histological response in Ewing sarcoma patients. We recommend preoperative tumor volume assessment to identify patients at greater risk for poor histological response who could benefit from more intensive chemotherapy protocols or additional radiotherapy.

Abstract 4230: Patient-derived tumoroid platforms with progressing tumor microenvironment representability reveals efficacy and resistance patterns in pre-clinical HSP90i regimen testing for improved clinical insights

Abstract Introduction: Drug candidates advance to phase I clinical trial after rigorous optimization at preclinical stage and yet nine out of ten drug candidates fail during clinical trials and approval stage. Lack of clinical efficacy is a major contributor (~ 50%) towards this 90% of the failure at clinical trials. Tremendous efforts are being taken to demonstrate drug efficacy in preclinical studies by mitigating the biological discrepancy of molecular target between human disease and the model used for the validation. Herein this study, we aim to showcase therapeutic responses of heat-shock protein 90 (HSP90) inhibitor regimens (HSP90i only and HSP90i + Doxorubicin combination) against three-dimensional (3D), patient-derived tumoroid (PDT) model platforms with progressing tumor microenvironment (TME) representability by incorporating supplementary TME structure, immunity, and oxygenation. Revelations on efficacy and resistance patterns of such drug treatments on increasingly complex models is aimed at providing unique insights to tumor and drug-drug interplay. Methods: Human Uterine Adenosarcoma PDT models were prepared across three COMPASS (Custom Organoid Modeling Platform for Accurate SolutionS) platforms: 1) COMPASS 1a - Non-scaffold 2) COMPASS 3b - Scaffold + Immune cells and 3) COMPASS 3d - Scaffold + Immune cells + Oxygenation. The various platforms were tested against two regimens A) HSP90i only [50 nM and 100 nM] and B) HSP90i [50 nM and 100 nM] + Doxorubicin [50 nM]. Tumor size and volume assessment were carried out on Days 3 and 7 post-treatment -with statistical done via one-way ANOVA, followed by student’s t test for significance (p&amp;lt;0.05, n=3). Results: While a simpler platform COMPASS 1a showcased the therapeutic efficacy of monotherapeutic HSP90i [at 100 nM] (p&amp;lt;0.05), progressively complex models (COMPASS 3b and COMPASS 3d) have otherwise indicated the lack of significant tumor volume changes. On the other hand, current findings on combination therapies of HSP90i and Doxorubicin have revealed persistent therapeutic resistance (even suggestive of patterns of increasing tumor growth) on COMPASS 1a, necessitating further comparisons on the more complex models. Conclusion: Our results demonstrate the need to test drugs in context of TME and oxygenation to help stratify patients to develop better clinical trials based on patient tumor characteristics of vascularization and immunogenicity. We plan to test several different drug modalities and cancer types for these parameters to demonstrate the need and effectiveness of testing prior to taking the drugs to the clinic. Citation Format: Huckie Del Mundo, Damieanus O. Ochola, Bhuvanesh Dave, Preethi Samuel. Patient-derived tumoroid platforms with progressing tumor microenvironment representability reveals efficacy and resistance patterns in pre-clinical HSP90i regimen testing for improved clinical insights [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4230.

Monitoring of neoadjuvant chemotherapy response of breast cancer with ultrasound localization microscopy

Neoadjuvant chemotherapy (NAC) is the standard treatment for high-risk breast cancer, but less than 30% of patients exhibit a complete response, necessitating better preselection and monitoring. Ultrasound localization microscopy (ULM) offers potential improvements by providing super-resolution images of vasculature and functional features. To assess its efficacy, we applied ULM to breast cancer patients undergoing NAC, comparing their responses based on ULM features. Patients were evaluated before their first, second, and fourth NAC cycles, assessing tumor volume and employing contrast-enhanced ultrasound (CEUS) in split-screen mode. CEUS videos underwent postprocessing, involving MB localization and tracking, and motion compensation. ULM images, derived from tracked MBs, generated parameter maps for analyzing vascular changes and differences between responders and non-responders. Preliminary results of 14 patients revealed that during the first three cycles of NAC, the tumor volume as well as vessel coverage (i.e., ratio of vessel to tumor surface) of responders noticeably dropped. In contrast, non-responders showed only minor tumor size reduction and overall no decrease in coverage. Furthermore, the mean distance to the closest track was noticeably higher in non-responders. These findings, though preliminary, hint at ULM's potential for enhancing preselection and monitoring of NAC patients.

