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Mutational processes giving rise to lung adenocarcinomas (LADCs) in non-smokers remain elusive. We analyzed 138 LADC whole genomes, including 83 cases with minimal contribution of smoking-associated mutational signature. Genomic rearrangements were not correlated with smoking-associated mutations and frequently served as driver events of smoking-signature-low LADCs. Complex genomic rearrangements, including chromothripsis and chromoplexy, generated 74% of known fusion oncogenes, including EML4-ALK, CD74-ROS1, and KIF5B-RET. Unlike other collateral rearrangements, these fusion-oncogene-associated rearrangements were frequently copy-number-balanced, representing a genomic signature of early oncogenesis. Analysis of mutation timing revealed that fusions and point mutations of canonical oncogenes were often acquired in the early decades of life. During a long latency, cancer-related genes were disrupted or amplified by complex rearrangements. The genomic landscape was different between subgroups-EGFR-mutant LADCs had frequent whole-genome duplications with p53 mutations, whereas fusion-oncogene-driven LADCs had frequent SETD2 mutations. Our study highlights LADC oncogenesis driven by endogenous mutational processes.
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Adenocarcinoma de Pulmão , Rearranjo Gênico , Neoplasias Pulmonares , Mutação , Proteínas de Fusão Oncogênica , Adenocarcinoma de Pulmão/genética , Adenocarcinoma de Pulmão/metabolismo , Adenocarcinoma de Pulmão/patologia , Feminino , Humanos , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/metabolismo , Neoplasias Pulmonares/patologia , Masculino , Proteínas de Fusão Oncogênica/genética , Proteínas de Fusão Oncogênica/metabolismoRESUMO
OBJECTIVE: To compare nutritional and postoperative outcomes between early oral feeding and late oral feeding with jejunostomy feeding support after esophagectomy. SUMMARY BACKGROUND DATA: Esophagectomy is associated with substantial body weight loss and malnutrition, impacting the prognosis of esophageal cancer patients. Despite many studies on post-esophagectomy nutritional support, optimal strategies remain elusive. This study investigates the impact of jejunostomy feeding with late oral feeding compared to conventional oral feeding on nutritional and postoperative outcomes. METHODS: We performed a single-center prospective open-labelled randomized controlled trial between 2020 and 2022. Patients aged 18 to 75 years with resectable esophageal cancer were randomly assigned to undergo either early oral feeding (early group) or late oral feeding with jejunostomy feeding support (late group) after esophagectomy. The primary endpoint was body weight loss from preoperative body weight at postoperative 4-5 weeks and 4 months. Other perioperative and nutritional outcomes were also evaluated. RESULTS: We randomly assigned 29 patients to the early group and 29 patients to the late group. The late group exhibited significantly less body weight loss at both postoperative 4-5 weeks (8.3% vs. 5.6%; P =0.002) and 4 months (15.0% vs. 10.5%; P =0.003). The total calorie intake and protein intake were higher in the late group for both postoperative 4-5 weeks (1800 kcal/day vs. 1100 kcal/day; P <0.001) and 4 months (1565 kcal/day vs. 1200 kcal/day; P =0.010). Sixty percentage of early group changed to malnutrition state, while 40% of the late group changed to malnutrition. The complication rate and length of hospital stays were similar. CONCLUSIONS: The late group demonstrated prevention of significant body weight loss, enhanced nutritional intake, and reduces malnutrition without compromising short-term surgical outcomes.
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Background Currently, no tool exists for risk stratification in patients undergoing segmentectomy for non-small cell lung cancer (NSCLC). Purpose To develop and validate a deep learning (DL) prognostic model using preoperative CT scans and clinical and radiologic information for risk stratification in patients with clinical stage IA NSCLC undergoing segmentectomy. Materials and Methods In this single-center retrospective study, transfer learning of a pretrained model was performed for survival prediction in patients with clinical stage IA NSCLC who underwent lobectomy from January 2008 to March 2017. The internal set was divided into training, validation, and testing sets based on the assignments from the pretraining set. The model was tested on an independent test set of patients with clinical stage IA NSCLC who underwent segmentectomy from January 2010 to December 2017. Its prognostic performance was analyzed using the time-dependent area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for freedom from recurrence (FFR) at 2 and 4 years and lung cancer-specific survival and overall survival at 4 and 6 years. The model sensitivity and specificity were compared with those of the Japan Clinical Oncology Group (JCOG) eligibility criteria for sublobar resection. Results The pretraining set included 1756 patients. Transfer learning was performed in an internal set of 730 patients (median age, 63 years [IQR, 56-70 years]; 366 male), and the segmentectomy test set included 222 patients (median age, 65 years [IQR, 58-71 years]; 114 male). The model performance for 2-year FFR was as follows: AUC, 0.86 (95% CI: 0.76, 0.96); sensitivity, 87.4% (7.17 of 8.21 patients; 95% CI: 59.4, 100); and specificity, 66.7% (136 of 204 patients; 95% CI: 60.2, 72.8). The model showed higher sensitivity for FFR than the JCOG criteria (87.4% vs 37.6% [3.08 of 8.21 patients], P = .02), with similar specificity. Conclusion The CT-based DL model identified patients at high risk among those with clinical stage IA NSCLC who underwent segmentectomy, outperforming the JCOG criteria. © RSNA, 2024 Supplemental material is available for this article.
