Enhancing the reliability of deep learning-based head and neck tumour segmentation using uncertainty estimation with multi-modal images.

OBJECTIVE: Deep learning shows promise in autosegmentation of head and neck cancer (HNC) primary tumours (GTV-T) and nodal metastases (GTV-N). However, errors such as including non-tumour regions or missing nodal metastases still occur. Conventional methods often make overconfident predictions, compromising reliability. Incorporating uncertainty estimation, which provides calibrated confidence intervals can address this issue. Our aim was to investigate the efficacy of various uncertainty estimation methods in improving segmentation reliability. We evaluated their confidence levels in voxel predictions and ability to reveal potential segmentation errors. Approach. We retrospectively collected data from 567 HNC patients with diverse cancer sites and multi-modality images (CT, PET, T1-, and T2-weighted MRI) along with their clinical GTV-T/N delineations. Using the nnUNet 3D segmentation pipeline, we compared seven uncertainty estimation methods, evaluating them based on segmentation accuracy (Dice similarity coefficient, DSC), confidence calibration (Expected Calibration Error, ECE), and their ability to reveal segmentation errors (Uncertainty-Error overlap using DSC, UE-DSC). Main Results. Evaluated on the hold-out test dataset (n=97), the median DSC scores for GTV-T and GTV-N segmentation across all uncertainty estimation methods had a narrow range, from 0.73 to 0.76 and 0.78 to 0.80, respectively. In contrast, the median ECE exhibited a wider range, from 0.30 to 0.12 for GTV-T and 0.25 to 0.09 for GTV-N. Similarly, the median UE-DSC also ranged broadly, from 0.21 to 0.38 for GTV-T and 0.22 to 0.36 for GTV-N. A probabilistic network - PhiSeg method consistently demonstrated the best performance in terms of ECE and UE-DSC. Significance. Our study highlights the importance of uncertainty estimation in enhancing the reliability of deep learning for autosegmentation of HNC GTV. The results show that while segmentation accuracy can be similar across methods, their reliability, measured by calibration error and uncertainty-error overlap, varies significantly. Used with visualisation maps, these methods may effectively pinpoint uncertainties and potential errors at the voxel level. .

Use of Decipher Prostate Biopsy Test in Patients with Favorable-risk Disease Undergoing Conservative Management or Radical Prostatectomy in the Surveillance, Epidemiology, and End Results Registry.

BACKGROUND AND OBJECTIVE: The extent of prostate cancer found on biopsy, as well as prostate cancer grade and genomic tests, can affect clinical decision-making. The impact of these factors on the initial management approach and subsequent patient outcomes for men with favorable-grade prostate cancer has not yet been determined on a population level. Our objective was to explore the association of Decipher 22-gene genomic classifier (GC) biopsy testing on the initial use of conservative management versus radical prostatectomy (RP) and to determine the independent effect of GC scores on RP pathologic outcomes. METHODS: A total of 87 140 patients diagnosed with grade group 1 and 2 prostate cancer between 2016 and 2018 from the Surveillance, Epidemiology, and End Results registry data were linked to GC testing results (2576 tested and 84 564 untested with a GC). The primary endpoints of interest were receipt of conservative management or RP, pathologic upgrading (pathologic grade group 3-5), upstaging (pathologic ≥T3b), and adverse pathologic features (pathologic upgrading, upstaging, or lymph node invasion). Multivariable logistic regressions quantified the association of variables with outcomes of interest. KEY FINDINGS AND LIMITATIONS: GC tested patients were more likely to have grade group 2 on biopsy (51% vs 46%, p < 0.001) and lower prostate-specific antigen (6.1 vs 6.3, p = 0.016). Conservative management increased from 37% to 39% and from 22% to 24% during 2016-2018 for the GC tested and untested populations, respectively. GC testing was significantly associated with increased odds of conservative management (odds ratio [OR] 2.1, 95% confidence interval [CI] 1.9-2.4, p < 0.001). The distribution of biopsy GC risk was as follows: 45% low risk, 30% intermediate risk, and 25% high risk. In adjusted analyses, higher GC (per 0.1 increment) scores (OR 1.24, 95% CI 1.17-1.31, p < 0.001) and percent positive cores (1.07, 95% CI 1.02-1.12, p = 0.009) were significantly associated with the receipt of RP. A higher GC score was significantly associated with all adverse outcomes (pathologic upgrading [OR 1.29, 95% CI 1.12-1.49, p < 0.001], upstaging [OR 1.31, 95% CI 1.05-1.62, p = 0.020], and adverse pathology [OR 1.27, 95% CI 1.12-1.45, p < 0.001]). Limitations include observational biases associated with the retrospective study design. CONCLUSIONS AND CLINICAL IMPLICATIONS: Men who underwent GC testing were more likely to undergo conservative management. GC testing at biopsy is prognostic of adverse pathologic outcomes in a large population-based registry. PATIENT SUMMARY: In this population analysis of men with favorable-risk prostate cancer, those who underwent genomic testing at biopsy were more likely to undergo conservative management. Of men who initially underwent radical prostatectomy, higher genomic risk but not tumor volume was associated with adverse pathologic outcomes. The use of genomic testing at prostate biopsy improves risk stratification and may better inform treatment decisions than the use of tumor volume alone.