Response evaluation criteria in PSMA PET/CT (RECIP 1.0) in metastatic castration-resistant prostate cancer.

45 Background: Response Evaluation Criteria in Prostate-Specific Membrane Antigen (PSMA) PET/CT (RECIP 1.0) initially integrated software-based quantitative assessment of PSMA-positive total tumor volume (PSMA-VOL). Clinical implementation of such software is not expected soon, limiting the use of RECIP in practice. This study aimed to assess the agreement of RECIP determined using tumor segmentation software (quantitative RECIP) with RECIP determined by qualitative reads by nuclear medicine physicians (visual RECIP) for response evaluation in metastatic castration-resistant prostate cancer. Methods: This multicenter retrospective study at three academic centers included patients who received lutetium-177 (177Lu) PSMA treatment between December 2014 and July 2019. PSMA PET/CT images at baseline and 12 weeks were assessed qualitatively by five nuclear medicine physicians for changes in PSMA-VOL and for new lesions. Quantitative changes in PSMA-VOL were also measured using tumor segmentation software. The status of new lesions was combined with qualitative changes in PSMA-VOL to determine visual RECIP and with quantitative changes in PSMA-VOL to determine quantitative RECIP. The primary outcomes were the agreement between visual and quantitative RECIP and the interreader reliability of visual RECIP according to the Fleiss κ. The secondary outcome was the association of visual RECIP with overall survival according to Cox regression. Results: A total of 124 patients (median age, 73 years [IQR, 67–76 years]) were included. Forty (32%) and 84 (68%) patients had quantitative RECIP progressive disease (PD) and non-PD, respectively. Agreement between visual versus quantitative RECIP was excellent (κ=0.89; 118 of 124 patients [95%]). Agreement among readers in classifying visual RECIP PD versus non-PD was excellent (κ=0.81; 103 of 124 patients [83%]). RECIP-PD was associated with significantly shorter overall survival compared with RECIP-nonPD (hazard ratio, 2.6 [95%CI: 1.7, 3.8]; P &lt;.001). Conclusions: Qualitatively assessed RECIP demonstrated excellent agreement with quantitative RECIP and excellent interreader reliability, and can be readily implemented in clinical practice for response evaluation in patients with metastatic castration-resistant prostate cancer undergoing 177Lu-PSMA therapy and possible other systemic therapies.

Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases

BackgroundWe developed models for tumor segmentation to automate the assessment of total tumor volume (TTV) in patients with colorectal liver metastases (CRLM).MethodsIn this prospective cohort study, pre- and post-systemic treatment computed tomography (CT) scans of 259 patients with initially unresectable CRLM of the CAIRO5 trial (NCT02162563) were included. In total, 595 CT scans comprising 8,959 CRLM were divided into training (73%), validation (6.5%), and test sets (21%). Deep learning models were trained with ground truth segmentations of the liver and CRLM. TTV was calculated based on the CRLM segmentations. An external validation cohort was included, comprising 72 preoperative CT scans of patients with 112 resectable CRLM. Image segmentation evaluation metrics and intraclass correlation coefficient (ICC) were calculated.ResultsIn the test set (122 CT scans), the autosegmentation models showed a global Dice similarity coefficient (DSC) of 0.96 (liver) and 0.86 (CRLM). The corresponding median per-case DSC was 0.96 (interquartile range [IQR] 0.95–0.96) and 0.80 (IQR 0.67–0.87). For tumor segmentation, the intersection-over-union, precision, and recall were 0.75, 0.89, and 0.84, respectively. An excellent agreement was observed between the reference and automatically computed TTV for the test set (ICC 0.98) and external validation cohort (ICC 0.98). In the external validation, the global DSC was 0.82 and the median per-case DSC was 0.60 (IQR 0.29–0.76) for tumor segmentation.ConclusionsDeep learning autosegmentation models were able to segment the liver and CRLM automatically and accurately in patients with initially unresectable CRLM, enabling automatic TTV assessment in such patients.Relevance statementAutomatic segmentation enables the assessment of total tumor volume in patients with colorectal liver metastases, with a high potential of decreasing radiologist’s workload and increasing accuracy and consistency.Key points• Tumor response evaluation is time-consuming, manually performed, and ignores total tumor volume.• Automatic models can accurately segment tumors in patients with colorectal liver metastases.• Total tumor volume can be accurately calculated based on automatic segmentations.Graphical

