With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia. Therefore, we developed a deep learning (DL) model to establish the associations between testicular grayscale ultrasound images and testicular histology. We retrospectively included two-dimensional testicular grayscale ultrasound from patients with azoospermia (353 men with 4357 images between July 2017 and December 2021 in The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China) to develop a DL model. We obtained testicular histology during conventional testicular sperm extraction. Our DL model was trained based on ultrasound images or fusion data (ultrasound images fused with the corresponding testicular volume) to distinguish spermatozoa presence in pathology (SPP) and spermatozoa absence in pathology (SAP) and to classify maturation arrest (MA) and Sertoli cell-only syndrome (SCOS) in patients with SAP. Areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to analyze model performance. DL based on images achieved an AUC of 0.922 (95% confidence interval [CI]: 0.908-0.935), a sensitivity of 80.9%, a specificity of 84.6%, and an accuracy of 83.5% in predicting SPP (including normal spermatogenesis and hypospermatogenesis) and SAP (including MA and SCOS). In the identification of SCOS and MA, DL on fusion data yielded better diagnostic performance with an AUC of 0.979 (95% CI: 0.969-0.989), a sensitivity of 89.7%, a specificity of 97.1%, and an accuracy of 92.1%. Our study provides a noninvasive method to predict testicular histology for patients with azoospermia, which would avoid unnecessary testicular biopsy.
Humans ; Male ; Azoospermia/diagnostic imaging* ; Deep Learning ; Testis/pathology* ; Retrospective Studies ; Adult ; Ultrasonography/methods* ; Sperm Retrieval ; Sertoli Cell-Only Syndrome/diagnostic imaging*

The aim of this investigation was to determine the optimal storage medium for testicular hypothermic transportation and identify the ideal concentration for the application of the protective agent 5-aminolevulinic acid (5-ALA). Furthermore, this study aimed to explore the underlying mechanism of the protective effects of 5-ALA. First, we collected and stored mouse testicular fragments in different media, including Hank's balanced salt solution (HBSS; n = 5), Dulbecco's Modified Eagle Medium/Nutrient Mixture F-12 (DMEM/F12; n = 5), and alpha-minimum essential medium (αMEM; n = 5). Storage of testicular tissue in HBSS preserved the integrity of testicular morphology better than that in the DMEM/F12 group ( P < 0.05) and the αMEM group ( P < 0.01). Testicular fragments were subsequently placed in HBSS with various concentrations of 5-ALA (0 [control], 1 mmol l -1 , 2 mmol l -1 , and 5 mmol l -1 ) to determine the most effective concentration of 5-ALA. The 2 mmol l -1 5-ALA group ( n = 3) presented the highest positive rate of spermatogonial stem cells compared with those in the control, 1 mmol l -1 , and 5 mmol l -1 5-ALA groups. Finally, the tissue fragments were preserved in HBSS with control ( n = 3) and 2 mmol l -1 5-ALA ( n = 3) under low-temperature conditions. A comparative analysis was performed against fresh testes ( n = 3) to elucidate the underlying mechanism of 5-ALA. Gene set enrichment analysis (GSEA) for WikiPathways revealed that the p38 mitogen-activated protein kinase (MAPK) signaling pathway was downregulated in the 2 mmol l -1 5-ALA group compared with that in the control group (normalized enrichment score [NES] = -1.57, false discovery rate [FDR] = 0.229, and P = 0.019). In conclusion, these data suggest that using 2 mmol l -1 5-ALA in HBSS effectively protected the viability of spermatogonial stem cells upon hypothermic transportation.
Male ; Animals ; Testis/cytology* ; Aminolevulinic Acid/pharmacology* ; Mice ; Organ Preservation/methods* ; Organ Preservation Solutions/pharmacology* ; Cryopreservation/methods*