Appropriateness of Dose Attenuation Margin Outside the Gross Tumor Volume (GTV) in Volumetric-Modulated Arc-Based Radiosurgery for Brain Metastasis With the Steepest Dose Gradient Outside the GTV and Biologically Effective Dose 80 Gy to GTV Boundary.

Introduction In stereotactic radiosurgery (SRS) for brain metastasis (BM), volumetric-modulated arcs (VMA) can provide a suitable dose distribution and efficient delivery, even with a widely available 5-mm leaf-width multileaf collimator (MLC). The planning optimization with affirmatively accepting internal high doses of a gross tumor volume (GTV) enhances the steepness of the dose gradient outside the GTV. However, an excessively steep dose falloff outside a GTV is susceptible to insufficient coverage of inherent irradiation uncertainties with the dose attenuation margin. This study was conducted to examine the appropriateness of dose attenuation margin outside a GTV in 5-mm MLC VMA-based SRS with a steep dose gradient and dose prescription with a biologically effective dose (BED) 80 Gy in various fractions to the GTV margin. Materials and methods This was a planning study for the clinical scenario of a single BM and targeted 28 GTVs, including nine sphere-shaped models with diameters of 5-45 mm and 19 clinical BMs (GTV 0.08-44.33 cc). SRS plans were generated for each GTV using 5-mm MLC VMA with an optimization that prioritized the steepness of dose falloff outside the GTV boundary without any internal dose constraints. A prescribed dose with the BED 80 Gy in 1-10 fraction(s) was assigned to the GTV D V-0.01 cc, a minimum dose of GTV minus 0.01 cc (D >95% for GTV >0.20 cc, D 95% for GTV ≤0.20 cc). The BED was based on the linear-quadratic formula with an alpha/beta ratio of 10 (BED10). Two planning systems were compared for the GTV + 2 mm structures that were generated by adding an isotropic 2-mm margin to the GTV. Results The GTV + 2 mm volumes differed significantly between the systems and further varied on the dose-volume histograms. The D V-0.05 cc, D 98%, and D 95% of the GTV + 2 mm were associated with substantial over- or under-coverages of the GTV + 2 mm, although the irradiated isodose volumes (IIVs) of the D 98% were closest to the GTV + 2 mm in general. The coverage values of the GTV + 2 mm with the minimum dose of the IIV equivalent to the GTV + 2 mm, D eIIV, were 93.3%-98.7% (≥95% in 26 cases). The GTV + 2 mm D eIIV relative to the GTV D V-0.01 cc was ≥81.9% (BED10 ≥60 Gy in ≤5 fractions) in 13 cases, while those were <69.8% (BED10 <48 Gy in ≤5 fractions) in four cases with the GTV of 0.33-1.77 cc. Conclusions A dose attenuation margin outside a GTV can be excessively steep for some small GTVs in 5-mm MLC VMA-based SRS with a steepest dose gradient and a BED10 80 Gy in ≤5 fractions to the GTV D V-0.01 cc, for which an adjustment of the too precipitous dose gradient is preferred to sufficiently cover relevant uncertainties. A GTV + 2 mm D eIIV with ≥95% coverage is more suitable for evaluating the appropriateness of dose attenuation outside the GTV than other common metrics with a fixed % coverage or D V-≤0.05 cc. Given the substantial variability in margin addition functions among planning systems, dose prescription to a margin-added GTV is unsuitable for ensuring uniform dose prescription.

