Circulating tumor DNA and radiological tumor volume identify patients at risk for relapse with resected, early-stage non-small-cell lung cancer.

BACKGROUND: Predicting relapse and overall survival (OS) in early-stage non-small-cell lung cancer (NSCLC) patients remains challenging. Therefore, we hypothesized that detection of circulating tumor DNA (ctDNA) can identify patients with increased risk of relapse and that integrating radiological tumor volume measurement along with ctDNA detectability improves prediction of outcome. PATIENTS AND METHODS: We analyzed 366 serial plasma samples from 85 patients who underwent surgical resections and assessed ctDNA using a next-generation sequencing liquid biopsy assay, and measured tumor volume using a computed tomography-based three-dimensional annotation. RESULTS: Our results showed that patients with detectable ctDNA at baseline or after treatment and patients who did not clear ctDNA after treatment had a significantly worse clinical outcome. Integrating radiological analysis allowed the stratification in risk groups prognostic of clinical outcome as confirmed in an independent cohort of 32 patients. CONCLUSIONS: Our findings suggest ctDNA and radiological monitoring could be valuable tools for guiding follow-up care and treatment decisions for early-stage NSCLC patients.

Metabolic tumor volume predicts outcome in patients with advanced stage follicular lymphoma from the RELEVANCE trial.

BACKGROUND: We investigated the prognostic value of baseline positron emission tomography (PET) parameters for patients with treatment-naïve follicular lymphoma (FL) in the phase III RELEVANCE trial, comparing the immunomodulatory combination of lenalidomide and rituximab (R2) versus R-chemotherapy (R-chemo), with both regimens followed by R maintenance therapy. PATIENTS AND METHODS: Baseline characteristics of the entire PET-evaluable population (n = 406/1032) were well balanced between treatment arms. The maximal standard uptake value (SUVmax) and the standardized maximal distance between tow lesions (SDmax) were extracted, the standardized distance between two lesions the furthest apart, were extracted. The total metabolic tumor volume (TMTV) was computed using the 41% SUVmax method. RESULTS: With a median follow-up of 6.5 years, the 6-year progression-free survival (PFS) was 57.8%, the median TMTV was 284 cm3, SUVmax was 11.3 and SDmax was 0.32 m-1, with no significant difference between arms. High TMTV (>510 cm3) and FLIPI were associated with an inferior PFS (P = 0.013 and P = 0.006, respectively), whereas SUVmax and SDmax were not (P = 0.08 and P = 0.12, respectively). In multivariable analysis, follicular lymphoma international prognostic index (FLIPI) and TMTV remained significantly associated with PFS (P = 0.0119 and P = 0.0379, respectively). These two adverse factors combined stratified the overall population into three risk groups: patients with no risk factors (40%), with one factor (44%), or with both (16%), with a 6-year PFS of 67.7%, 54.5%, and 41.0%, respectively. No significant interaction between treatment arms and TMTV or FLIPI (P = 0.31 or P = 0.59, respectively) was observed. The high-risk group (high TMTV and FLIPI 3-5) had a similar PFS in both arms (P = 0.45) with a median PFS of 68.4% in the R-chemo arm versus 71.4% in the R2 arm. CONCLUSIONS: Baseline TMTV is predictive of PFS, independently of FLIPI, in patients with advanced FL even in the context of antibody maintenance.

Differentially expressed genes associated with high metabolic tumor volume served as diagnostic markers and potential therapeutic targets for pancreatic cancer.

BACKGROUND: The lack of distinct biomarkers for pancreatic cancer is a major cause of early-stage detection difficulty. The pancreatic cancer patient group with high metabolic tumor volume (MTV), one of the values measured from positron emission tomography-a confirmatory method and standard care for pancreatic cancer, showed a poorer prognosis than those with low MTV. Therefore, MTV-associated differentially expressed genes (DEGs) may be candidates for distinctive markers for pancreatic cancer. This study aimed to evaluate the possibility of MTV-related DEGs as markers or therapeutic targets for pancreatic cancer. METHODS: Tumor tissues and their normal counterparts were obtained from patients undergoing preoperative 18F-FDG PET/CT. The tissues were classified into MTV-low and MTV-high groups (7 for each) based on the MTV2.5 value of 4.5 (MTV-low: MTV2.5 < 4.5, MTV-high: MTV2.5 ≥ 4.5). Gene expression fold change was first calculated in cancer tissue compared to its normal counter and then compared between low and high MTV groups to obtain significant DEGs. To assess the suitability of the DEGs for clinical application, the correlation of the DEGs with tumor grades and clinical outcomes was analyzed in TCGA-PAAD, a large dataset without MTV information. RESULTS: Total RNA-sequencing (MTV RNA-Seq) revealed that 44 genes were upregulated and 56 were downregulated in the high MTV group. We selected the 29 genes matching MTV RNA-seq patterns in the TCGA-PAAD dataset, a large clinical dataset without MTV information, as MTV-associated genes (MAGs). In the analysis with the TCGA dataset, MAGs were significantly associated with patient survival, treatment outcomes, TCGA-PAAD-suggested markers, and CEACAM family proteins. Some MAGs showed an inverse correlation with miRNAs and were confirmed to be differentially expressed between normal and cancerous pancreatic tissues. Overexpression of KIF11 and RCC1 and underexpression of ADCY1 and SDK1 were detected in ~ 60% of grade 2 pancreatic cancer patients and associated with ~ 60% mortality in stages I and II. CONCLUSIONS: MAGs may serve as diagnostic markers and miRNA therapeutic targets for pancreatic cancer. Among the MAGs, KIF11, RCC1, ADCY, and SDK1 may be early diagnostic markers.