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Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Humanos , Masculino , Pessoa de Meia-Idade , Idoso , Carcinoma Pulmonar de Células não Pequenas/diagnóstico por imagem , Carcinoma Pulmonar de Células não Pequenas/cirurgia , Neoplasias Pulmonares/diagnóstico por imagem , Neoplasias Pulmonares/cirurgia , Pneumonectomia , Prognóstico , Estudos Retrospectivos , Tomografia Computadorizada por Raios XRESUMO
PURPOSE: Mediastinal nodal staging is crucial for surgical candidate selection in non-small cell lung cancer (NSCLC), but conventional imaging has limitations often necessitating invasive staging. We investigated the additive clinical value of fibroblast activation protein inhibitor (FAPI) PET/CT, an imaging technique targeting fibroblast activation protein, for mediastinal nodal staging of NSCLC. METHODS: In this prospective pilot study, we enrolled patients scheduled for surgical resection of NSCLC based on specific criteria designed to align with indications for invasive staging procedures. Patients were included when meeting at least one of the following: (1) presence of FDG-positive N2 lymph nodes, (2) clinical N1 stage, (3) central tumor location or tumor diameter of ≥ 3 cm, and (4) adenocarcinoma exhibiting high FDG uptake. [68Ga]FAPI-46 PET/CT was performed before surgery after a staging workup including [18F]FDG PET/CT. The diagnostic accuracy of [68Ga]FAPI-46 PET/CT for "N2" nodes was assessed through per-patient visual assessment and per-station quantitative analysis using histopathologic results as reference standards. RESULTS: Twenty-three patients with 75 nodal stations were analyzed. Histopathologic examination confirmed that nine patients (39.1%) were N2-positive. In per-patient assessment, [68Ga]FAPI-46 PET/CT successfully identified metastasis in eight patients (sensitivity 0.89 (0.52-1.00)), upstaging three patients compared to [18F]FDG PET/CT. [18F]FDG PET/CT detected FDG-avid nodes in six (42.8%) of 14 N2-negative patients. Among them, five were considered non-metastatic based on calcification and distribution pattern, and one was considered metastatic. In contrast, [68Ga]FAPI-46 PET/CT correctly identified all non-metastatic patients solely based on tracer avidity. In per-station analysis, [68Ga]FAPI-46 PET/CT discriminated metastasis more effectively compared to [18F]FDG PET/CT-based (AUC of ROC curve 0.96 (0.88-0.99) vs. 0.68 (0.56-0.78), P < 0.001). CONCLUSION: [68Ga]FAPI-46 PET/CT holds promise as an imaging tool for preoperative mediastinal nodal staging in NSCLC, with improved sensitivity and the potential to reduce false-positive results, optimizing the need for invasive staging procedures.
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Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Mediastino , Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada , Adulto , Idoso , Idoso de 80 Anos ou mais , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Carcinoma Pulmonar de Células não Pequenas/diagnóstico por imagem , Carcinoma Pulmonar de Células não Pequenas/patologia , Neoplasias Pulmonares/diagnóstico por imagem , Neoplasias Pulmonares/patologia , Linfonodos/diagnóstico por imagem , Linfonodos/patologia , Metástase Linfática/diagnóstico por imagem , Mediastino/diagnóstico por imagem , Estadiamento de Neoplasias , Projetos Piloto , Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada/métodos , Período Pré-Operatório , Estudos Prospectivos , QuinolinasRESUMO
BACKGROUND: Identification of lung cancer subtypes is critical for successful treatment in patients, especially those in advanced stages. Many advanced and personal treatments require knowledge of specific mutations, as well as up- and down-regulations of genes, for effective targeting of the cancer cells. While many studies focus on individual cell structures and delve deeper into gene sequencing, the present study proposes a machine learning method for lung cancer classification based on low-magnification cancer outgrowth patterns in a 2D co-culture environment. METHODS: Using a magnetic well plate holder, circular pattern lung cancer cell clusters were generated among fibroblasts, and daily images were captured to monitor cancer outgrowth over a 9-day period. These outgrowth images were then augmented and used to train a convolutional neural network (CNN) model based on the lightweight TinyVGG architecture. The model was trained with pairs of classes representing three subtypes of NSCLC: A549 (adenocarcinoma), H520 (squamous cell carcinoma), and H460 (large cell carcinoma). The objective was to assess whether this lightweight machine learning model could accurately classify the three lung cancer cell lines at different stages of cancer outgrowth. Additionally, cancer outgrowth images of two patient-derived lung cancer cells, one with the KRAS oncogene and the other with the EGFR oncogene, were captured and classified using the CNN model. This demonstration aimed to investigate the translational potential of machine learning-enabled lung cancer classification. RESULTS: The lightweight CNN model achieved over 93% classification accuracy at 1 day of outgrowth among A549, H460, and H520, and reached 100% classification accuracy at 7 days of outgrowth. Additionally, the model achieved 100% classification accuracy at 4 days for patient-derived lung cancer cells. Although these cells are classified as Adenocarcinoma, their outgrowth patterns vary depending on their oncogene expressions (KRAS or EGFR). CONCLUSIONS: These results demonstrate that the lightweight CNN architecture, operating locally on a laptop without network or cloud connectivity, can effectively create a machine learning-enabled model capable of accurately classifying lung cancer cell subtypes, including those derived from patients, based upon their outgrowth patterns in the presence of surrounding fibroblasts. This advancement underscores the potential of machine learning to enhance early lung cancer subtyping, offering promising avenues for improving treatment outcomes in advanced stage-patients.