Fluorine-18 FDG PET/CT and New NIMS Grading System for Chemotherapy Response in Breast Cancer.

Background: Positron emission tomography with computed tomography (PET-CT) using fluorine 18-fluorodeoxyglucose (F-18 FDG) is increasingly used to stage patients with locally advanced breast cancer and for assessing treatment response after neoadjuvant chemotherapy (NACT). Aims and Objectives: The aim of the study was to assess the correlation between PET-CT parameters and pathologic response of breast primary after NACT in breast cancer patients and to devise a grading system called NIMS grading system for response assessment using PET quantitative parameters. Materials and Methods: 55 patients who underwent F-18 FDG PET-CT before starting the therapy and again after completion of therapy were identified and included in the study. The clinical data and the histopathologic findings were recorded. All the patients received chemotherapy followed by surgery with axillary lymph node dissection. The PET-CT results were interpreted both qualitatively by visual analysis and quantitatively by estimating maximum Standardized uptake values(SUVmax) and other parameters - SUVmean, SUL, SUVBSA, Metabolic tumor volume (MTV) and Total lesion glycolysis (TLG). Results: The sensitivity and specificity of F-18 FDG PET-CT to detect the residual disease after neoadjuvant chemotherapy was 75.6% & 92.8% respectively. Differences between complete response and residual disease were significant for ΔSUVmax(p=0.005), ΔSUVmean(p=0.006), ΔSUL (0.005) and ΔSUVBSA(0.004), while ΔMTV and ΔTLG were not significantly different between the two groups. The new NIMS grading system included scoring of ΔSUVmax, ΔSUVBSA, ΔTLG and ΔMTV on scale of 1 to 4 and correlated well with PERCIST criteria. Conclusion: F-18 FDG PET-CT had a good accuracy in the detection of residual disease after completion of NACT. Pre chemotherapy PET-CT is not adequate to predict the response of primary tumour to chemotherapy. However, changes in the values of various PET-CT parameters are a sensitive tool to assess the response to chemotherapy. The new grading system is easy to use and showed good correlation to PERCIST.

Case report: Pulmonary sarcomatoid carcinoma demonstrating rapid growth on follow-up CT.

Background: The tumor growth rate and tumor volume doubling time are crucial parameters in diagnosing and managing lung lesions. Pulmonary sarcomatoid carcinoma (PSC) is a unique and highly malignant subtype of lung cancer, with limited documentation on its growth feature. This article aims to address the gap in knowledge regarding a PSC's growth patterns by describing the characteristics of a confirmed case using computed tomography, thereby enhancing the understanding of this rare disease. Case presentation: A 79-year-old man was transferred to our center presenting with a mild cough, blood-tinged sputum, and a malignant nodule in the left upper lobe. Chest CT revealed a solid nodule in the left upper lobe. A follow-up CT ten days later showed a significant increase in the size of the nodule, accompanied by ground-glass opacity in the surrounding lung. The rapid preoperative growth of the nodule suggested a non-neoplastic lesion, and intraoperative frozen pathology also considered the possibility of tuberculosis. Subsequently, a left upper apical-posterior segment (S1 + 2) resection was performed. Postoperative tumor pathology confirmed the diagnosis of pulmonary sarcomatoid carcinoma with extensive giant cell carcinoma and necrosis. Immunohistochemistry indicated approximately 60% PD-L1 positive and genetic testing revealed a MET mutation. The patient was discharged with oral crizotinib targeted therapy, and his condition remained stable postoperatively. The patient is currently undergoing regular follow-up at our hospital, with no evidence of distant metastasis or recurrence. Conclusion: Pulmonary sarcomatoid carcinoma can exhibit rapid tumor growth on imaging, and PSC should be considered in the differential diagnosis for lesions that present with a fast growth rate. Timely and appropriate treatment for PSC may lead to a good prognosis.