Dual-tracer PET/CT protocol with [18F]FDG and [68Ga]Ga-FAPI-46 outperforms single-tracer PET/CT with [18F]FDG in different cancer types, resulting in larger functional and gross tumor volume.

PURPOSE: Fibroblast activation protein (FAP) detected by positron-emission tomography (PET) using fibroblast activation protein inhibitor (FAPI) appears to be a promising target for cancer imaging, staging, and therapy, providing added value and strength as a complement to [18F]fluorodeoxyglucose (FDG) in cancer imaging. We recently introduced a combined single-session/dual-tracer protocol with [18F]FDG and [68Ga]Ga-FAPI for cancer imaging and staging. Malignant tissue visualization and target-to-background uptake ratios (TBRs) as well as functional tumor volume (FTV) and gross tumor volume (GTV) were assessed in the present study with single-tracer [18F]FDG PET/computed tomography (CT) and with dual-tracer [18F]FDG&[68Ga]Ga-FAPI-46 PET/CT. METHODS: A total of 19 patients with head and neck and gastrointestinal cancers received initial [18F]FDG-PET/CT followed by dual-tracer PET/CT after additional injection of [68Ga]Ga-FAPI-46 during the same medical appointment (on average 13.9 ± 12.3 min after injection of [18F]FDG). Two readers visually compared detection rate of malignant tissue, TBR, FTV, and GTV for tumor and metastatic tissue in single- and dual-tracer PET/CT. RESULTS: The diagnostic performance of dual-tracer compared to single-tracer PET/CT was equal in 13 patients and superior in 6 patients. The mean TBRs of tumors and metastases in dual-tracer PET/CTs were mostly higher compared to single-tracer PET/CT using maximal count rates (CRmax). GTV and FTV were significantly larger when measured on dual-tracer compared to single-tracer PET/CT. CONCLUSION: Dual-tracer PET/CT with [18F]FDG and [68Ga]Ga-FAPI-46 showed better visualization due to a generally higher TBR and larger FTV and GTV compared to [18F]FDG-PET/CT in several tumor entities, suggesting that [68Ga]Ga-FAPI-46 provides added value in pretherapeutic staging.

A single-center, multi-factor, retrospective study to improve the diagnostic accuracy of primary prostate cancer using [68Ga]Ga-PSMA-11 total-body PET/CT imaging.

PURPOSE: To improve the diagnostic accuracy of initial detection in patients with suspected primary prostate cancer (PCa). METHODS: Eighty-four patients who underwent Gallium-68-labeled prostate-specific membrane antigen ([68Ga]Ga-PSMA-11) total-body positron emission tomography/computed tomography (PET/CT) imaging before treatment in our department were enrolled. The maximum standard uptake value (SUVmax) of the prostate (SUVmax-PSMA), liver (SUVmax-PSMA-L), and mediastinal blood pool (SUVmax-PSMA-M) was measured using [68Ga]Ga-PSMA-11 total-body PET/CT imaging. The [68Ga]Ga-PSMA-11 derived metabolic tumor volume (MTV), the total lesion (TLP), and the cross-sectional areas of focal concentration in the prostate (CAP) were also determined. Besides, the prostate-specific antigen (PSA) levels and the above imaging characteristics were analyzed using receiver operating characteristic curves to identify the cutoff value to improve the diagnostic accuracy of suspected PCa. Finally, a multivariate regression analysis was conducted to discover the independent predictor to improve the diagnostic accuracy on [68Ga]Ga-PSMA-11 total-body imaging. RESULTS: There was no significant difference between the PCa and Non-PCa groups in age, height, weight, injected dose, except for the PSA levels, the SUVmax-PSMA, TLP, MTV, and CAP. Besides, the SUVmax-PSMA-T/L and SUVmax-PSMA-T/M derived from SUVmax-PSMA were both significantly different. In addition, the areas under the curve of PSA levels, SUVmax-PSMA, SUVmax-PSMA-T/L, SUVmax-PSMA-T/M, TLP, MTV, and CAP to predict PCa on [68Ga]Ga-PSMA-11 imaging were 0.620 (95% confidence interval (CI) 0.485-0.755), 0.864 (95% CI 0.757-0.972), 0.819 (95% CI 0.704-0.935), 0.876 (95% CI 0.771-0.980), 0.845 (95% CI 0.741-0.949), 0.820 (95% CI 0.702-0.938), 0.627 (95% CI 0.499-0.754), respectively. However, a multivariate regression analysis showed that SUVmax-PSMA was an independent predictor, with a cutoff value of 11.5 and an odds ratio of 1.221. CONCLUSION: The SUVmax-PSMA with a cutoff value of 11.5 was an independent predictor to improve the diagnostic accuracy of PCa on [68Ga]Ga-PSMA-11 total-body imaging.