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Técnicas de Cocultura , Fibroblastos , Neoplasias Pulmonares , Aprendizado de Máquina , Redes Neurais de Computação , Humanos , Neoplasias Pulmonares/patologia , Neoplasias Pulmonares/genética , Linhagem Celular Tumoral , Técnicas de Cocultura/métodos , Carcinoma Pulmonar de Células não Pequenas/genética , Carcinoma Pulmonar de Células não Pequenas/patologiaRESUMO
OBJECTIVES: The prognostic value of ground-glass opacity at preoperative chest CT scans in early-stage lung adenocarcinomas is a matter of debate. We aimed to clarify the existing evidence through a single-center, retrospective cohort study and to quantitatively summarize the body of literature by conducting a meta-analysis. METHODS: In a retrospective cohort study, patients with clinical stage I lung adenocarcinoma were identified, and the prognostic value of ground-glass opacity was analyzed using multivariable Cox regression. Commercial artificial intelligence software was adopted as the second reader for the presence of ground-glass opacity. The primary end points were freedom from recurrence (FFR) and lung cancer-specific survival (LCSS). In a meta-analysis, we systematically searched Embase and OVID-MEDLINE up to December 30, 2021, for the studies based on the eighth-edition staging system. The pooled hazard ratios (HRs) of solid nodules (i.e., absence of ground-glass opacity) for various end points were calculated with a multi-level random effects model. RESULTS: In a cohort of 612 patients, solid nodules were associated with worse outcomes for FFR (adjusted HR, 1.98; 95% CI: 1.17-3.51; p = 0.01) and LCSS (adjusted HR, 1.937; 95% CI: 1.002-4.065; p = 0.049). The artificial intelligence assessment and multiple sensitivity analyses revealed consistent results. The meta-analysis included 13 studies with 12,080 patients. The pooled HR of solid nodules was 2.13 (95% CI: 1.69-2.67; I2 = 30.4%) for overall survival, 2.45 (95% CI: 1.52-3.95; I2 = 0.0%) for FFR, and 2.50 (95% CI: 1.28-4.91; I2 = 30.6%) for recurrence-free survival. CONCLUSIONS: The absence of ground-glass opacity in early-stage lung adenocarcinomas is associated with worse postoperative survival. CLINICAL RELEVANCE STATEMENT: Early-stage lung adenocarcinomas manifesting as solid nodules at preoperative chest CT, which indicates the absence of ground-glass opacity, were associated with poor postoperative survival. There is room for improvement of the clinical T categorization in the next edition staging system. KEY POINTS: ⢠In a retrospective study of 612 patients with stage I lung adenocarcinoma, solid nodules were associated with shorter freedom from recurrence (adjusted hazard ratio [HR], 1.98; p = 0.01) and lung cancer-specific survival (adjusted HR, 1.937; p = 0.049). ⢠Artificial intelligence-assessed solid nodules also showed worse prognosis (adjusted HR for freedom from recurrence, 1.94 [p = 0.01]; adjusted HR for lung cancer-specific survival, 1.93 [p = 0.04]). ⢠In meta-analyses, the solid nodules were associated with shorter freedom from recurrence (HR, 2.45) and shorter overall survival (HR, 2.13).
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Adenocarcinoma de Pulmão , Neoplasias Pulmonares , Humanos , Prognóstico , Estudos Retrospectivos , Inteligência Artificial , Estadiamento de Neoplasias , Adenocarcinoma de Pulmão/patologia , Neoplasias Pulmonares/patologia , Tomografia Computadorizada por Raios X/métodosRESUMO
OBJECTIVES: To analyze the diagnostic performance and prognostic value of CT-defined visceral pleural invasion (CT-VPI) in early-stage lung adenocarcinomas. METHODS: Among patients with clinical stage I lung adenocarcinomas, half of patients were randomly selected for a diagnostic study, in which five thoracic radiologists determined the presence of CT-VPI. Probabilities for CT-VPI were obtained using deep learning (DL). Areas under the receiver operating characteristic curve (AUCs) and binary diagnostic measures were calculated and compared. Inter-rater agreement was assessed. For all patients, the prognostic value of CT-VPI by two radiologists and DL (using high-sensitivity and high-specificity cutoffs) was investigated using Cox regression. RESULTS: In 681 patients (median age, 65 years [interquartile range, 58-71]; 382 women), pathologic VPI was positive in 130 patients. For the diagnostic study (n = 339), the pooled AUC of five radiologists was similar to that of DL (0.78 vs. 0.79; p = 0.76). The binary diagnostic performance of radiologists was variable (sensitivity, 45.3-71.9%; specificity, 71.6-88.7%). Inter-rater agreement was moderate (weighted Fleiss κ, 0.51; 95%CI: 0.43-0.55). For overall survival (n = 680), CT-VPI by radiologists (adjusted hazard ratio [HR], 1.27 and 0.99; 95%CI: 0.84-1.92 and 0.63-1.56; p = 0.26 and 0.97) or DL (HR, 1.44 and 1.06; 95%CI: 0.86-2.42 and 0.67-1.68; p = 0.17 and 0.80) was not prognostic. CT-VPI by an attending radiologist was prognostic only in radiologically solid tumors (HR, 1.82; 95%CI: 1.07-3.07; p = 0.03). CONCLUSION: The diagnostic performance and prognostic value of CT-VPI are limited in clinical stage I lung adenocarcinomas. This feature may be applied for radiologically solid tumors, but substantial reader variability should be overcome. CLINICAL RELEVANCE STATEMENT: Although the diagnostic performance and prognostic value of CT-VPI are limited in clinical stage I lung adenocarcinomas, this parameter may be applied for radiologically solid tumors with appropriate caution regarding inter-reader variability. KEY POINTS: ⢠Use of CT-defined visceral pleural invasion in clinical staging should be cautious, because prognostic value of CT-defined visceral pleural invasion remains unexplored. ⢠Diagnostic performance and prognostic value of CT-defined visceral pleural invasion varied among radiologists and deep learning. ⢠Role of CT-defined visceral pleural invasion in clinical staging may be limited to radiologically solid tumors.