PSMA-PET follow-up to assess response in patients not receiving PSMA therapy: Is there value beyond localization of disease?

Introduction: Prostate Specific Membrane Antigen Positron Emission Tomography (PSMA-PET) is routinely used for the staging of patients with prostate cancer, but data on response assessment are sparse and primarily stem from metastatic castration-resistant prostate cancer (mCRPC) patients treated with PSMA radioligand therapy. Still, follow-up PSMA-PET is employed in earlier disease stages in case of clinical suspicion of disease persistence, recurrence or progression to decide if localized or systemic treatment is indicated. Therefore, the prognostic value of PSMA-PET derived tumor volumes in earlier disease stages (i.e., hormone-sensitive prostate cancer (HSPC) and non-[177Lu]Lu-PSMA-617 (LuPSMA) therapy castration resistant prostate cancer (CRPC)) are evaluated in this manuscript. Methods: A total number of 73 patients (6 primary staging, 42 HSPC, 25 CRPC) underwent two (i.e., baseline and follow-up, median interval: 379 days) whole-body [68Ga]Ga-PSMA-11 PET/CT scans between Nov 2014 and Dec 2018. Analysis was restricted to non-LuPSMA therapy patients. PSMA-PETs were retrospectively analyzed and primary tumor, lymph node-, visceral-, and bone metastases were segmented. Body weight-adjusted organ-specific and total tumor volumes (PSMAvol: sum of PET volumes of all lesions) were measured for baseline and follow-up. PSMAvol response was calculated as the absolute difference of whole-body tumor volumes. High metastatic burden (>5 metastases), RECIP 1.0 and PSMA-PET Progression Criteria (PPP) were determined. Survival data were sourced from the cancer registry. Results: The average number of tumor lesions per patient on the initial PET examination was 10.3 (SD 28.4). At baseline, PSMAvol was strongly associated with OS (HR 3.92, p <0.001; n = 73). Likewise, response in PSMAvol was significantly associated with OS (HR 10.48, p < 0.005; n = 73). PPP achieved significance as well (HR 2.19, p <0.05, n = 73). Patients with hormone sensitive disease and poor PSMAvol response (upper quartile of PSMAvol change) in follow-up had shorter outcome (p < 0.05; n = 42). PSMAvol in bones was the most relevant parameter for OS prognostication at baseline and for response assessment (HR 31.11 p < 0.001; HR 32.27, p < 0.001; n = 73). Conclusion: PPP and response in PSMAvol were significantly associated with OS in the present heterogeneous cohort. Bone tumor volume was the relevant miTNM region for OS prognostication. Future prospective evaluation of the performance of organ specific PSMAvol in more homogeneous cohorts seems warranted.

The predictors of lymphopenia and its effects on survival in locally advanced esophageal squamous cell carcinoma.

To investigate the impact of the effective radiation dose to immune cells (EDIC) and gross tumor volume (GTV) on lymphopenia and survival in patients with locally advanced esophageal squamous cell carcinoma (LAESCC). Between January 2013 and December 2020, 272 LAESCC patients were treated with definitive radiotherapy in two institutions. Based on radiation doses to the lungs, heart, and body region scanned, EDIC was calculated as an equal uniform dose to the total blood considering blood flow and fraction effect. The radiotherapy plan was used to calculate the GTVs. Lymphopenia was graded based on the lowest lymphocyte count during RT. The overall survival (OS), progress-free survival (PFS), and local recurrence-free survival (LRFS) were analyzed statistically. The lowest lymphocyte count was significantly correlated with EDIC (r= -0.389, p < .001) and GTV (r= -0.211, p < .001). Lymphopenia, EDIC, and GTV are risk factors for patients with ESCC. In a Kaplan-Meier analysis with EDIC and GTV as stratification factors, lymphopenia was not associated with OS in the EDIC>12.9 Gy group (p = .294)and EDIC ≤ 12.9 Gy group, and it was also not associated with OS in GTV>68.8 cm3 group (p = .242) and GTV ≤ 68.8 cm3 group(p = .165). GTV and EDIC had an impact on the relationship between lymphopenia and OS in patients with LAESCC undergoing definitive RT. Poorer OS, PFS, and LRFS are correlated with lymphopenia, higher EDIC, and larger GTV.