SURG-41. PROGNOSTIC IMPACT OF EXTENDED RESECTIONS IN IDH MUTANT LOWER-GRADE DIFFUSE GLIOMA: RESULTS FROM A VOLUMETRIC ANALYSIS

Abstract In IDH mutant (IDHmut) glioma, the prognostic impact of extended resections is still under debate. A potential outcome benefit may be influenced by the tumor’s molecular phenotype. Also, many studies lack objective quantitative assessment of residual tumor volume (RTV). Here, we analyzed the prognostic benefit of surgery in a molecularly defined cohort of patients harboring an astrocytoma or oligodendroglioma, IDHmut, WHO grade 2 and 3, for whom volumetric analysis of RTV was possible. Patient-, tumor- and treatment-related factors including pre- and post-operative MR FLAIR tumor volumes were correlated with progression-free (PFS) and overall (OS) survival. Confounders of resectability were analyzed by multiple linear regression. 206 patients treated at our institution over one decade were identified retrospectively. Mean age was 40.37 years. 105 patients suffered from IDHmut astrocytoma and 101 patients from oligodendroglioma. Resection (n = 185 patients; 89.8%) as opposed to biopsy was associated with prolonged PFS and OS in astrocytoma (p = 0.007; p = 0.038) and WHO grade 2 tumors (p = 0.012; p &amp;lt; 0.0001). In patients undergoing resection, median pre- and post-operative tumor volumes did not differ between tumor subtypes but were significantly larger in WHO grade 3 than 2 tumors (pre-operative: 71.3 vs. 28.8 cm3; RTV: 7.78 vs. 1.98 cm3). Median RTV of the complete cohort was 4.6 cm3. There was a stepwise improvement of outcome with decreasing RTV (PFS: p = 0.0003; OS: p = 0.0004; data given for quartiles), with the strongest effect observed for patients with no RTV (median PFS: undefined vs. 58 months; p = 0.007). In multivariable analysis, RTV independently predicted OS (p = 0.001; HR 1.012) but not PFS. Resectability was only influenced by pre-operative FLAIR tumor volume (p &amp;lt; 0.0001). In summary, a continuous association of RTV and outcome was observed, even at the highest levels of resection. This finding was more pronounced in astrocytoma. Thus, a maximal resection should be strived for whenever safely achievable.

Computer-aided diagnosis of skeletal metastases in multi-parametric whole-body MRI

The confident detection of metastatic bone disease is essential to improve patients' comfort and increase life expectancy. Multi-parametric magnetic resonance imaging (MRI) has been successfully used for monitoring of metastatic bone disease, allowing for comprehensive and holistic evaluation of the total tumour volume and treatment response assessment. The major challenges of radiological reading of whole-body MRI come from the amount of data to be reviewed and the scattered distribution of metastases, often of complex shapes. This makes bone lesion detection and quantification demanding for a radiologist and prone to error. Additionally, whole-body MRI are often corrupted with multiple spatial and intensity distortions, which further degrade the performance of image reading and image processing algorithms. In this work we propose a fully automated computer-aided diagnosis system for the detection and segmentation of metastatic bone disease using whole-body multi-parametric MRI. The system consists of an extensive image preprocessing pipeline aiming at enhancing the image quality, followed by a deep learning framework for detection and segmentation of metastatic bone disease. The system outperformed state-of-the-art methodologies, achieving a detection sensitivity of 63% with a mean of 6.44 false positives per image, and an average lesion Dice coefficient of 0.53. A provided ablation study performed to investigate the relative importance of image preprocessing shows that introduction of region of interest mask and spatial registration have a significant impact on detection and segmentation performance in whole-body MRI. The proposed computer-aided diagnosis system allows for automatic quantification of disease infiltration and could provide a valuable tool during radiological examination of whole-body MRI.