OBJECTIVE: To investigate the clinical characteristics and prognostic of venetoclax (VEN) combined targeted therapy in acute leukemia (AL) patients with PICALM∷MLLT10 fusion gene positivity. METHODS: A retrospective analysis was conducted on 16 PICALM∷MLLT10-positive AL patients treated at the First Affiliated Hospital of Soochow University from January 2021 to August 2024. These patients were diagnosed by targeted RNA sequencing (RNA-seq) or reverse transcription multiplex PCR, including newly diagnosed and relapsed/refractory (R/R) cases. The immunophenotypes, genetic features, gene mutations, and the efficacy of VEN combination targeted therapy of patients were evaluated. RESULTS: Among the 16 cases, 3 were confirmed by reverse transcription multiplex PCR, and 13 were detected through targeted RNA-seq among 528 AL patients, with a detection rate of 2.46%. The averge age of patients was (28.0±8.58) years. Patients exhibited diverse immunophenotypes, including 7 cases of acute myeloid leukemia, 5 of acute T-lymphoblastic leukemia, 1 of acute B-lymphoblastic leukemia, 1 of acute undifferentiated leukemia, and 2 of mixed-phenotype acute leukemia. Among them, 11 had extramedullary disease (EMD), 14 expressed CD7, and 12 expressed CD33. Major co-occurring mutations included PHF6 (6 cases), NOTCH1 (5 cases), and 7 cases with complex karyotypes. Of the 12 patients who received standard induction therapy, 7 did not achieve remission (PR+NR). All 4 patients treated with VEN combination therapy achieved complete remission (CR). Among the 7 induction failure cases, 4 achieved CR upon re-induction with VEN, while the remaining 3 re-induced with standard therapy, did not achieve CR. Thirteen patients received allogeneic hematopoietic stem cell transplantation, including 6 who received maintenance therapy with hypomethylating agents (HMA) alone or in combination with VEN, and seven were followed up. Survival analysis showed that the overall survival was better in the maintenance therapy group (P =0.044). CONCLUSION PICALM∷MLLT10-positive AL involves multiple lineages and demonstrates poor response to conventional chemotherapy. VEN combination therapy shows promising efficacy in both newly diagnosed and R/R patients. Post-transplant maintenance therapy with HMA alone or combined with VEN may extend survival; however, further clinical validation is required.
Humans ; Sulfonamides/therapeutic use* ; Retrospective Studies ; Prognosis ; Bridged Bicyclo Compounds, Heterocyclic/therapeutic use* ; Adult ; Male ; Female ; Leukemia, Myeloid, Acute/genetics* ; Mutation ; Oncogene Proteins, Fusion/genetics* ; Precursor Cell Lymphoblastic Leukemia-Lymphoma/drug therapy*

OBJECTIVE: To explore and evaluate the efficacy and safety of a modified thiotepa-based conditioning regimen combined with autologous hematopoietic stem cell transplantation (ASCT) for the treatment of primary central nervous system lymphoma (PCNSL). METHODS: In a retrospective, single center, single arm study, we collected data of 28 patients with PCNSL who underwent high-dose chemotherapy followed by autologous stem cell transplantation (HDC-ASCT) at our center from March 2021 to December 2024. The clinical characteristics of the patients, the conditioning regimen details, treatment-related toxicities and adverse reactions, post-transplant disease remission status, and survival outcomes were analyzed. RESULTS: A total of 28 patients were included. Among them, 19 patients received ASCT as first-line consolidation therapy in complete response (CR) or partial response (PR) status, and 9 patients with relapsed/refractory disease underwent salvage ASCT. The median time to neutrophil engraftment was 9 days (range: 5-11 days), and the median time to platelet engraftment was 10 days (range: 6-13 days). All patients achieved CR at the initial efficacy evaluation post-ASCT. The main complications during the transplantation period were febrile neutropenia (26 cases) and grade 3 diarrhea (9 cases). No transplantation-related mortality occurred. Post-ASCT, 19 patients received maintenance therapy, which was demonstrated to be safe and effective. Three patients relapse, and one patient died. The median progression-free survival (PFS) and overall survival (OS) of patients were not reached. The estimated 1-year and 2-year cumulative PFS rates were 88.4% and 66.3%, respectively, while the 1-year and 2-year OS rates were both 94.1%. CONCLUSION The modified thiotepa-based conditioning regimen combined with ASCT is safe and effective for the treatment of PCNSL.
Humans ; Thiotepa/therapeutic use* ; Retrospective Studies ; Transplantation, Autologous ; Transplantation Conditioning/methods* ; Central Nervous System Neoplasms/therapy* ; Hematopoietic Stem Cell Transplantation ; Female ; Male ; Middle Aged ; Adult ; Lymphoma/therapy* ; Treatment Outcome ; Aged