Exploring habitats-based spatial distributions: improving predictions of lymphovascular invasion in invasive breast cancer.

RATIONALE AND OBJECTIVES: Accurate assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) plays a pivotal role in tailoring personalized treatment plans. This study aimed to investigate habitats-based spatial distributions to quantitatively measure tumor heterogeneity on multiparametric magnetic resonance imaging (MRI) scans and assess their predictive capability for LVI in patients with IBC. MATERIALS AND METHODS: In this retrospective cohort study, we consecutively enrolled 241 women diagnosed with IBC between July 2020 and July 2023 and who had 1.5 T/T1-weighted images, fat-suppressed T2-weighted images, and dynamic contrast-enhanced MRI. Habitats-based spatial distributions were derived from the gross tumor volume (GTV) and gross tumor volume plus peritumoral volume (GPTV). GTV_habitats and GPTV_habitats were generated through sub-region segmentation, and their performances were compared. Subsequently, a combined nomogram was developed by integrating relevant spatial distributions with the identified MR morphological characteristics. Diagnostic performance was compared using receiver operating characteristic curve analysis and decision curve analysis. Statistical significance was set at p < 0.05. RESULTS: GPTV_habitats exhibited superior performance compared to GTV_habitats. Consequently, the GPTV_habitats, diffusion-weighted imaging rim signs, and peritumoral edema were integrated to formulate the combined nomogram. This combined nomogram outperformed individual MR morphological characteristics and the GPTV_habitats index, achieving area under the curve values of 0.903 (0.847 -0.959), 0.770 (0.689 -0.852), and 0.843 (0.776 -0.910) in the training set and 0.931 (0.863 -0.999), 0.747 (0.613 -0.880), and 0.849 (0.759 -0.938) in the validation set. CONCLUSION: The combined nomogram incorporating the GPTV_habitats and identified MR morphological characteristics can effectively predict LVI in patients with IBC.

Predictive value of primary tumor volume change during concurrent chemoradiotherapy in patients with unresectable stage III non-small cell lung cancer.

BACKGROUND AND PURPOSE: No established early biomarkers currently exist to predict responses during concurrent chemoradiotherapy (CCRT) in patients with unresectable non-small cell lung cancer (NSCLC). This study investigated the potential of gross tumor volume (GTV) and its changes during CCRT as predictors of survival outcomes. MATERIALS AND METHODS: We identified 227 patients with unresectable stage III NSCLC who underwent definitive CCRT followed by durvalumab between November 2018 and December 2022. GTV was defined as the volume of the primary tumor, assessed at two time points: before starting CCRT for initial planning (GTV1), and at the fourth week of CCRT for adaptive planning (GTV2). Both relative and absolute regressions between GTV1 and GTV2 were calculated. RESULTS: The median GTV1 volume was 90 mL (range, 5-840 mL), and the median GTV2 volume was 64 mL (range, 1-520 mL), resulting in median absolute and relative regressions of 18.6 mL and 25.0 %, respectively. Among the GTV parameters, relative GTV regression exhibited the strongest predictive value, with an area under the curve (AUC) of 0.804 for in-field progression and 0.711 for overall progression. The 1-year progression-free survival rates for the high (>30 %), intermediate (0-30 %), and low (≤0%) relative regression groups were 88.0 %, 62.6 %, and 14.3 %, respectively (p = 0.006 for high vs. intermediate; p < 0.001 for intermediate vs. low). Additionally, GTV2 volume demonstrated stronger associations with survival outcomes than GTV1 volume. CONCLUSION: Relative GTV regression was identified as a promising early predictor for patients with unresectable stage III NSCLC. Further development of a multi-parametric predictive model is warranted to guide patient-tailored therapeutic approaches.