TMTV-Net: fully automated total metabolic tumor volume segmentation in lymphoma PET/CT images - a multi-center generalizability analysis.

PURPOSE: Total metabolic tumor volume (TMTV) segmentation has significant value enabling quantitative imaging biomarkers for lymphoma management. In this work, we tackle the challenging task of automated tumor delineation in lymphoma from PET/CT scans using a cascaded approach. METHODS: Our study included 1418 2-[18F]FDG PET/CT scans from four different centers. The dataset was divided into 900 scans for development/validation/testing phases and 518 for multi-center external testing. The former consisted of 450 lymphoma, lung cancer, and melanoma scans, along with 450 negative scans, while the latter consisted of lymphoma patients from different centers with diffuse large B cell, primary mediastinal large B cell, and classic Hodgkin lymphoma cases. Our approach involves resampling PET/CT images into different voxel sizes in the first step, followed by training multi-resolution 3D U-Nets on each resampled dataset using a fivefold cross-validation scheme. The models trained on different data splits were ensemble. After applying soft voting to the predicted masks, in the second step, we input the probability-averaged predictions, along with the input imaging data, into another 3D U-Net. Models were trained with semi-supervised loss. We additionally considered the effectiveness of using test time augmentation (TTA) to improve the segmentation performance after training. In addition to quantitative analysis including Dice score (DSC) and TMTV comparisons, the qualitative evaluation was also conducted by nuclear medicine physicians. RESULTS: Our cascaded soft-voting guided approach resulted in performance with an average DSC of 0.68 ± 0.12 for the internal test data from developmental dataset, and an average DSC of 0.66 ± 0.18 on the multi-site external data (n = 518), significantly outperforming (p < 0.001) state-of-the-art (SOTA) approaches including nnU-Net and SWIN UNETR. While TTA yielded enhanced performance gains for some of the comparator methods, its impact on our cascaded approach was found to be negligible (DSC: 0.66 ± 0.16). Our approach reliably quantified TMTV, with a correlation of 0.89 with the ground truth (p < 0.001). Furthermore, in terms of visual assessment, concordance between quantitative evaluations and clinician feedback was observed in the majority of cases. The average relative error (ARE) and the absolute error (AE) in TMTV prediction on external multi-centric dataset were ARE = 0.43 ± 0.54 and AE = 157.32 ± 378.12 (mL) for all the external test data (n = 518), and ARE = 0.30 ± 0.22 and AE = 82.05 ± 99.78 (mL) when the 10% outliers (n = 53) were excluded. CONCLUSION: TMTV-Net demonstrates strong performance and generalizability in TMTV segmentation across multi-site external datasets, encompassing various lymphoma subtypes. A negligible reduction of 2% in overall performance during testing on external data highlights robust model generalizability across different centers and cancer types, likely attributable to its training with resampled inputs. Our model is publicly available, allowing easy multi-site evaluation and generalizability analysis on datasets from different institutions.

Prognostic value of baseline and interim [18F]FDG PET metabolic parameters in pediatric Hodgkin's lymphoma.

BACKGROUND: Hodgkin lymphoma (HL) in pediatric populations has a high survival rate but poses risks for long-term morbidities. Although [18F]fluoro­2­deoxy­2­d­glucose positron emission tomography ([18F]FDG PET) scans offer potential for improved risk stratification, the definitive prognostic value of quantitative [18F]FDG PET parameters remains unclear for pediatric HL. METHODS: A single-center, retrospective study included pediatric patients diagnosed with HL between 2016 and 2023 treated according to EuroNet-PHL-C1 and DAL/GPOH-HD protocols. Patients underwent baseline and interim PET/CT scans after two chemotherapy cycles. Event-free survival (EFS) was the primary endpoint, Deauville score was the secondary endpoint. Quantitative [18F]FDG PET parameters included SUVmax, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) that were evaluated using two segmentation methods (SUV 2.5, 41% SUVmax). Survival outcomes were assessed using Cox regression analysis. RESULTS: A total of 115 patients (50 males, median age 14.2 years) were studied, with a median follow-up period of 35 months. During this period, 16 cases (13.9%) of relapse or progression were noted. Baseline and interim MTV 2.5, MTV 41%, TLG 2.5, and TLG 41%, along with interim SUVmax, were significantly associated with worse EFS and correlated with post-treatment Deauville scores. In multivariable analysis, interim MTV 2.5 > 0 ml (adj. hazard ratio, HR: 3.89, p = 0.009) and interim TLG 41% ≥ 30 g (adj. HR: 7.98, p = 0.006) were independent risk factors for EFS. CONCLUSION: Baseline and interim [18F]FDG PET parameters can serve as significant prognostic indicators for EFS and treatment response in pediatric HL. These quantitative measures could enhance individualized, risk-adapted treatment strategies for children and adolescents with HL.