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Adenocarcinoma de Pulmão , Neoplasias Pulmonares , Idoso , Feminino , Humanos , Adenocarcinoma de Pulmão/diagnóstico por imagem , Adenocarcinoma de Pulmão/patologia , Neoplasias Pulmonares/patologia , Estadiamento de Neoplasias , Pleura/diagnóstico por imagem , Pleura/patologia , Prognóstico , Tomografia Computadorizada por Raios X , Masculino , Pessoa de Meia-IdadeRESUMO
Deciphering the cellular composition in genome-wide spatially resolved transcriptomic data is a critical task to clarify the spatial context of cells in a tissue. In this study, we developed a method, CellDART, which estimates the spatial distribution of cells defined by single-cell level data using domain adaptation of neural networks and applied it to the spatial mapping of human lung tissue. The neural network that predicts the cell proportion in a pseudospot, a virtual mixture of cells from single-cell data, is translated to decompose the cell types in each spatial barcoded region. First, CellDART was applied to a mouse brain and a human dorsolateral prefrontal cortex tissue to identify cell types with a layer-specific spatial distribution. Overall, the proposed approach showed more stable and higher accuracy with short execution time compared to other computational methods to predict the spatial location of excitatory neurons. CellDART was capable of decomposing cellular proportion in mouse hippocampus Slide-seq data. Furthermore, CellDART elucidated the cell type predominance defined by the human lung cell atlas across the lung tissue compartments and it corresponded to the known prevalent cell types. CellDART is expected to help to elucidate the spatial heterogeneity of cells and their close interactions in various tissues.
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Redes Neurais de Computação , Análise de Célula Única , Transcriptoma , Animais , Encéfalo/citologia , Humanos , Pulmão/citologia , CamundongosRESUMO
Robotic esophagectomy has improved early outcomes and enhanced the quality of lymphadenectomy for esophageal cancer surgery. This study aimed to determine risk factors for long-term survival following robotic esophagectomy and the causes of long-term mortality. We included patients who underwent robotic esophagectomy at our institute between 2010 and 2022. Robotic esophagectomy was defined as a surgical procedure performed robotically in both the abdomen and thorax. Robotic esophagectomy was performed in patients at all stages, including advanced stages, even in patients with stage IV and supraclavicular lymph node metastasis. A total of 340 patients underwent robotic esophagectomy during the study period. Ivor-Lewis operation and McKeown operation were performed on 153 (45.0%) and 187 (55.0%) patients, respectively. The five-year survival rates based on clinical stages were as follows: 85.2% in stage I, 62.0% in stage II, 54.5% in stage III, and 40.3% in stage IV. Risk factors for long-term survival included body mass index, Charlson comorbidity index, clinical stages, and postoperative complications of grade 4 or higher. Among the cases of long-term mortality, recurrence accounted for 42 patients (61.7%), while non-cancer-related death occurred in 26 patients (38.2%). The most common cause of non-cancer-related death was malnutrition and poor general condition, observed in 11 patients (16.2%). Robotic esophagectomy has demonstrated the ability to achieve acceptable long-term survival rates, even in patients with cervical lymph node metastasis. However, addressing high-grade postoperative complications and long-term malnutrition remains crucial for further improving the long-term survival outcomes of patients with esophageal cancer.
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Introduction Cancers in general, and specifically lung cancer, continue to have low patient survival rates when the patient is at an advanced stage when diagnosed. It appears that the local environment, especially fibroblasts and their signaling molecules, tends to induce metastasis, increase cancer cell resistance to treatment, and aid in tumor growth rates. Since 3-D models quickly become too complex and/or expensive, and therefore rarely leave the lab they are developed in, it is interesting to develop a 2-D model that more closely mimics the clustered tumor formation and bulk interaction with a surrounding fibroblast environment. Methods In the present study, we utilize an off-the-shelf stereolithography 3-D printer, standard use well plates, magnets, and metallic beads to create a customizable 2-D co-culture system capable of being analyzed quantitatively with staining and qualitatively with standard fluorescent/brightfield microscopy to determine cancer-fibroblast interactions while also being able to test chemotherapeutic drugs in a high-throughput manner with standard 96-well plates. Results Comparisons from monoculture and co-culture growth rates shows that the presence of fibroblasts allows for significantly increased growth rates for H460 cancer. Additionally, viability of cancer cells can be quantified with simple cell staining methods and morphology and cell-cell interactions can be observed and studied. Discussion The high throughput model demonstrates that boundary condition changes can be observed between cancer cells and fibroblasts based upon the different chemotherapeutics that have been administered.