Improved early outcome prediction by MRI-based 3D tumor volume assessment in patients with CNS lymphomas.

Central nervous system lymphomas (CNSL) display remarkable clinical heterogeneity, yet accurate prediction of outcomes remains challenging. The IPCG criteria are widely used in routine practice for the assessment of treatment response. However, the value of the IPCG criteria for ultimate outcome prediction is largely unclear, mainly due to the uncertainty in delineating complete from partial responses during and after treatment. We explored various MRI features including semi-automated 3D tumor volume measurements at different disease milestones and their association with survival in 93 CNSL patients undergoing curative-intent treatment. At diagnosis, patients with more than 3 lymphoma lesions, periventricular involvement, and high 3D tumor volumes showed significantly unfavorable PFS and OS. At first interim MRI during treatment, the IPCG criteria failed to discriminate outcomes in responding patients. Therefore, we randomized these patients into training and validation cohorts to investigate whether 3D tumor volumetry could improve outcome prediction. We identified a 3D tumor volume reduction of ≥97% as the optimal threshold for risk stratification (=3D early response, 3D_ER). Applied to the validation cohort, patients achieving 3D_ER had significantly superior outcomes. In multivariate analyses, 3D_ER was independently prognostic of PFS and OS. Finally, we leveraged prognostic information from 3D MRI features and circulating biomarkers to build a composite metric that further improved outcome prediction in CNSL. We developed semi-automated 3D tumor volume measurements as strong and independent early predictors of clinical outcomes in CNSL patients. These radiologic features could help improve risk stratification and help guide future treatment approaches.

P14.04.B CIRCULATING TUMOR CELLS AS A NON-INVASIVE GLIOBLASTOMA DIAGNOSTIC AND PROGNOSTIC TOOL

Abstract BACKGROUND Circulating tumor cells (CTCs) detection in the blood of glioblastoma (GBM) patients can facilitate the assessment of tumoral information without the need for invasive approaches. Our aim is to use GBM CTCs to obtain knowledge about the tumor and improve the current GBM diagnosis and prognosis. MATERIAL AND METHODS We collected primary, matched recurrent GBM and healthy controls blood samples. CTCs were enriched from whole blood controls with Parsortix Cell Separation System and analysed on DEPArray system. CTCs were recognized by immunofluorescence with GFAP.The CTCs have been counted for all the samples. Copy Number Aberrations (CNAs) analysis was performed on CTCs. To compare CTCs genetic background with the same patient’s primary and recurrence tissues, whole exome sequencing (WES) was realized on tumor tissues. Recurrence of GBM was diagnosed according to the RANO criteria +/-path proven second surgery +/- FET PET imaging proven. We assessed tumor volume of contrast enhanced (CE) (as a surrogate marker of BBB damage) and nCE at the time of the CTCs collection by seminautomatic segmentation on ITKsnap software. RESULTS We found CTCs in GBM patients newly diagnosed at the time of first surgery (86%) but also at recurrence (100 %). Cell counting demonstrated in both primary and recurrent GBM patients’ blood a statistically significantly higher number of CTCs compared to healthy controls (average of cells number 15.8 vs 2.3). Interestingly, recurrent GBM patients showed more CTCs than primary GBM patients (average 9 vs 22.7). CNAs analysis confirmed the tumor status of the selected CTCs. Some of GBM classical mutations (in HTERT promoter, BRAF, PTEN, PI3KCA and EGFR genes) have been searched both in the tumor tissues and in the CTCs. HTERT was found mutated in CTCs of one patient’s tumor carrying mutant HTERT and wild type IDH1/2. We also included three IDH1 mutant glioma patients and one BRAFV600E mutant GBM patient in order to track these in CTCs. We did not find BRAF mutations in CTCs isolated from one recurrent BRAFV600E GBM patient with a very low allele frequency in the recurrent tumor (&amp;lt;1%). CONCLUSION We found CTCs in GBM patients newly diagnosed and at recurrence. Several factors may affect CTCs release such as tumor volume, CE and the CTCs genetic BACKGROUND , which seems to be heterogeneous. CTCs detection by our methodology could be helpful in the clinical setting for diagnostic, prognostic and theranostic purposes and for patients monitoring. Prospective study is ongoing in order to assess CTCs variations on and after therapy. This study, first investigated the genetic heterogeneity of CTCs released in the blood flow from the parental tumor.