Artificial intelligence-based, volumetric assessment of the bone marrow metabolic activity in [18F]FDG PET/CT predicts survival in multiple myeloma.

PURPOSE: Multiple myeloma (MM) is a highly heterogeneous disease with wide variations in patient outcome. [18F]FDG PET/CT can provide prognostic information in MM, but it is hampered by issues regarding standardization of scan interpretation. Our group has recently demonstrated the feasibility of automated, volumetric assessment of bone marrow (BM) metabolic activity on PET/CT using a novel artificial intelligence (AI)-based tool. Accordingly, the aim of the current study is to investigate the prognostic role of whole-body calculations of BM metabolism in patients with newly diagnosed MM using this AI tool. MATERIALS AND METHODS: Forty-four, previously untreated MM patients underwent whole-body [18F]FDG PET/CT. Automated PET/CT image segmentation and volumetric quantification of BM metabolism were based on an initial CT-based segmentation of the skeleton, its transfer to the standardized uptake value (SUV) PET images, subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, ten different uptake thresholds (AI approaches), based on reference organs or absolute SUV values, were applied for definition of pathological tracer uptake and subsequent calculation of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Correlation analysis was performed between the automated PET values and histopathological results of the BM as well as patients' progression-free survival (PFS) and overall survival (OS). Receiver operating characteristic (ROC) curve analysis was used to investigate the discrimination performance of MTV and TLG for prediction of 2-year PFS. The prognostic performance of the new Italian Myeloma criteria for PET Use (IMPeTUs) was also investigated. RESULTS: Median follow-up [95% CI] of the patient cohort was 110 months [105-123 months]. AI-based BM segmentation and calculation of MTV and TLG were feasible in all patients. A significant, positive, moderate correlation was observed between the automated quantitative whole-body PET/CT parameters, MTV and TLG, and BM plasma cell infiltration for all ten [18F]FDG uptake thresholds. With regard to PFS, univariable analysis for both MTV and TLG predicted patient outcome reasonably well for all AI approaches. Adjusting for cytogenetic abnormalities and BM plasma cell infiltration rate, multivariable analysis also showed prognostic significance for high MTV, which defined pathological [18F]FDG uptake in the BM via the liver. In terms of OS, univariable and multivariable analysis showed that whole-body MTV, again mainly using liver uptake as reference, was significantly associated with shorter survival. In line with these findings, ROC curve analysis showed that MTV and TLG, assessed using liver-based cut-offs, could predict 2-year PFS rates. The application of IMPeTUs showed that the number of focal hypermetabolic BM lesions and extramedullary disease had an adverse effect on PFS. CONCLUSIONS: The AI-based, whole-body calculations of BM metabolism via the parameters MTV and TLG not only correlate with the degree of BM plasma cell infiltration, but also predict patient survival in MM. In particular, the parameter MTV, using the liver uptake as reference for BM segmentation, provides solid prognostic information for disease progression. In addition to highlighting the prognostic significance of automated, global volumetric estimation of metabolic tumor burden, these data open up new perspectives towards solving the complex problem of interpreting PET scans in MM with a simple, fast, and robust method that is not affected by operator-dependent interventions.

[18F]FDG-PET/CT-based risk stratification in women with locally advanced uterine cervical cancer.