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.

Lymphoma: The Added Value of Radiomics, Volumes and Global Disease Assessment.

Lymphoma represents a condition that holds promise for cure with existing treatment modalities; nonetheless, the primary clinical obstacle lies in advancing therapeutic outcomes by pinpointing high-risk individuals who are unlikely to respond favorably to standard therapy. In this article, the authors will delineate the significant strides achieved in the lymphoma field, with a particular emphasis on the 3 prevalent subtypes: Hodgkin lymphoma, diffuse large B-cell lymphomas, and follicular lymphoma.

A case of lung metastasis from gastric cancer presenting as ground-glass opacity dominant nodules.

BACKGROUND: Most metastatic lung tumors present as solid nodules on chest computed tomography (CT). In contrast, ground-glass opacity on chest computed tomography usually suggests low-grade malignant lesions such as adenocarcinoma in situ or atypical adenomatous hyperplasia of the lung. CASE PRESENTATION: A 75-year-old woman with a history of gastric cancer surgery approximately 5 years prior was referred to the Department of Thoracic Surgery at our hospital because of two newly appearing pulmonary ground-glass opacity-dominant nodules on chest computed tomography. She had two ground-glass opacities in the right lower lobe, one in the S6 segment was 12 mm and the other in the S10 segment was 8 mm. On chest computed tomography 15 months prior to referral, the lesion in the S6 segment was 8 mm, and the lesion in the S10 segment was 2 mm. She was suspected to have primary lung cancer and underwent wide-wedge resection of the nodule in the S6 segment. In the resected specimen, polygonal tumor cells infiltrated the alveolar septa, with some tumor cells exhibiting signet ring cell morphology. Based on morphological similarities to the tumor cells of previous gastric cancers and the results of immunostaining, the patient was diagnosed with lung metastases of gastric cancer. CONCLUSIONS: Pulmonary nodules in patients with a history of cancer in other organs, even if ground-glass opacity is predominant, should also be considered for the possibility of metastatic pulmonary tumors if they are growing rapidly.

Chemotherapeutic potential of lupeol against cancer in pre-clinical model: A systematic review and meta-analysis.

BACKGROUND: Extensive research on Lupeol's potential in cancer prevention highlights its ability to target various cancer-related factors. It regulates proliferative markers, modulates signaling pathways, including PI3K/AKT/mTOR, and influences inflammatory and apoptotic mechanisms. Additionally, Lupeol demonstrates selectivity in killing cancer cells while sparing normal cells, thus minimizing the risk of toxic effects on healthy tissues. HYPOTHESIS: Therefore, we aimed to explore Lupeol's potential roles as a chemotherapeutic agent and as a sensitizer to chemotherapy by reviewing various animal-based studies published on its effects. STUDY DESIGN: We conducted a comprehensive search across databases, including PubMed, PMC, Cochrane, EuroPMC, and ctri.gov.in to identify pertinent articles. Our focus was solely on published animal studies examining Lupeol's anti-cancer effects, with reviewers independently assessing bias risk and resolving discrepancies through consensus. RESULT: 20 studies were shortlisted. The results demonstrated that Lupeol brings changes in the tumor volume by [Hedges's g: -6.62; 95 % CI: -8.68, -4.56; τ2: 24.36, I2: 96.50 %; p < 0.05] and tumor weight by [Hedges's g: -3.97; 95 % CI: -5.20, -2.49; τ2: 2.70, I2: 79.27 %; p <0.05]. The high I2, negative Egger's value, and asymmetrical funnel plot show the publication bias among the studies. Further, Lupeol in combination with other chemotherapeutic agents showed better outcomes as compared to them alone [Hedges's g: -6.38; 95 % CI: -11.82, -0.94; τ2: 46.91; I2: 98.68 %; p <0.05]. Lupeol also targets various signaling molecules and pathways to exert an anti-cancer effect. CONCLUSION: In conclusion, Lupeol significantly reduces tumor volume and weight. Combining Lupeol with other chemotherapy agents shows promise for enhancing anti-cancer effects. However, high variability among studies and evidence of publication bias suggest caution in interpreting results.