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BACKGROUND: 99mTc-MAA accumulation within the tumor representing pulmonary arterial perfusion, which is variable and may have a clinical significance. We evaluated the prognostic significance of 99mTc-MAA distribution within the tumor in non-small cell lung cancer (NSCLC) patients in terms of detecting occult nodal metastasis and lymphovascular invasion, as well as predicting the recurrence-free survival (RFS). METHODS: Two hundred thirty-nine NSCLC patients with clinical N0 status who underwent preoperative lung perfusion SPECT/CT were retrospectively evaluated and classified according to the visual grading of 99mTc-MAA accumulation in the tumor. Visual grade was compared with the quantitative parameter, standardized tumor to lung ratio (TLR). The predictive value of 99mTc-MAA accumulation with occult nodal metastasis, lymphovascular invasion, and RFS was assessed. RESULTS: Eighty-nine (37.2%) patients showed 99mTc-MAA accumulation and 150 (62.8%) patients showed the defect on 99mTc-MAA SPECT/CT. Among the accumulation group, 45 (50.5%) were classified as grade 1, 40 (44.9%) were grade 2, and 4 (4.5%) were grade 3. TLR gradually and significantly increased from grade 0 (0.009 ± 0.005) to grade 1 (0.021 ± 0.005, P < 0.05) and to grade 2-3 (0.033 ± 0.013, P < 0.05). The following factors were significant predictors for occult nodal metastasis in univariate analysis: central location, histology different from adenocarcinoma, tumor size greater than 3 cm representing clinical T2 or higher, and the absence of 99mTc-MAA accumulation within the tumor. Defect in the lung perfusion SPECT/CT remained significant at the multivariate analysis (Odd ratio 3.25, 95%CI [1.24 to 8.48], p = 0.016). With a median follow-up of 31.5 months, the RFS was significantly shorter in the defect group (p = 0.008). Univariate analysis revealed that cell type of non-adenocarcinoma, clinical stage II-III, pathologic stage II-III, age greater than 65 years, and the 99mTc-MAA defect within tumor as significant predictors for shorter RFS. However, only the pathologic stage remained statistically significant, in multivariate analysis. CONCLUSION: The absence of 99mTc-MAA accumulation within the tumor in preoperative lung perfusion SPECT/CT represents an independent risk factor for occult nodal metastasis and is relevant as a poor prognostic factor in clinically N0 NSCLC patients. 99mTc-MAA tumor distribution may serve as a new imaging biomarker reflecting tumor vasculatures and perfusion which can be associated with tumor biology and prognosis.
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Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Humanos , Idoso , Carcinoma Pulmonar de Células não Pequenas/diagnóstico por imagem , Carcinoma Pulmonar de Células não Pequenas/cirurgia , Metástase Linfática , Estudos Retrospectivos , Neoplasias Pulmonares/diagnóstico por imagem , Neoplasias Pulmonares/cirurgia , Neoplasias Pulmonares/patologia , Tomografia Computadorizada de Emissão de Fóton Único/métodos , Tomografia Computadorizada por Raios X/métodos , Pulmão/patologia , Perfusão , Compostos RadiofarmacêuticosRESUMO
OBJECTIVES: To develop and validate CT-based deep learning (DL) models that learn morphological and histopathological features for lung adenocarcinoma prognostication, and to compare them with a previously developed DL discrete-time survival model. METHODS: DL models were trained to simultaneously predict five morphological and histopathological features using preoperative chest CT scans from patients with resected lung adenocarcinomas. The DL score was validated in temporal and external test sets, with freedom from recurrence (FFR) and overall survival (OS) as outcomes. Discrimination was evaluated using the time-dependent area under the receiver operating characteristic curve (TD-AUC) and compared with the DL discrete-time survival model. Additionally, we performed multivariable Cox regression analysis. RESULTS: In the temporal test set (640 patients; median age, 64 years), the TD-AUC was 0.79 for 5-year FFR and 0.73 for 5-year OS. In the external test set (846 patients; median age, 65 years), the TD-AUC was 0.71 for 5-year OS, equivalent to the pathologic stage (0.71 vs. 0.71 [p = 0.74]). The prognostic value of the DL score was independent of clinical factors (adjusted per-percentage hazard ratio for FFR (temporal test), 1.02 [95% CI: 1.01-1.03; p < 0.001]; OS (temporal test), 1.01 [95% CI: 1.002-1.02; p = 0.01]; OS (external test), 1.01 [95% CI: 1.005-1.02; p < 0.001]). Our model showed a higher TD-AUC than the DL discrete-time survival model, but without statistical significance (2.5-year OS: 0.73 vs. 0.68; p = 0.13). CONCLUSION: The CT-based prognostic score from collective deep learning of morphological and histopathological features showed potential in predicting survival in lung adenocarcinomas. CLINICAL RELEVANCE STATEMENT: Collective CT-based deep learning of morphological and histopathological features presents potential for enhancing lung adenocarcinoma prognostication and optimizing pre-/postoperative management. KEY POINTS: ⢠A CT-based prognostic model was developed using collective deep learning of morphological and histopathological features from preoperative CT scans of 3181 patients with resected lung adenocarcinoma. ⢠The prognostic performance of the model was comparable-to-higher performance than the pathologic T category or stage. ⢠Our approach yielded a higher discrimination performance than the direct survival prediction model, but without statistical significance (0.73 vs. 0.68; p=0.13).