BACKGROUND: [18F]FDG-PET/CT is used for staging and treatment planning in patients with locally advanced cervical cancer (LACC). We studied if a PET-based prediction model could provide additional risk stratification beyond International Federation of Gynaecology and Obstetrics (FIGO) staging in our population with LACC to aid treatment decision making. METHODS: In total, 183 patients with LACC treated with chemoradiation between 2013 and 2018 were included. Patients were treated according to FIGO 2009 and retrospectively reclassified according to FIGO 2018 staging system. After validation of an existing PET-based prediction model, the predicted recurrent free survival (RFS), disease specific survival (DSS) and overall survival (OS) at 1, 3, and 5 years, based on metabolic tumor volume (MTV), maximum standardized uptake value (SUVmax) and highest level of [18F]FDG-positive node was calculated. Then the observed survival was compared to the predicted survival. An area under the curve (AUC) close to or higher than 0.7 was considered adequate for accurate prediction. The Youden (J) index defined survival chance cutoff values for low and high risk groups. RESULTS: All AUC values for the comparison between predicted and observed outcomes were > 0.7 except for 5-year RFS and for 5-year OS which were close to 0.7 (0.684 and 0.650 respectively). Cutoff values for low and high risk survival chance were 0.44 for the 3-year RFS and 0.47 for the 5-year OS. The FIGO 2009 system could not differentiate between the risk profiles. After reclassification according to FIGO 2018, all patients with stage IIIC2 and IVB fell in the high risk and almost all patients with stages IB2-IIIB and IVA in the low risk group. In patients with stage IIIC1 disease the FIGO stage cannot discriminate between the risk profiles. CONCLUSIONS: Low and high risk patients with LACC can be identified with the PET-based prediction model. In particular patients with stage IIIC1 need additional risk stratification besides the FIGO 2018 staging. The Kidd model could be a useful tool to aid treatment decision making in these patients. Our results also support the choice of [18F]FDG-PET/CT imaging in patients with LACC.

A low total metabolic tumor volume independently predicts for a longer time to first treatment in initially observed, low tumor burden follicular lymphoma.

Watchful waiting is an acceptable management strategy for advanced-stage, low tumor burden (LTB) patients with follicular lymphoma (FL). However, the prediction of how long this treatment-free observation period will last remains imperfect. We explored whether total metabolic tumor volume (TMTV) and other positron emission tomography parameters were predictive of time to first treatment (TTFT). We analyzed 97 grade 1-3A advanced-stage LTB FL patients and found that a high TMTV was associated with other tumor burden features at diagnosis. Patients with a TMTV above our established cutoff of 50 mL had a significantly shorter median duration of observation (2.6 vs. 8.8 years; p = 0.001). At 5 years, 77% of patients with a high TMTV and 46% of patients with a low TMTV required treatment. In the multivariable analysis, a high TMTV was the only independent factor predicting TTFT (hazard ratio = 2.09; p = 0.017). Overall, TMTV is a strong predictor of the duration of observation in LTB FL patients. Upon validation of our cutoff in external series and standardization of the methodology, the TMTV could become an additional factor to consider deferring or initiating treatment in otherwise LTB patients.

Impact of baseline and interim quantitative PET parameters on outcomes of classical Hodgkin Lymphoma.

Currently, analysis of interim PET (iPET) according to the Deauville score (DS) is the most important predictive factor in Hodgkin lymphoma (HL); however, there is room for improvement in its prognostic power. This study aimed to evaluate the prognostic value of quantitative PET analysis (maximum standard uptake value [SUVmax], total metabolic tumor volume [TMTV] and total lesion glicolysis [TLG]) at baseline (PET0) and iPET in a retrospective cohort of newly diagnosed classical HL. For positive iPET (+ iPET), the reduction of quantitative parameters in relation to PET0 (ΔSUVmax, ΔTMTV and ΔTLG) was calculated. Between 2011 and 2017, 234 patients treated with ABVD were analyzed. Median age was 30 years-old, 59% had advanced stage disease, 57% a bulky mass and 25% a + iPET (DS 4-5). At baseline, high TLG was associated with an increased cumulative incidence of failure (CIF) (p = 0.032) while neither SUVmax, TMTV or TLG were associated with overall survival (OS) or progression-free survival (PFS). In multivariate analysis, only iPET was associated with CIF (p < 0.001). Among ΔSUVmax, ΔTMTV and ΔTLG, only a ΔSUVmax ≥ 68.8 was significant for PFS (HR: 0.31, CI95%: 0.11-0.86, p = 0.024). A subset of patients with improved PFS amongst + iPET was identified by the quantitative (ΔSUVmax ≥ 68.8%) analysis. In this real-world Brazilian cohort, with prevalent high-risk patients, quantitative analysis of PET0 did not demonstrate to be prognostic, while a dynamic approach incorporating the ΔSUVmax to + iPET succeeded in refining a subset with better prognosis. These findings warrant validation in larger series and indicate that not all patients with + iPET might need treatment intensification.

Liver Resection Criteria for Patients with Hepatocellular Carcinoma and Multiple Tumors Based on Total Tumor Volume.