Role of Novel Quantitative Imaging Techniques in Hematological Malignancies.

Hematological malignancies exhibit a widespread distribution, necessitating evaluation of disease activity over the entire body. In clinical practice, visual analysis and semiquantitative parameters are used to assess 18F-FDGPET/CT imaging, which solely represents measurements of disease activity from limited area and may not adequately reflect global disease assessment. An efficient method for assessing the global disease burden of hematological malignancies is to employ PET/computed tomography based novel quantitative parameters. In this article, we explored novel quantitative parameters on PET/CT imaging for assessing global disease burden and the potential role of artificial intelligence (AI) to determine these parameters in evaluation of hematological malignancies.

Application of Delta T1 maps for quantitative and objective assessment of extent of resection and survival prediction in glioblastoma.

Background and Objectives: Gross-total resection (GTR) and low residual tumor volume (RTV) have been associated with increased survival in glioblastoma. Largely due to the subjectivity involved, the determination of GTR and RTV remains difficult in the postoperative setting. In response, the objective of this study is to evaluate the clinical efficacy of an easy-to-use MRI metric, called delta T1 (dT1), to quantify extent of resection (EOR) and RTV, in comparison to radiologist impression, to predict overall survival (OS) in glioblastoma patients. Methods: 59 patients who underwent resection of glioblastoma were retrospectively identified. Delta T1 (dT1) images, automatically created from the difference between calibrated post- and pre-contrast T1-weighted images, were used to quantify EOR and RTV. Kaplan-Meier survival estimates were determined for EOR categories, an RTV cutoff of 5cm3 and radiologist interpretation of EOR. Multivariate Cox proportional hazard regression analysis was used to evaluate RTV and EOR along with effects related to sex, KPS, MGMT, and age on OS. Results: Kaplan-Meier analysis revealed a statistically significant difference in median OS for a dT1-determined RTV cutoff of 5 cm3 (P=.0024, HR=2.18 (1.232-3.856)), but not for radiological impression (P=0.666) or dT1-determined EOR (P=0.0803), which was limited to a comparison between partial and subtotal resections. Furthermore, when covariates were accounted for in multivariate Cox regression, significant differences in OS were retained for dT1-determined RTV. Additionally, a significantly strong yet short-term effect of MGMT methylation status on OS was revealed for each RTV and EOR model. Conclusion: The utility of dT1 maps to quantify EOR and RTV in glioblastoma and predict survival, suggests an emerging role for dT1s with relevance for intraoperative MRI, neuro-navigation and postoperative disease surveillance.

Multi-modal segmentation with missing image data for automatic delineation of gross tumor volumes in head and neck cancers.