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This corrects the article on p. e294 in vol. 37, PMID: 36281485.
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AIM: Sentinel lymph node (SLN) mapping allows node-negative patients to be spared from the surgical comorbidities associated with total lymphadenectomy. This study aimed to evaluate the oncological outcomes of SLN biopsy versus complete lymph node dissection in patients with early-stage endometrial carcinoma. METHODS: Retrospective analyses were performed in patients with pathologically confirmed endometrioid endometrial carcinoma, who underwent minimally invasive surgical staging with SLN biopsy or complete lymph node dissection at Yonsei Cancer Center between 2015 and 2019. RESULTS: A total of 301 patients were included in this study. Eighty-two patients underwent SLN biopsy, while 219 underwent complete lymph node dissection. There were no significant differences in patient characteristics between the two groups. In terms of operative characteristics, the SLN biopsy-only group had a significantly shorter surgical duration (p < 0.001) than the lymphadenectomy group. The mean follow-up period was 41.4 months. There were no differences in progression-free survival (PFS) and overall survival (OS) between the two groups (SLN biopsy vs. complete lymph node dissection; p = 0.798 and 0.301, respectively). Multivariate analysis revealed that SLN biopsy was not an independent prognostic factor for PFS or OS. CONCLUSION: Our results showed that SLN biopsy provided oncological outcomes similar to those of lymphadenectomy.
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Neoplasias do Endométrio , Linfonodo Sentinela , Feminino , Humanos , Biópsia de Linfonodo Sentinela , Linfonodos/patologia , Linfonodo Sentinela/patologia , Linfonodo Sentinela/cirurgia , Estudos Retrospectivos , Estadiamento de Neoplasias , Excisão de Linfonodo/métodos , Neoplasias do Endométrio/cirurgia , Neoplasias do Endométrio/patologiaRESUMO
Background A preoperative CT-based deep learning (DL) prediction model was proposed to estimate disease-free survival in patients with resected lung adenocarcinoma. However, the black-box nature of DL hinders interpretation of its results. Purpose To provide histopathologic evidence underpinning the DL survival prediction model and to demonstrate the feasibility of the model in identifying patients with histopathologic risk factors through unsupervised clustering and a series of regression analyses. Materials and Methods For this retrospective study, data from patients who underwent curative resection for lung adenocarcinoma without neoadjuvant therapy from January 2016 to September 2020 were collected from a tertiary care center. Seven histopathologic risk factors for the resected adenocarcinoma were documented: the aggressive adenocarcinoma subtype (cribriform, morular, solid, or micropapillary-predominant subtype); mediastinal nodal metastasis (pN2); presence of lymphatic, venous, and perineural invasion; visceral pleural invasion (VPI); and EGFR mutation status. Unsupervised clustering using 80 DL model-driven CT features was performed, and associations between the patient clusters and the histopathologic features were analyzed. Multivariable regression analyses were performed to investigate the added value of the DL model output to the semantic CT features (clinical T category and radiologic nodule type [ie, solid or subsolid]) for histopathologic associations. Results A total of 1667 patients (median age, 64 years [IQR, 57-71 years]; 975 women) were evaluated. Unsupervised patient clusters 3 and 4 were associated with all histopathologic risk factors (P < .01) except for EGFR mutation status (P = .30 for cluster 3). After multivariable adjustment, model output was associated with the aggressive adenocarcinoma subtype (odds ratio [OR], 1.03; 95% CI: 1.002, 1.05; P = .03), venous invasion (OR, 1.03; 95% CI: 1.004, 1.06; P = .02), and VPI (OR, 1.08; 95% CI: 1.06, 1.10; P < .001), independently of the semantic CT features. Conclusion The deep learning model extracted CT imaging surrogates for the histopathologic profiles of lung adenocarcinoma. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Yanagawa in this issue.