BACKGROUND: In many Asian hepatocellular carcinoma (HCC) guidelines, resection is an option for multiple HCCs. It is difficult to compare small but multiple tumors vs. fewer large tumors in terms of the traditional tumor burden definition. We aimed to evaluate the role of liver resection for multiple HCCs and determine factors associated with survival benefits. METHODS: We reviewed 160 patients with multiple HCCs who underwent liver resection between July 2003 and December 2018. The risk factors for tumor recurrence were assessed using Cox proportional hazards modeling, and survival was analyzed using the Kaplan-Meier method. RESULTS: In all 160 patients, 133 (83.1%) exceeded the Milan criteria. Total tumor volume (TTV) > 275 cm3 and serum alpha-fetoprotein (AFP) level > 20 ng/mL were associated with disease-free survival. Patients beyond the Milan criteria were grouped into three risk categories: no risk (TTV ≤ 275 cm3 and AFP ≤ 20 ng/mL, n = 39), one risk (either TTV > 275 cm3 or AFP > 20 ng/mL, n = 76), and two risks (TTV > 275 cm3 and AFP > 20 ng/mL, n = 18). No-risk group had comparable disease-free survival (p = 0.269) and overall survival (p = 0.215) to patients who met the Milan criteria. CONCLUSION: Patients with TTV ≤ 275 cm3 and AFP ≤ 20 ng/mL can have good outcomes even exceed the Milan criteria.

MRI-based volumetric tumor parameters before and during chemoradiation predict tumor recurrence and patient survival in locally advanced cervical cancer: a subgroup analysis of a phase II prospective trial.

BACKGROUND: This subgroup analysis of a prospective phase II trial aimed to identify valuable and accessible prognostic factors for overall survival (OS) and progression-free survival (PFS) of patients with locally advanced cervical cancer (LACC). METHODS: Patients with FIGO II to IVA cervical cancer were assessed in this study. All patients underwent concurrent chemoradiotherapy (CCRT) followed by brachytherapy. Tumor parameters based on MRI scans before and during CCRT were evaluated for Overall survival (OS) and Progression-free survival (PFS). RESULTS: A total of 86 patients were included in this analysis with a median follow-up period of 31.7 months. Three-year OS and PFS rates for all patients were 87.1% and 76.5%, respectively. Univariate Cox regression analysis showed that restaging tumor size (rTS) over 2.55 cm (p < 0.001), initial tumor volume (iTV) over 55.99 cc (p < 0.001), downstaging (p = 0.042), and restaging tumor volume (rTV) over 6.25 cc (p = 0.006) were significantly associated with OS. rTS (p < 0.001), iTV (p < 0.001), downstaging (p = 0.027), and rTV (p < 0.001) were identified as significant prognostic factors for PFS. In the stepwise multivariable analysis, only rTS > 2.55 cm showed statistically significant with OS (HR: 5.47, 95% CI 1.80-9.58, p = 0.035) and PFS (HR: 3.83, 95% CI 1.50-11.45; p = 0.025). CONCLUSIONS: Initial tumor size and restaging tumor volume that are easily accessible during radiotherapy provide valuable prognostic information for cervical cancer. MRI-based measurable volumetric scoring system can be readily applied in real-world practice of cervical cancer. CLINICAL TRIAL INFORMATION: This study is a subgroup analysis of prospective trial registered at ClinicalTrials.gov Identifier: NCT02993653.

Significance of radiation therapy in the myxoid round-cell liposarcoma treatment regimen.

BACKGROUND AND PURPOSE: Because myxoid liposarcomas are more radiosensitive than other soft tissue sarcomas, there have been several reports of 50 Gy preoperative radiation therapy combined with surgery, but the wound complication rate is reportedly high. We have performed preoperative irradiation at a reduced dose of 40 Gy and definitive radiation therapy for unresectable cases. This study aimed to report the tumor reduction rate and oncological results with a reduced dose of preoperative irradiation and the outcome of definitive irradiation for unresectable cases. MATERIALS AND METHODS: Forty-one patients with myxoid liposarcoma treated in our institution between 2002 and 2021 were included. We examined the tumor volume shrinkage rate with preoperative radiation, compared complications and oncological outcomes between preoperative radiation and surgery-only cases, and investigated the prognosis and tumor shrinkage of definitive radiation cases. RESULTS: The total dose irradiated was 40 Gy except in two cases. The mean tumor volume reduction rate was 52.0%. A decreased dose of preoperative radiation did not worsen clinical outcomes with fewer complications. The total dose of definitive radiation was approximately 60 Gy. The mean tumor volume reduction rate was 55.0%. The tumor shrinkage maintenance rate was 100% in a median follow-up period of 50.5 months. CONCLUSION: Preoperative radiation therapy for myxoid liposarcoma near vital organs is a good approach because even with a reduced dose of 40 Gy, significant tumor reduction and excellent results were achieved. Definitive radiation therapy is the recommended treatment for older patients with serious comorbidities or inoperable patients.

Predictive value of cyst/tumor volume ratio of pituitary adenoma for tumor cell proliferation.