BACKGROUND: Head and neck (HN) gross tumor volume (GTV) auto-segmentation is challenging due to the morphological complexity and low image contrast of targets. Multi-modality images, including computed tomography (CT) and positron emission tomography (PET), are used in the routine clinic to assist radiation oncologists for accurate GTV delineation. However, the availability of PET imaging may not always be guaranteed. PURPOSE: To develop a deep learning segmentation framework for automated GTV delineation of HN cancers using a combination of PET/CT images, while addressing the challenge of missing PET data. METHODS: Two datasets were included for this study: Dataset I: 524 (training) and 359 (testing) oropharyngeal cancer patients from different institutions with their PET/CT pairs provided by the HECKTOR Challenge; Dataset II: 90 HN patients(testing) from a local institution with their planning CT, PET/CT pairs. To handle potentially missing PET images, a model training strategy named the "Blank Channel" method was implemented. To simulate the absence of a PET image, a blank array with the same dimensions as the CT image was generated to meet the dual-channel input requirement of the deep learning model. During the model training process, the model was randomly presented with either a real PET/CT pair or a blank/CT pair. This allowed the model to learn the relationship between the CT image and the corresponding GTV delineation based on available modalities. As a result, our model had the ability to handle flexible inputs during prediction, making it suitable for cases where PET images are missing. To evaluate the performance of our proposed model, we trained it using training patients from Dataset I and tested it with Dataset II. We compared our model (Model 1) with two other models which were trained for specific modality segmentations: Model 2 trained with only CT images, and Model 3 trained with real PET/CT pairs. The performance of the models was evaluated using quantitative metrics, including Dice similarity coefficient (DSC), mean surface distance (MSD), and 95% Hausdorff Distance (HD95). In addition, we evaluated our Model 1 and Model 3 using the 359 test cases in Dataset I. RESULTS: Our proposed model(Model 1) achieved promising results for GTV auto-segmentation using PET/CT images, with the flexibility of missing PET images. Specifically, when assessed with only CT images in Dataset II, Model 1 achieved DSC of 0.56 ± 0.16, MSD of 3.4 ± 2.1 mm, and HD95 of 13.9 ± 7.6 mm. When the PET images were included, the performance of our model was improved to DSC of 0.62 ± 0.14, MSD of 2.8 ± 1.7 mm, and HD95 of 10.5 ± 6.5 mm. These results are comparable to those achieved by Model 2 and Model 3, illustrating Model 1's effectiveness in utilizing flexible input modalities. Further analysis using the test dataset from Dataset I showed that Model 1 achieved an average DSC of 0.77, surpassing the overall average DSC of 0.72 among all participants in the HECKTOR Challenge. CONCLUSIONS: We successfully refined a multi-modal segmentation tool for accurate GTV delineation for HN cancer. Our method addressed the issue of missing PET images by allowing flexible data input, thereby providing a practical solution for clinical settings where access to PET imaging may be limited.

FET PET-based target volume delineation for the radiotherapy of glioblastoma: A pictorial guide to help overcome methodological pitfalls.

PET is increasingly used for target volume definition in the radiotherapy of glioblastoma, as endorsed by the 2023 ESTRO-EANO guidelines. In view of its growing adoption into clinical practice and upcoming PET-based multi-center trials, this paper aims to assist in overcoming common pitfalls of FET PET-based target delineation in glioblastoma.

Prognostic value of total tumor volume in patients with colorectal liver metastases: A secondary analysis of the randomized CAIRO5 trial with external cohort validation.

BACKGROUND: This study aimed to assess the prognostic value of total tumor volume (TTV) for early recurrence (within 6 months) and overall survival (OS) in patients with colorectal liver metastases (CRLM), treated with induction systemic therapy followed by complete local treatment. METHODS: Patients with initially unresectable CRLM from the multicenter randomized phase 3 CAIRO5 trial (NCT02162563) who received induction systemic therapy followed by local treatment were included. Baseline TTV and change in TTV as response to systemic therapy were calculated using the CT scan before and the first after systemic treatment, and were assessed for their added prognostic value. The findings were validated in an external cohort of patients treated at a tertiary center. RESULTS: In total, 215 CAIRO5 patients were included. Baseline TTV and absolute change in TTV were significantly associated with early recurrence (P = 0.005 and P = 0.040, respectively) and OS in multivariable analyses (P = 0.024 and P = 0.006, respectively), whereas RECIST1.1 was not prognostic for early recurrence (P = 0.88) and OS (P = 0.35). In the validation cohort (n = 85), baseline TTV and absolute change in TTV remained prognostic for early recurrence (P = 0.041 and P = 0.021, respectively) and OS in multivariable analyses (P < 0.0001 and P = 0.012, respectively), and showed added prognostic value over conventional clinicopathological variables (increase C-statistic, 0.06; 95 % CI, 0.02 to 0.14; P = 0.008). CONCLUSION: Total tumor volume is strongly prognostic for early recurrence and OS in patients who underwent complete local treatment of initially unresectable CRLM, both in the CAIRO5 trial and the validation cohort. In contrast, RECIST1.1 did not show prognostic value for neither early recurrence nor OS.

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.

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.