Evaluation of the therapeutic efficacy of 213Bi-labelled DOTA-conjugated alpha-melanocyte stimulating hormone peptide analogues in melanocortin-1 receptor positive preclinical melanoma model

Melanocortin-1 receptor (MC1-R) targeting alpha-melanocyte stimulating hormone-analogue (α-MSH) biomolecules labelled with α-emitting radiometal seem to be valuable in the targeted radionuclide therapy of MC1-R positive melanoma malignum (MM). Herein is reported the anti-tumor in vivo therapeutic evaluation of MC1-R-affine [213Bi]Bi-DOTA-NAPamide and HOLDamide treatment in MC1-R positive B16-F10 melanoma tumor-bearing C57BL/6J mice. On the 6th, 8th and 10th days post tumor cell inoculation; the treated groups of mice were intravenously injected with approximately 5 MBq of both amide derivatives. Beyond body weight and tumor volume assessment, [68Ga]Ga-DOTA-HOLDamide and NAPamide-based PET/MRI scans, and ex vivo biodistribution studies were executed 30,- and 90 min postinjection. In the PET/MRI imaging studies the B16-F10 tumors were clearly visualized with both 68Ga-labelled tracers, however, significantly lower tumor-to-muscle (T/M) ratios were observed by using [68Ga]Ga-DOTA-HOLDamide. After alpha-radiotherapy treatment the tumor size of the control group was larger relative to both treated cohorts, while the smallest tumor volumes were observed in the NAPamide-treated subclass on the 10th day. Relatively higher [213Bi]Bi-DOTA-NAPamide accumulation in the B16-F10 tumors (%ID/g: 2.71 ± 0.15) with discrete background activity led to excellent T/M ratios, particularly 90 min postinjection. Overall, the therapeutic application of receptor selective [213Bi]Bi-DOTA-NAPamide seems to be feasible in MC1-R positive MM management.

VOLUMETRIC ANALYSIS OF RENAL MASSES AS PREDICTORS OF PARTIAL NEPHRECTOMY OUTCOMES.

Objective To examine the role of endophytic tumor volume assessment (endophycity) on perioperative partial nephrectomy outcomes. Patients and Methods Retrospective review of 212 consecutive laparoscopic and open partial nephrectomies from single institution using pre-operative imaging and one-year follow-up. Demographics, comorbidities, RENAL Nephrometry scores, and all peri- and post-operative outcomes were recorded. Volumetric analysis performed using imaging software, independently assessed by two, blinded radiologists. Univariate and multivariate statistical analysis were completed to assess predictive value of endophycity for all clinically meaningful outcomes. Results Among those undergoing minimally invasive surgery (MIS), lower tumor endophycity was associated with higher likelihood of trifecta outcome (negative surgical margin, <10% decline in eGFR, the absence of complications) irrespective of max tumor size. For MIS, estimated blood loss (EBL) increased with greater tumor endophycity regardless of tumor size. Among those who underwent open partial nephrectomy, lower tumor endophycity was associated with trifecta outcomes for tumors >4cm only. On multivariate analysis with log-scaled odds ratios (OR), tumor endophycity and total kidney volume had the strongest correlation with tumor-related complications (OR=3.23, 2.66). The analysis identified that tumor endophycity and tumor volume on imaging were inversely correlated with of trifecta outcomes (OR=0.53 for both covariates). Conclusions Volumetric assessment of tumor endophycity performed well in identifying partial nephrectomy outcomes. As automated imaging software improves, volumetric analysis may prove to be a useful adjunct in pre-operative planning and patient counseling.