BACKGROUND: MRI has been widely used to predict the preoperative proliferative potential of pituitary adenoma (PA). However, the relationship between the cyst/tumor volume ratio (C/T ratio) and the proliferative potential of PA has not been reported. Herein, we determined the predictive value of the C/T ratio of PA for tumor cell proliferation. METHODS: The clinical data of 72 patients with PA and cystic change on MRI were retrospectively analyzed. PA volume, cyst volume, and C/T ratio were calculated. The corresponding intraoperative specimens were collected. Immunohistochemistry and hematoxylin-eosin staining were performed to evaluate the Ki67 index and nuclear atypia. Patients were categorized according to the Ki67 index (< 3% and ≥ 3%) and nuclear atypia (absence and presence). Univariate and multivariate analyses were used to identify the significant predictors of the Ki67 index and nuclear atypia. The receiver operating characteristic curve assessed the prediction ability of the significant predictors. RESULTS: Larger tumor volumes, smaller cyst volumes, and lower C/T ratios were found in patients with higher Ki67 indexes and those with nuclear atypia (P < 0.05). C/T ratio was an independent predictor of the Ki67 index (odds ratio = 0.010, 95% confidence interval = 0.000-0.462) and nuclear atypia (odds ratio = 0.010, 95% confidence interval = 0.000-0.250). The predictive value of the C/T ratio did not differ significantly from that of tumor volume (P > 0.05) but was better than that of cyst volume (P < 0.05). The area under the curve of the C/T ratio for predicting the Ki67 index and nuclear atypia was larger than that for predicting cyst volume and tumor volume. CONCLUSIONS: C/T ratios can be used to predict PA tumor proliferation preoperatively. Our findings may facilitate the selection of surgery timing and the efficacy evaluation of surgery.

New predictors of microvascular invasion for small hepatocellular carcinoma ≤ 3 cm.

BACKGROUND: Microvascular invasion (MVI) is a risk factor for postoperative recurrence of hepatocellular carcinoma (HCC), even in early-stage HCC. In small HCC ≤ 3 cm, treatment options include anatomical resection or non-anatomical resection, and MVI has a major effect on treatment decisions. We aimed to identify the predictors of MVI in small HCC ≤ 3 cm. METHODS: We retrospectively studied 129 patients with very early or early-stage HCC ≤ 3 cm who had undergone 18F-fluorodeoxyglucose positron emission tomography/computed tomography and subsequent hepatic resection from January 2016 to August 2023. These patients were divided into the derivation cohort (n = 86) and validation cohort (n = 43). We examined the risk factors for MVI using logistic regression analysis, and established a predictive scoring system in the derivation cohort. We evaluated the accuracy of our scoring system in the validation cohort. RESULTS: In the derivation cohort, a Lens culinaris agglutinin-reactive fraction of alpha-fetoprotein (AFP-L3), prothrombin induced by vitamin K deficiency or antagonist-II (PIVKA-II), and metabolic tumor volume (MTV) were independent predictors of MVI. We established the scoring system using these three factors. In the validation test, there were no MVI-positive cases with a score of 0 and 1, and all cases were MVI-positive with a score of 4. Moreover, with a score ≥ 2, the sensitivity, specificity, and accuracy of our scoring system were 100%, 71.4%, and 81.4%, respectively. CONCLUSIONS: Our scoring system can accurately predict MVI in small HCC ≤ 3 cm, and could contribute to establishing an appropriate treatment strategy.

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

OBJECTIVE: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Factors associated with the local control of brain metastases: a systematic search and machine learning application.

BACKGROUND: Enhancing Local Control (LC) of brain metastases is pivotal for improving overall survival, which makes the prediction of local treatment failure a crucial aspect of treatment planning. Understanding the factors that influence LC of brain metastases is imperative for optimizing treatment strategies and subsequently extending overall survival. Machine learning algorithms may help to identify factors that predict outcomes. METHODS: This paper systematically reviews these factors associated with LC to select candidate predictor features for a practical application of predictive modeling. A systematic literature search was conducted to identify studies in which the LC of brain metastases is assessed for adult patients. EMBASE, PubMed, Web-of-Science, and the Cochrane Database were searched up to December 24, 2020. All studies investigating the LC of brain metastases as one of the endpoints were included, regardless of primary tumor type or treatment type. We first grouped studies based on primary tumor types resulting in lung, breast, and melanoma groups. Studies that did not focus on a specific primary cancer type were grouped based on treatment types resulting in surgery, SRT, and whole-brain radiotherapy groups. For each group, significant factors associated with LC were identified and discussed. As a second project, we assessed the practical importance of selected features in predicting LC after Stereotactic Radiotherapy (SRT) with a Random Forest machine learning model. Accuracy and Area Under the Curve (AUC) of the Random Forest model, trained with the list of factors that were found to be associated with LC for the SRT treatment group, were reported. RESULTS: The systematic literature search identified 6270 unique records. After screening titles and abstracts, 410 full texts were considered, and ultimately 159 studies were included for review. Most of the studies focused on the LC of the brain metastases for a specific primary tumor type or after a specific treatment type. Higher SRT radiation dose was found to be associated with better LC in lung cancer, breast cancer, and melanoma groups. Also, a higher dose was associated with better LC in the SRT group, while higher tumor volume was associated with worse LC in this group. The Random Forest model predicted the LC of brain metastases with an accuracy of 80% and an AUC of 0.84. CONCLUSION: This paper thoroughly examines factors associated with LC in brain metastases and highlights the translational value of our findings for selecting variables to predict LC in a sample of patients who underwent SRT. The prediction model holds great promise for clinicians, offering a valuable tool to predict personalized treatment outcomes and foresee the impact of changes in treatment characteristics such as radiation dose.