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Adenocarcinoma de Pulmão , Adenocarcinoma , Aprendizado Profundo , Neoplasias Pulmonares , Feminino , Humanos , Pessoa de Meia-Idade , Adenocarcinoma/patologia , Adenocarcinoma de Pulmão/diagnóstico por imagem , Adenocarcinoma de Pulmão/patologia , Receptores ErbB/genética , Estudos Retrospectivos , Tomografia Computadorizada por Raios X/métodos , Masculino , IdosoRESUMO
Background Although various guidelines discourage performing brain MRI for staging purposes in asymptomatic patients with clinical stage IA non-small cell lung cancer (NSCLC), evidence regarding their postoperative survival is lacking. Purpose To investigate the survival benefit of performing brain MRI in asymptomatic patients with early-stage NSCLC. Materials and Methods Patients who underwent curative resection between February 2009 and March 2016 for clinical TNM stage T1N0M0 NSCLC were retrospectively included. Patient survival and development of brain metastasis during postoperative surveillance were documented. The cumulative survival rate and incidence of brain metastasis were compared between patients who underwent surgery with or without staging brain MRI by using Cox regression and a Fine-Gray subdistribution hazard model, respectively, for multivariable adjustment. Propensity score matching and inverse probability of treatment weighting were applied for confounder adjustment. Results A total of 628 patients (mean age, 64 years ± 10 [SD]; 319 men) were included, of whom 53% (331 of 628) underwent staging brain MRI. In the multivariable analyses, brain MRI did not show prognostic benefits for brain metastasis-free survival (hazard ratio [HR], 1.06; 95% CI: 0.69, 1.63; P = .79), time to brain metastasis (HR, 1.60; 95% CI: 0.70, 3.94; P = .29), and overall survival (HR, 0.86; 95% CI, 0.54, 1.37; P = .54). Consistent results were obtained after propensity score matching (brain metastasis-free survival [HR, 0.97; 95% CI: 0.60, 1.57; P = .91], time to brain metastasis [HR, 1.29; 95% CI: 0.50, 3.33; P = .60], and overall survival [HR, 0.89; 95% CI: 0.53, 1.51; P = .67]) and inverse probability of treatment weighting. Conclusion No difference was observed between asymptomatic patients with clinical stage IA non-small cell lung cancer who underwent staging brain MRI and those who did not in terms of brain metastasis-free survival, time to brain metastasis, and overall survival. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Bizzi and Pascuzzo in this issue.
Assuntos
Neoplasias Encefálicas , Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Encéfalo/patologia , Neoplasias Encefálicas/patologia , Carcinoma Pulmonar de Células não Pequenas/diagnóstico por imagem , Carcinoma Pulmonar de Células não Pequenas/cirurgia , Humanos , Neoplasias Pulmonares/patologia , Imageamento por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Estadiamento de Neoplasias , Prognóstico , Estudos RetrospectivosRESUMO
BACKGROUND: Relationship between cancer cell glycolysis and the landscape of tumor immune microenvironment in human cancers was investigated. METHODS: Forty-one fresh lung adenocarcinoma (ADC) tissues were analyzed using flow cytometry for comprehensive immunoprofiling. Formalin-fixed tissues were immunostained for hexokinase-2 (HK2) to assess cancer cell glycolysis. For validation, formalin-fixed tissues from 375 lung ADC, 118 lung squamous cell carcinoma (SqCC), 338 colon ADC, and 78 lung cancer patients treated with anti-PD-1/PD-L1 immunotherapy were immunostained for HK2, CD8, and FOXP3. RESULTS: Based on immunoprofiling of lung ADC, HK2 tumor expression was associated with the composition of lymphoid cells rather than myeloid cells. High HK2 tumor expression was associated with immunosuppressive/pro-tumorigenic features, especially decreased ratio of CD8 + T-cells to Tregs (rho = -0.415, P = 0.012). This correlation was also confirmed in four different cohorts including lung ADC and SqCC, colon ADC, and the immunotherapy cohort (rho = -0.175~-0.335, all P < 0.05). A low CD8 + T-cell to Treg ratio was associated with poor progression-free survival and overall survival in lung SqCC patients, and a shorter overall survival in the immunotherapy cohort (all, P < 0.05). CONCLUSION: An increase in HK2 expression may contribute to shaping the immunosuppressive/pro-tumorigenic tumor microenvironment by modulating the CD8 + T-cell to Treg ratio. Targeting tumor HK2 expression might be a potential strategy for enhancing anti-tumor immunity.
Assuntos
Adenocarcinoma de Pulmão , Carcinoma Pulmonar de Células não Pequenas , Carcinoma de Células Escamosas , Neoplasias Pulmonares , Humanos , Linfócitos T Reguladores , Hexoquinase/metabolismo , Carcinoma Pulmonar de Células não Pequenas/patologia , Antígeno B7-H1/metabolismo , Neoplasias Pulmonares/patologia , Linfócitos T CD8-Positivos , Adenocarcinoma de Pulmão/metabolismo , Microambiente Tumoral , Carcinoma de Células Escamosas/metabolismo , Carcinogênese/metabolismo , Formaldeído , Linfócitos do Interstício TumoralRESUMO
OBJECTIVES: Long term outcomes following fertility sparing robot-assisted radical trachelectomy (RRT). METHODS: A retrospective study of consecutive women selected for RRT between 2007 and 2019 at five referral centres. Generally used selection criteria for fertility-sparing surgery were applied. Oncologic, reproductive and long-term clinical data were analysed. RESULTS: Of the 166 included women, 149 completed a RRT. Median tumor size was 9 mm (range 3-20 mm), 111 women (75%) had FIGO 2009 stage IB1 cancer and 4.8% were node positive. At a median follow up of 58 months, 12 of all women (7.2%) and 9 of 149 women (6%) who underwent completed RRT with fertility preservation had recurred and two had died. 70 of 88 women (80%) who attempted to conceive succeeded, resulting in 81 pregnancies that progressed beyond the first trimester and 76 live births of which 54 (70%) were delivered at term and 65 (86%) delivered after gestational week 32. A short postoperative cervical length was associated with impaired fertility. A late secondary hysterectomy was necessary in four women due to persistent bleeding (n = 2), hematometra due to a cervical stenosis (n = 1) and recurrent dysplasia (n = 1). CONCLUSION: In this long-term follow-up of RRT the recurrence rate is comparable to larger individual studies of minimally invasive or vaginal radical trachelectomy with similar risk profile and follow up. The high pregnancy rate and low rate of premature delivery before 32 weeks GA may promote the use of robot-assisted approach.