Oral vinorelbine in young patients with desmoid-type fibromatosis.

Desmoid-type fibromatosis are rare intermediate tumors in children and adolescents. Owing to local aggressiveness and relapse, systemic treatment for symptomatic advanced or progressive forms is recommended. Following promising results in adult patients, oral vinorelbine is investigated in young patients. A retrospective review of young patients (<25 years old) with advanced or progressive desmoid type fibromatosis treated with oral vinorelbine in eight large centers of the Société Française des Cancers de l'Enfant was performed. In addition to tumor assessment according to RECIST 1.1, pre-treatment and during-treatment imagery were reviewed centrally to assess tumor volume and estimate fibrosis score through the change in percentage in hypoT2 signal intensity. From 2005 to 2020, 24 patients (median age 13.9 years [range, 1.0-23.0]) received oral vinorelbine. Median number of prior systemic lines of treatment was 1 (range, 0-2), mainly based on intravenous low dose methotrexate and vinblastine. Before vinorelbine initiation, all patients had a progressive disease: radiological for 19, radiological and clinical (pain) for three and only clinical for two. Oral vinorelbine was delivered for a median duration of 12 months (range, 1-42). The toxicity profile was favorable, with no grade 3-4 event. Overall response estimated on 23 evaluable patients according to RECIST 1.1 criteria was three partial responses (13%), 18 stabilization (78%) and two progressive disease (9%). Overall progression-free survival was 89.3% (95% confidential intervals 75.2-100) at 24 months. Four stable tumors according to standard RECIST criteria displayed a partial response with > 65% tumor volume reduction. Among 21 informative patients, the estimated fibrosis score decreased for 15 patients, was stable for four patients and increased for two patients. Oral vinorelbine seems to be effective to control advanced or progressive desmoid type fibromatosis in young patients, with a well-tolerated profile. These results support testing this drug as first-line alone or in combination to improve response rate while preserving quality of life.

Counteracting gemcitabine+nab-paclitaxel induced dysbiosis in KRAS wild type and KRASG12D mutated pancreatic cancer in vivo model

Pancreatic cancer (PC) has a very low survival rate mainly due to late diagnosis and refractoriness to therapies. The latter also cause adverse effects negatively affecting the patients’ quality of life, often requiring dose reduction or discontinuation of scheduled treatments, compromising the chances of cure. We explored the effects of a specific probiotic blend on PC mice xenografted with KRAS wild-type or KRASG12D mutated cell lines alone or together with gemcitabine+nab-paclitaxel treatment to then assess tumor volume and clinical pathological variables. Beside a semi-quantitative histopathological evaluation of murine tumor and large intestine samples, histochemical and immunohistochemical analyses were carried out to evaluate collagen deposition, proliferation index Ki67, immunological microenvironment tumor-associated, DNA damage markers and also mucin production. Blood cellular and biochemical parameters and serum metabolomics were further analyzed. 16S sequencing was performed to analyze the composition of fecal microbiota. Gemcitabine+nab-paclitaxel treatment impaired gut microbial profile in KRAS wild-type and KRASG12D mice. Counteracting gemcitabine+nab-paclitaxel- induced dysbiosis through the administration of probiotics ameliorated chemotherapy side effects and decreased cancer-associated stromatogenesis. Milder intestinal damage and improved blood count were also observed upon probiotics treatment as well as a positive effect on fecal microbiota, yielding an increase in species richness and in short chain fatty acids producing- bacteria. Mice’ serum metabolomic profiles revealed significant drops in many amino acids upon probiotics administration in KRAS wild-type mice while in animals transplanted with PANC-1 KRASG12D mutated all treated groups showed a sharp decline in serum levels of bile acids with respect to control mice. These results suggest that counteracting gemcitabine+nab-paclitaxel-induced dysbiosis ameliorates chemotherapy side effects by restoring a favorable microbiota composition. Relieving adverse effects of the chemotherapy through microbiota manipulation could be a desirable strategy in order to improve pancreatic cancer patients’ quality of life and to increase the chance of cure.