Novel computational biology modeling system can accurately forecast response to neoadjuvant therapy in early breast cancer.

BACKGROUND: Generalizable population-based studies are unable to account for individual tumor heterogeneity that contributes to variability in a patient's response to physician-chosen therapy. Although molecular characterization of tumors has advanced precision medicine, in early-stage and locally advanced breast cancer patients, predicting a patient's response to neoadjuvant therapy (NAT) remains a gap in current clinical practice. Here, we perform a study in an independent cohort of early-stage and locally advanced breast cancer patients to forecast tumor response to NAT and assess the stability of a previously validated biophysical simulation platform. METHODS: A single-blinded study was performed using a retrospective database from a single institution (9/2014-12/2020). Patients included: ≥ 18 years with breast cancer who completed NAT, with pre-treatment dynamic contrast enhanced magnetic resonance imaging. Demographics, chemotherapy, baseline (pre-treatment) MRI and pathologic data were input into the TumorScope Predict (TS) biophysical simulation platform to generate predictions. Primary outcomes included predictions of pathological complete response (pCR) versus residual disease (RD) and final volume for each tumor. For validation, post-NAT predicted pCR and tumor volumes were compared to actual pathological assessment and MRI-assessed volumes. Predicted pCR was pre-defined as residual tumor volume ≤ 0.01 cm3 (≥ 99.9% reduction). RESULTS: The cohort consisted of eighty patients; 36 Caucasian and 40 African American. Most tumors were high-grade (54.4% grade 3) invasive ductal carcinomas (90.0%). Receptor subtypes included hormone receptor positive (HR+)/human epidermal growth factor receptor 2 positive (HER2+, 30%), HR+/HER2- (35%), HR-/HER2+ (12.5%) and triple negative breast cancer (TNBC, 22.5%). Simulated tumor volume was significantly correlated with post-treatment radiographic MRI calculated volumes (r = 0.53, p = 1.3 × 10-7, mean absolute error of 6.57%). TS prediction of pCR compared favorably to pathological assessment (pCR: TS n = 28; Path n = 27; RD: TS n = 52; Path n = 53), for an overall accuracy of 91.2% (95% CI: 82.8% - 96.4%; Clopper-Pearson interval). Five-year risk of recurrence demonstrated similar prognostic performance between TS predictions (Hazard ratio (HR): - 1.99; 95% CI [- 3.96, - 0.02]; p = 0.043) and clinically assessed pCR (HR: - 1.76; 95% CI [- 3.75, 0.23]; p = 0.054). CONCLUSION: We demonstrated TS ability to simulate and model tumor in vivo conditions in silico and forecast volume response to NAT across breast tumor subtypes.

Baseline metabolic tumour burden improves risk stratification in Hodgkin lymphoma: A Children's Oncology Group study.

The Children's Oncology Group AHOD0831 study used a positron emission tomography (PET) response-adapted approach in high-risk Hodgkin lymphoma, whereby slow early responders (SERs) received more intensive therapy than rapid early responders (RERs). We explored if baseline PET-based characteristics would improve risk stratification. Of 166 patients enrolled in the COG AHOD0831 study, 94 (57%) had baseline PET scans evaluable for quantitative analysis. For these patients, total body metabolic tumour volume (MTV), total lesion glycolysis (TLG), maximum standardized uptake value (SUVmax ) and peak SUV (SUVpeak ) were obtained. MTV/TLG thresholds were an SUV of 2.5 (MTV2.5 /TLG2.5 ) and 40% of the tumour SUVmax (MTV40% /TLG40% ). TLG2.5 was associated with event-free survival (EFS) in the complete cohort (p = 0.04) and in RERs (p = 0.01), but not in SERs (p = 0.8). The Youden index cut-off for TLG2.5 was 1841. Four-year EFS was 92% for RER/TLG2.5  up to 1841, 60% for RER/TLG2.5  greater than 1841, 74% for SER/TLG2.5  up to 1841 and 79% for SER/TLG2.5  greater than 1841. Second EFS for RER/TLG2.5  up to 1841 was 100%. Thus, RERs with a low baseline TLG2.5 experienced excellent EFS with less intensive therapy, whereas RERs with a high baseline TLG2.5 experienced poor EFS. These findings suggest that patients with a high upfront tumour burden may benefit from intensified therapy, even if they achieve a RER.