Assuntos
Preservação da Fertilidade , Robótica , Traquelectomia , Neoplasias do Colo do Útero , Feminino , Preservação da Fertilidade/métodos , Humanos , Masculino , Estadiamento de Neoplasias , Gravidez , Estudos Retrospectivos , Traquelectomia/efeitos adversos , Traquelectomia/métodos , Neoplasias do Colo do Útero/patologia , Neoplasias do Colo do Útero/cirurgiaRESUMO
OBJECTIVE: We investigated the utility of Positron emission tomography-Computed tomography (PET-CT) in the setting of two different sentinel lymph node (SLN) mapping techniques; the conventional cervical injection method (one-step) and the two-step method, which involves fundal injection followed by cervical injection. METHODS: Patients with endometrial cancer undergoing FDG PET-CT followed by laparoscopic or robotic surgical staging with SLN mapping at the Yonsei Cancer Center between July 2014 and April 2021 were stratified into the PET-positive group (with suspected or likely lymph nodes metastasis) and PET-negative group. A chart review was performed for the number of harvested SLNs, patterns of SLN metastases, and recurrence. RESULTS: Among 466 patients undergoing one-step (n = 276) and two-step (n = 190) SLN mapping, LN metastasis was identified in 21 of 434 PET-negative and 18 of 32 PET-positive patients. The sensitivity and specificity of PET-CT for diagnosing lymph node metastasis were 46.2% and 96.7%, respectively. Among PET-positive patients with LN metastasis, anatomical distribution was concordant in 14/18 patients (77.8%). Among PET-negative patients, four (2.3%) had metastatic para-aortic SLNs, including three (1.7%) with isolated para-aortic metastases; metastatic para-aortic SLNs were exclusively found in the two-step group. Among PET-positive patients, para-aortic SLN metastasis was identified in 35.7% of two-step and 16.7% of one-step group. Among the 21 PET false-negative patients, recurrence was seen in four patients (19%) after a median follow-up of 34 months (range: 7-70 months). CONCLUSIONS: PET-CT served as a useful guide to clinicians with high anatomical concordance rate in patients with LN metastasis. However, despite high specificity, sensitivity was limited. SLN metastasis pattern, especially at the para-aortic level, indicates that the two-step SLN technique might be useful in PET-negative and PET-positive patients.
Assuntos
Neoplasias do Endométrio , Linfonodo Sentinela , Neoplasias do Endométrio/diagnóstico por imagem , Neoplasias do Endométrio/patologia , Neoplasias do Endométrio/cirurgia , Feminino , Humanos , Excisão de Linfonodo , Linfonodos/diagnóstico por imagem , Linfonodos/patologia , Linfonodos/cirurgia , Metástase Linfática/diagnóstico por imagem , Metástase Linfática/patologia , Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada/métodos , Linfonodo Sentinela/diagnóstico por imagem , Linfonodo Sentinela/patologia , Linfonodo Sentinela/cirurgia , Biópsia de Linfonodo Sentinela/métodosRESUMO
OBJECTIVE: This study aimed to determine the incidence of thrombotic events in ovarian cancer patients following a de-escalated prophylactic strategy and to stratify risk groups. METHODS: We reviewed the records of patients who underwent debulking surgery for ovarian cancer at a single institution between January 2007 and May 2019. We identified clinically diagnosed and radiologically confirmed cases of thrombotic events-classified as pulmonary thromboembolism (PE), deep vein thrombosis (DVT), and other thrombotic events-within 6 months of debulking surgery. RESULTS: After excluding 13 patients diagnosed with thromboembolism at the baseline or during neoadjuvant chemotherapy, 799 were analyzed. Since the introduction of medical prophylaxis at our institution in 2009, 482 patients (60%) received medical prophylaxis with subcutaneous injection of low molecular weight heparin for 5 days with mechanical prophylaxis, whereas 317 (40%) received mechanical prophylaxis only. After debulking surgery, thrombotic events occurred in 28 patients (3.5%) including PE (n = 11), DVT (n = 10), and other thrombotic events (n = 7). Multivariable analysis identified age, body mass index (BMI), and operative duration as independent risk factors associated with thrombotic events. A thrombotic event was an independent prognostic factor for overall survival (HR 2.17, 95% CI 1.16-4.1). A cut-off analysis for pre-operative identifiable risk factors showed age < 57 years and BMI < 21 could help define low-risk groups. One patient from 172 low-risk patients (0.58%) experienced a thrombotic event. CONCLUSIONS: The thrombotic event incidence was low in our cohort. A de-escalated prophylaxis strategy may be considered in young (age < 57 years) and lean (BMI < 21) patients.