Breast Cancer Growth on Serial MRI: Volume Doubling Time Based on 3-Dimensional Tumor Volume Assessment.

The volume doubling time (VDT) of breast cancer was most frequently calculated using the two-dimensional (2D) diameter, which is not reliable for irregular tumors. It was rarely investigated using three-dimensional (3D) imaging with tumor volume on serial magnetic resonance imaging (MRI). To investigate the VDT of breast cancer using 3D tumor volume assessment on serial breast MRIs. Retrospective. Sixty women (age at diagnosis: 57 ± 10 years) with breast cancer, assessed by two or more breast MRI examinations. The median interval time was 791 days (range: 70-3654 days). 3-T, fast spin-echo T2-weighted imaging (T2WI), single-shot echo-planar diffusion-weighted imaging (DWI), and gradient echo dynamic contrast-enhanced imaging. Three radiologists independently reviewed the morphological, DWI, and T2WI features of lesions. The whole tumor was segmented to measure the volume on contrast-enhanced images. The exponential growth model was fitted in the 11 patients with at least three MRI examinations. The VDT of breast cancer was calculated using the modified Schwartz equation. Mann-Whitney U test, Kruskal-Wallis test, Chi-squared test, intraclass correlation coefficients, and Fleiss kappa coefficients. A P-value <0.05 was considered statistically significant. The exponential growth model was evaluated using the adjusted R2 and root mean square error (RMSE). The median tumor diameter was 9.7 mm and 15.2 mm on the initial and final MRI, respectively. The median adjusted R2 and RMSE of the 11 exponential models were 0.97 and 15.8, respectively. The median VDT was 540 days (range: 68-2424 days). For invasive ductal carcinoma (N=33), the median VDT of the non-luminal type was shorter than that of the luminal type (178 days vs. 478 days). On initial MRI, breast cancer manifesting as a focus or mass lesion showed a shorter VDT than that of a non-mass enhancement (NME) lesion (median VDT: 426 days vs. 665 days). A shorter VDT was observed in breast cancer manifesting as focus or mass as compared to an NME lesion. 3 TECHNICAL EFFICACY: Stage 2.

Potential benefits of intraoperative ultrasound in neurosurgery.

Computed tomography and magnetic resonance imaging are the most popular diagnostic tools to visualize intracranial pathology and help surgical planning in the neurosurgical practice. However, these preoperative techniques need to be supplemented with intraoperative methods, if the lesion size may increase or preoperative anatomy may change after the primary imaging due to rapid progression of the underlying intracranial disease. In such situations intraoperative ultrasound could be a valuable technique in real-time imaging of intracranial pathologic processes, real-time control of surgical procedure, assistance in drain or catheter placement, and real-time assessment of residual tumor or hematoma volume during neurosurgical interventions.

Stacked Gated Recurrent Unit Classifier with CT Images for Liver Cancer Classification

Liver cancer is one of the major diseases with increased mortality in recent years, across the globe. Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis (CAD) models have been developed to detect the presence of liver cancer accurately and classify its stages. Besides, liver cancer segmentation outcome, using medical images, is employed in the assessment of tumor volume, further treatment plans, and response monitoring. Hence, there is a need exists to develop automated tools for liver cancer detection in a precise manner. With this motivation, the current study introduces an Intelligent Artificial Intelligence with Equilibrium Optimizer based Liver cancer Classification (IAIEO-LCC) model. The proposed IAIEO-LCC technique initially performs Median Filtering (MF)-based pre-processing and data augmentation process. Besides, Kapur’s entropy-based segmentation technique is used to identify the affected regions in liver. Moreover, VGG-19 based feature extractor and Equilibrium Optimizer (EO)-based hyperparameter tuning processes are also involved to derive the feature vectors. At last, Stacked Gated Recurrent Unit (SGRU) classifier is exploited to detect and classify the liver cancer effectively. In order to demonstrate the superiority of the proposed IAIEO-LCC technique in terms of performance, a wide range of simulations was conducted and the results were inspected under different measures. The comparison study results infer that the proposed IAIEO-LCC technique achieved an improved accuracy of 98.52%.